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Spectrum Management for Science in the 21st Century (2010)

Chapter:2 The Earth Exploration-Satellite Service

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Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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2
The Earth Exploration-Satellite Service

In 1960 the first weather satellite dramatically opened humanity’s eyes to the beauty and complexity of Earth’s atmosphere. Never before had anyone photographed a hurricane’s movement or cyclonic shape (see Figure 2.1) or observed the global form of atmospheric waves on a planetary scale. After proving the usefulness of orbiting weather observations, NASA and the National Oceanic and Atmospheric Administration (NOAA) began developing ever more sensitive and innovative space-based instruments that help people understand the natural world around them and their impact on it (see Box 2.1). Modern observation systems offer economically and societally important forecasts extending farther into the future than ever before, but these advances depend on protected radio frequency allocations.

With the development of more advanced instrumentation, it quickly became clear that there were great opportunities to observe at wavelengths other than what is usually called light. In fact, visible light is now only a small part of the story. Most current satellite sensors also observe terrestrial emissions at infrared and/or radio wavelengths. These environmental applications have evolved over the past 50 years by combining radio astronomy and geophysical techniques to form the new scientific field known as microwave remote sensing.

Human eyes evolved to detect visible light because Earth’s atmosphere allows solar radiation to pass through an “atmospheric window” at those wavelengths. In the same way, the “eye” of the satellite (the receiver) is designed to view Earth through atmospheric windows at other wavelengths, rather than observing reflected sunlight as human eyes do. Analogous to what infrared goggles (providing heat

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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FIGURE 2.1 Hurricane Camille as it approaches the Gulf States in 1969, as photographed from the NASA Nimbus III satellite. Image courtesy of NASA/Nimbus III Satellite.

FIGURE 2.1 Hurricane Camille as it approaches the Gulf States in 1969, as photographed from the NASA Nimbus III satellite. Image courtesy of NASA/Nimbus III Satellite.

vision) do, most satellite instruments detect the inherent emission of radiation (heat) from the atmosphere and terrestrial surface at wavelengths that reveal details invisible to human eyes. When the atmosphere itself is of interest, opaque wavelengths that do not pass through the atmosphere but are absorbed by it offer more information. Each window and opaque band responds differently to the various properties of the terrestrial surface and atmosphere, allowing those properties to be studied by a simultaneous analysis at multiple frequencies. The accuracy of these studies increases with the number of observed frequencies. The unique ability of passive microwave sensors to “see through” most clouds makes those sensors essential, particularly where clouds are persistent. The sensors are passive in that they do not transmit signals but instead only receive the natural background emission. Scientists can thus extract information from the radio spectrum on environmental properties as varied as atmospheric temperature and humidity, precipitation rate, soil moisture, ocean salinity, and ocean waves (and therefore surface winds and

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

BOX 2.1

The Origin of Earth Remote Sensing

Earth remote sensing techniques have developed over many years, evolving out of astronomy and accelerating as satellite technology became more robust.

  • Before 1932: Use of optical astronomy (initial passive spectral observations of stellar and planetary surface and atmospheric temperatures and compositions, demonstrating basic methods).

  • 1932: The first radio astronomy observations by pioneer radio astronomer Karl Jansky, revealing cosmic radiation.

  • 1940-1945: Wartime studies of centimeter- and millimeter-wave atmospheric absorption spectra and passive radiation; the development of sensitive radiometry.

  • 1968: Launch of the first passive microwave radiometer on the Soviet Cosmos-243 satellite—it observed sea surface temperature, land temperature, snow/ice cover, water vapor, and liquid water using four unscanned window channels at 3.5-37 GHz (unfortunately short-lived, operating only for weeks).

  • 1972, 1975: The first long-lived satellites to image window-channel parameters (humidity over ocean, sea ice, ocean roughness and wind, snow cover, precipitation, land temperature, etc.) and atmospheric temperature profiles: Nimbus-E Microwave Spectrometer (NEMS; two window channels and three opaque channels) and Electrical Scanning Microwave Radiometer (ESMR) imaging at 19.36 GHz launched on the NASA Nimbus-5 satellite in 1972, and the Scanning Microwave Radiometer (a wide-swath imaging version of NEMS) and the dual-polarized ESMR imaging at 37 GHz launched on Nimbus-6 in 1975.

  • 1978: The first operational weather satellites to incorporate imaging passive microwave spectrometers for temperature sounding (microwave sounding unit with four opaque-band channels at 50-58 GHz on TIROS-N and NOAA-6 and -7).

  • 1987: The first operational satellites to monitor surface parameters and atmospheric water (Special Sensor Microwave/Imager with seven window channels at four frequencies, 19.35-85.5 GHz, first launched by the Defense Meteorological Satellite Program).

  • Post-1987: Launch of continually improved research (NASA) and operational (National Oceanic and Atmospheric Administration and Department of Defense) passive microwave instrument types.

ocean internal waves). The full global coverage provided by satellites enables scientists to monitor Earth’s environment far more accurately and completely than had been possible using traditional means such as weather stations and balloon sounders. Satellite data have also greatly improved the accuracy of weather forecasts and enabled sensitive, large-scale climate studies revealing, for example, the effects of ozone-modifying trace gases. Figure 2.2 presents a typical image of the abundance of water vapor over the oceans as observed by combining observations in multiple frequencies obtained by the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) imaging passive microwave spectrometer.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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FIGURE 2.2 Advanced Microwave Scanning Radiometer-Earth (AMSR-E) data showing tropospheric water vapor abundance over Earth’s oceans, denoted by the colors in the image. Land is denoted by shades of gray, its shade depending on the elevation; sea ice is denoted by white. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

FIGURE 2.2 Advanced Microwave Scanning Radiometer-Earth (AMSR-E) data showing tropospheric water vapor abundance over Earth’s oceans, denoted by the colors in the image. Land is denoted by shades of gray, its shade depending on the elevation; sea ice is denoted by white. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

Today, the United States operates a suite of more than 30 satellites that measure Earth’s planetary environment and collectively represent many billions of dollars invested by U.S. taxpayers.

The significance of the passive radio services is suggested not only by the substantial government investment in their development and operation, but also by their impact on the national economy. The environmental products facilitated by the passive services are critical for day-to-day, long-term, and severe weather forecasting and also for the Department of Defense (DOD) and for the energy, agriculture, and transportation industries.1 The U.S. investment in passive Earth observatories provides the nation with a high degree of economic leverage over environmental events.

1

The 2006 report Economic Statistics for NOAA states that “weather and climate sensitive industries, both directly and indirectly, account for about one-third of the Nation’s GDP in sectors ranging from finance, insurance, and real estate to services, retail and wholesale trade and manufacturing. Industries directly impacted by weather such as agriculture, construction, energy distribution, and outdoor recreation account for nearly 10 percent of GDP.” National Oceanic and Atmospheric Administration, Economic Statistics for NOAA, Washington, D.C., 2006.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

On a larger scale, Earth’s climate is deemed so important to humanity that the 2007 Nobel Peace Prize was awarded to the Intergovernmental Panel on Climate Change (IPCC) and Albert Gore, Jr., “for their efforts to build up and disseminate greater knowledge about man-made climate change, and to lay the foundations for the measures that are needed to counteract such change.”2 The prize was based on the laureates’ assessment that large-scale climate change would irrevocably alter living conditions in many places in the world and thus lead to wide-spread civil unrest. Consistent with this assessment of the importance of climate to humanity are estimates that the potential consequences of global change in its various manifestations (sea ice loss, global warming and drought, coral bleaching, tropical ecosystem collapse, and other interrelated environmental problems) would be associated with unprecedented societal costs to the United States and the world.3 These staggering costs demand that the most precise information on global environmental processes be made available to decision makers grappling with questions of environmental policy. The precision of this information and the overall understanding of climate change are driven by both observational science and improved understanding and models of the environment, which in turn are dependent on the availability of spectrum for use in environmental observation. At stake are potential measures including limits on emissions of greenhouse gases such as carbon dioxide and methane, limits on aerosols and chlorofluorocarbons, restrictions on deforestation and freshwater usage, and stiff requirements for agricultural and manufacturing practices and the transportation industry.

It is also useful to note the educational value of government programs that apply radio science to environmental problems. These programs are largely conducted either through or in collaboration with universities and thereby train many graduate students at the cutting edge of both radio- and microwave-frequency technology and Earth science, thus contributing to economic sectors critical to U.S. global competitiveness and the defense of the nation.

The importance of environmental radio services has increased in parallel with the use of public and commercial wireless and other electronics technologies (see discussion in Chapter 4). Collectively there has been a substantial increase in the number of human-made radio signals that can interfere with and corrupt needed scientific and operational passive observations of the environment.4 The commoditization of wireless and other electronics technology has significantly increased the pressure on the passive uses of the spectrum in terms of allocations and disruptive

2

Available at http://nobelprize.org/nobel_prizes/peace/laureates/2007/; accessed August 26, 2008.

3

Intergovernmental Panel on Climate Change, Working Group II Report, Impacts, Adaptation and Vulnerability, Geneva, Switzerland, 2007.

4

Scientific observations are those conducted for research purposes. Operational observations are conducted in consistent, repeated ways for use in products such as weather forecasts.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

interference. As quickly as techniques have been developed to mitigate human-made interference, they are eroded by other expanding active uses of the spectrum. Moreover, as the spectral efficiency of wireless technology improves, the interference that it produces increasingly resembles random noise, which is more difficult to identify and mitigate. These difficulties are compounded by the increased use of spectrum licenses that permit unlimited numbers of approved devices to be used, with decreasing means for enforcement or further mitigation. Section 2.5 discusses these difficulties in a variety of circumstances.

Most active services can use coding techniques, better antenna systems, and higher-power transmitters to survive even high levels of interference, but these techniques are not applicable to passive services. There is a fundamental asymmetry between the spectral requirements of active communications services and passive environmental uses. Advances in wireless technology are rapidly increasing the abilities of competing communications services to share radio spectrum through agile time-frequency multiplexing, whereas the measurement precisions of the passive services are intrinsically limited by the strength of natural emissions, the reception bandwidth, and the observing time.

The competition for radio spectrum also has global implications, as the U.S. environment is affected by environmental conditions in other nations and vice versa. Both U.S. and foreign environmental satellites fly over almost the entire globe and continuously observe within the same spectral bands everywhere; thus critical environmental radio bands need to be uncontaminated everywhere. The data from these diverse, Earth-orbiting, multinational assets are increasingly being shared in the global public interest, which parallels the separate national interests, and can be obtained by no other means. Furthermore, the national character of environmental services and the multidecadal times required for their development and use in space make them much less nimble than the private sector that can develop new radiating products in a period of months. It has therefore become clear that a new look at spectrum policies and regulations is necessary in order to protect the critical passive environmental observations by Earth observation satellites and to permit the passive and active services to coexist productively. This chapter discusses the reasons behind the need for new regulations, which are further elaborated on in Chapter 4.

2.1
SPECIFIC APPLICATION AREAS OF PASSIVE MICROWAVE REMOTE SENSING

Earth remote sensing is critically important to the United States and to the advancement of human scientific knowledge about Earth and the environmental processes that support life and commerce. Microwave remote sensing, or the Earth Exploration-Satellite Service (EESS) in regulatory parlance, provides direct eco-

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

nomic benefits to the nation by obtaining information that has economic value to both the public and private sectors. In addition, the collection of these data is a highly technical enterprise that strengthens the U.S. industrial, defense, telecommunications, and environmental sectors. The United States operates in a competitive, information-dominated economy that is dependent not only on having access to the passive spectrum for obtaining data for commercial, governmental, and public purposes, but also on having skilled engineers who are trained in the most sophisticated microwave engineering techniques.

Passive and active remote sensing act in tandem to collect environmental information and ultimately to provide the benefits to society referred to above. Much of the data that lead to these benefits, however, is only available from passive microwave sensors, and these sensors have unique needs that must be met to enable the measurements that they make. For example, passive microwave remote sensing is indispensable for better numerical weather forecasting, large-scale monitoring of subsurface soil moisture, and so on, and improvements in weather forecasting are important economically and strategically.

This section presents a sampling of applications in which passive access to the microwave spectrum is essential for the country. The discussion is organized in the following broad topics: weather forecasting and monitoring, severe weather and disasters, climate and global change, resource management, aviation, defense and pubic safety, international partnerships, and education and technology. The subsection on international partnerships includes discussion of a recent international effort, initiated by the United States and the Group of Eight (G8), to ensure that the nations of the world engaged in space remote sensing collaborate in exchanging data to benefit their societies.

Weather Forecasting and Monitoring

Satelliteborne passive microwave sensors are a critical part of the global weather monitoring system. Passive microwave sensors are particularly critical for measuring temperature, humidity, and precipitation profiles in the cloud-affected troposphere below approximately 10 km, where most economically important weather occurs, and in measuring sea surface winds and temperatures and soil moisture. Part of the reason for this importance is that weather radars measure only the reflectivity of water and ice droplets in the atmosphere but are insensitive to these other parameters. Even so, the extraction of useful information from radar reflectivity measurements relies greatly on knowledge of the droplets’ size distribution, which requires complex and costly multiband radar measurements to measure directly. Passive microwave radiometers, however, directly measure the total quantity of liquid water as well as water vapor and other variables. Such radiometers can herald impending weather events by measuring the presence of water vapor in

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

advance of cloud formation and then detect the formation of liquid water droplets well in advance of the detection by rain radars. Moreover, when used in conjunction with weather radars, passive radiometers provide a high degree of precision in the measurement of the path- or area-averaged quantities being observed that serve to calibrate the radar’s signal. In this manner the radiometer is able to facilitate the radar’s capability to provide high resolution. Radars are thus useful in conjunction with radiometers but not as a substitute for them, as exemplified by the recent Tropical Rainfall Measuring Mission (TRMM) and the CloudSat and future Aquarius and Soil Moisture Active Passive (SMAP) missions.

Modern weather forecasts are based primarily on numerical weather prediction (NWP) models run on massively parallel computers. These models use direct data assimilation (DDA), a powerful technique developed during the past two decades that incorporates all available data from satellites, balloons, radars, and surface stations to steer NWP models. Major worldwide centers developing and operating these models are located in the United States, Europe, Canada, China, Japan, and Australia. Their algorithms, from the beginning, have relied heavily on passive microwave measurements of relevant environmental variables, and they will continue to do so as spatial and temporal resolutions improve. Passive microwave data in the opaque temperature-sensitive bands above 50 GHz have been particularly helpful because of their insensitivity to most clouds; these observations probably constitute the single most valuable data source currently enabling 1-week weather forecasts. The demand for improved space and time resolution has been relentless since the inception of NWP modeling in the 1970s and is expected to continue for the foreseeable future, particularly as wireless devices enabled by the Global Positioning System increase the demands for ever more site-specific, personalized information on weather.

In recent decades, the accuracy and usefulness of weather forecasts have increased tremendously because of progress in both NWP systems and satellite-based remote sensing systems. Figure 2.3 illustrates this progress in terms of the number of days for which forecasts of a given quality are obtained. For the highest-quality Southern Hemispheric forecasts, satellite data increase the forecast from 12 hours to 2 days—a factor of four—and for an anomaly correlation of 0.6 the forecast doubles from 3.25 to 6.5 days. The anomaly correlation is a common measure of forecast accuracy, with values above 0.6 generally considered to be significant.

Much of the improvement in forecasting shown in Figure 2.3 is due to the direct use of passive microwave data on their own, and to the integration of microwave and infrared data that combine the best features of both sensor types. Surface wind data over the ocean derived from spaceborne microwave measurements have also been helpful. These improvements are particularly striking in the Southern Hemisphere where data from surface stations and balloon soundings are sparse, but they also extend forecasts in the Northern Hemisphere by roughly

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.3 Anomaly correlation for days 0 to 7 for 500 hectopascal geopotential height in the zonal band 20°-80° South for August/September. The red and blue arrows indicate that the use of satellite data in the forecast model has doubled the length of a useful forecast (i.e., a forecast with anomaly correlation = 0.6). Image courtesy of NOAA.

FIGURE 2.3 Anomaly correlation for days 0 to 7 for 500 hectopascal geopotential height in the zonal band 20°-80° South for August/September. The red and blue arrows indicate that the use of satellite data in the forecast model has doubled the length of a useful forecast (i.e., a forecast with anomaly correlation = 0.6). Image courtesy of NOAA.

25 percent. Passive microwave sensors are also useful for tracing the movement of water through normal weather cycles. For instance, surface soil moisture, snow cover, and snow-water-equivalent drive energy exchange with the atmosphere and therefore affect weather forecasts. The major impact of these surface variables on forecast accuracy is just beginning to be seen (Figure 2.4).

Brief discussions of a few specific weather-monitoring topics follow.

Soil Moisture

Accurate knowledge of the parameters of soil moisture (SM) has been shown to improve forecasts of local storms and seasonal climate anomalies. In Figure 2.5, panel (C) shows the observed difference in rainfall between two extreme years, the flood year of 1993 minus the drought year of 1988, over the middle of the United States. Current atmospheric models tend to use sea surface temperatures (SSTs) as their primary boundary condition because so much of Earth’s surface is

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.4 A depiction of the impact of observations of soil moisture (left) on 12-hour rainfall forecasts that use Weather Research and Forecasting models (for June 12, 2002). Panels at right: Forecasts with and without the Land Information System (LIS) providing improved soil moisture initial and boundary conditions. Image courtesy of NASA.

FIGURE 2.4 A depiction of the impact of observations of soil moisture (left) on 12-hour rainfall forecasts that use Weather Research and Forecasting models (for June 12, 2002). Panels at right: Forecasts with and without the Land Information System (LIS) providing improved soil moisture initial and boundary conditions. Image courtesy of NASA.

FIGURE 2.5 The value of soil moisture data to climate forecasts. Predictability of seasonal climate is dependent on boundary conditions such as sea surface temperatures (SST) and soil moisture—soil moisture being particularly important over continental interiors. In the results of a simulation driven only by SST (panel A), the climate anomaly in panel C (observed difference in rainfall between the flood year of 1993 minus the drought year of 1988) is not reproduced. Results of the simulation driven by SST and soil moisture (panel B), however, accurately predict this seasonal anomaly. SOURCE: D. Entekhabi, G.R. Asrar, A.K. Betts, K.J. Beven, R.L. Bras, C.J. Duffy, T. Dunne, R.D. Koster, D.P. Lettenmaier, D.B. McLaughlin, and W.J. Shuttleworth, “An Agenda for Land Surface Hydrology Research and a Call for the Second International Hydrological Decade,” Bulletin of the American Meteorological Society, 80(10): 2043-2058 (1999).

FIGURE 2.5 The value of soil moisture data to climate forecasts. Predictability of seasonal climate is dependent on boundary conditions such as sea surface temperatures (SST) and soil moisture—soil moisture being particularly important over continental interiors. In the results of a simulation driven only by SST (panel A), the climate anomaly in panel C (observed difference in rainfall between the flood year of 1993 minus the drought year of 1988) is not reproduced. Results of the simulation driven by SST and soil moisture (panel B), however, accurately predict this seasonal anomaly. SOURCE: D. Entekhabi, G.R. Asrar, A.K. Betts, K.J. Beven, R.L. Bras, C.J. Duffy, T. Dunne, R.D. Koster, D.P. Lettenmaier, D.B. McLaughlin, and W.J. Shuttleworth, “An Agenda for Land Surface Hydrology Research and a Call for the Second International Hydrological Decade,” Bulletin of the American Meteorological Society, 80(10): 2043-2058 (1999).

ocean. However, models just using SSTs do not do a good job of capturing seasonal climate anomalies in the middle of large continents. As seen from the results in Figure 2.5(A), the climate anomaly is not reproduced. However, if SM data such as those derivable from space-based 1.4 GHz passive microwave measurements are incorporated, atmospheric models can accurately predict the seasonal anomalies in

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.6 Soil moisture data improve numerical weather prediction over continents by accurately initializing land surface states. In this example, 24-hour prior forecasts of a high-resolution atmospheric model of rainfall are shown without (A) and with (B) soil moisture input data. The observed data are shown in panel C. Provided by the National Snow and Ice Data Center.

FIGURE 2.6 Soil moisture data improve numerical weather prediction over continents by accurately initializing land surface states. In this example, 24-hour prior forecasts of a high-resolution atmospheric model of rainfall are shown without (A) and with (B) soil moisture input data. The observed data are shown in panel C. Provided by the National Snow and Ice Data Center.

extreme weather, as seen in Figure 2.5(B). In the second example of the importance of SM data (Figure 2.6), NWP can be improved over the continental United States by more accurately initializing the land surface state with soil moisture data.

Soil moisture is also a key parameter in forecasting relating to agriculture, drought, and flooding and for predicting vegetative stress and establishing related government policies. Passive microwave radiometers operating at frequencies of 10 GHz and lower are sensitive to variations in soil density, type, and moisture content and are needed for SM measurements. Radiometry in the 1-2 GHz range is arguably the best means for measuring subsurface soil moisture on a national or global basis.

Sea Surface Winds

Global sea surface wind data are critical for high-quality NWP forecasts, for developing data pertinent to tropical cyclone warnings, aircraft and ship operations, ship routing, and other civil and military operations. Sea surface wind data constitute one of the most important parameters in operational meteorological remote sensing. Space-based remote sensing of sea surface wind vector (SSWV) depends on precision measurements of polarimetric microwave emissions from the ocean surface. These measurements have been shown to improve the forecasting capability of NWP models significantly, thus contributing to maritime and coastal safety and commerce. The accuracy of the wind vector products obtained from the Naval Research Laboratory’s (NRL’s) WindSat retrievals to date has reached or exceeded the accuracy of the wind vector products available from active scatterometer systems such as QuikScat at moderate to high wind speeds. Also, the ability of microwave radiometers to measure simultaneously atmospheric and sea temperature properties motivates attempts to improve further the accuracy

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

of the radiometer products. In addition, the National Polar-orbiting Operational Environmental Satellite System (NPOESS, which will be the next generation of U.S. weather satellites) will include a microwave radiometer (called the Microwave Imager/Sounder, or MIS) that will likely have many capabilities similar to those of WindSat, including the capability to measure multiple parameters.

Sea Surface Temperature

Global, all-weather sea surface temperature data are critical for NWP and for climate research. SST measurements are important for understanding heat exchange and the coupling between ocean and atmosphere, and SST data are required by operational ocean analyses in order to properly constrain upper-ocean circulation and thermal structure. SST measurements in clear air can be obtained using electrooptical (traditional) instruments; however, clouds prevent these measurements, so passive microwave measurements within the approximately 4-11 GHz region are critical for obtaining coverage in areas that are seasonally cloud-covered. For example, areas in the U.S. Exclusive Economic Zone off the coast of Washington and Oregon are not imaged with traditional satellite SST sensors for weeks at a time owing to persistent stratus cloud cover, necessitating an all-weather solution. The standard SST measurement uncertainty for space-based SST measurements is 0.5 K at 50 km passive microwave (all-weather) capability.

Water Vapor Profiles

Global water vapor profiles are essential to the numerical weather prediction of rainfall and drought and help constrain such predictions in general. As in the case of temperature profile measurements, combined microwave and infrared spectral data can yield what is nearly all-weather global performance, even in most cloudy conditions. Two different types of microwave observations are used: those in transparent bands within which the water vapor absorption stands out (1) against the colder ocean background (ocean partially reflects the extremely cold cosmic background radiation), or (2) against that of cold low-emissivity land. No profile information is usually retrieved—only an estimate of the column-integrated abundance. The frequencies most often used for this purpose include 18.7, 22, 23.8, 31.4, 37, and 89 GHz. To improve retrieval accuracies, these channels are often dual-polarized (horizontal and vertical) and scanned at a near-constant angle of incidence (e.g., TRMM Microwave Imager [TMI], Special Sensor Microwave/Imager [SSM/I], Special Sensor Microwave/Imager Sounder [SSM/IS], WindSat, and AMSR-E). In addition, the opaque water vapor resonance near 183 GHz is often used in combination with some of the lower frequencies; these frequencies generally include 89, 150, 164-168, and 176-191 GHz, but they must

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

be used in combination with temperature profile information to yield the most accurate results (e.g., Advanced Microwave Sounding Unit [AMSU], SSM/IS). Instruments retrieving water vapor profiles are generally used to retrieve other parameters simultaneously, such as cloud water content, precipitation rate, and ice and snow cover information.

Severe Weather and Disasters

One impact of world population growth over the past 50 years is an increased vulnerability to natural disasters. Weather-related disasters include tornadoes, hurricanes, hail, blizzards, floods, mudslides, heat waves, forest fires, and drought. Some disasters have immediate impacts and others long-term effects; for example, rising sea levels could have major impacts on coastal areas, and severe declines in snow cover in the western United States could yield less spring snowmelt and water for summer agriculture and urban needs.

Extreme weather events and other natural disasters can be costly, not only in terms of the immediate loss of life and property that they can cause, but also because of the efforts needed to anticipate and respond to such disasters and in the long-term economic and societal consequences of such events. NOAA estimates that the cost in the United States of damages from tornadoes, hurricanes, and floods alone averages around $11.4 billion annually.5 Even one major hurricane, however, can significantly exceed these costs. Although the full cost of Hurricane Katrina, which made landfall near New Orleans on August 28, 2005, will not be known for many years, insured losses alone are estimated at $40.0 billion.6 EESS observations enable significant economic and societal savings owing to their ability to predict such costly natural events and to allow people to prepare for them.

To provide a perspective on the types of costs referred to above, Figure 2.7 highlights major U.S. weather-related disasters over the past 25 years. As growing population density along the coasts of the nation has increased the cost of coastal disasters, the mitigating effect of improved weather forecasting has been reducing those costs by increasing the warning times and the accuracy of the forecasts, leading to increased life-saving evacuation and cost-reducing physical preparations while precluding such steps where they are not needed. According to a report from the National Research Council’s Space Studies Board, the error in 3-day-forecast landfall positions of hurricanes was reduced from 210 miles in 1985 to about 110 miles in 2004, arguably halving the preparation area while increasing the population response and preparation effectiveness. Further, the accuracy

5

National Oceanic and Atmospheric Administration, Office of the Chief Economist, Economic Statistics for NOAA, 5th Ed., April 2006, p. 10.

6

Ibid, p. 18.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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FIGURE 2.7 Billion-dollar weather-related disasters in the United States from 1980 to 2005. NOTE: Earth Exploration-Satellite System measurements are now an important data source for improving the accuracy of forecasts. Image courtesy of NOAA.

FIGURE 2.7 Billion-dollar weather-related disasters in the United States from 1980 to 2005. NOTE: Earth Exploration-Satellite System measurements are now an important data source for improving the accuracy of forecasts. Image courtesy of NOAA.

of today’s 4-day forecasts is about the same as the 2-day forecasts of 20 years ago.7 EESS measurements have played a major role in improving these forecasts. The insurance industry is also increasingly interested in using passive microwave data to arbitrate claims involving hurricane-related flooding or winds that can often only be distinguished by passive microwave observations.

Figure 2.8 illustrates the value of all-weather microwave SST measurements for hurricane forecasting. Figure 2.8(A) shows how the NASA TMI viewed the cold wake of Hurricane Bonnie through cloud cover as the hurricane moved up the eastern coast of the United States on August 24-26, 1998. Figure 2.8(B) shows the same scene as viewed in infrared by the Advanced Very High Resolution Radiometer (AVHRR) a few days later as Hurricane Danielle moved up the coast on August 27.

7

National Research Council, Earth Science and Applications from Space: Urgent Needs and Opportunities to Serve the Nation, Washington, D.C.: The National Academies Press, 2005, p. 9.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.8 (A) Microwave imagery at 10 GHz supplied by the NASA Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) showing a cold wake (blue region near the white dots) produced by Hurricane Bonnie on August 24-26, 1998. (B) The cold wake was not seen by the visible and infrared Advanced Very High Resolution Radiometer (AVHRR) imager because of the areas of persistent rain and cloud cover (white patches) over the 3-day period. When Danielle crossed Bonnie’s cold wake on August 29, Danielle’s intensity dropped. Although the cloud cover prevented AVHRR from observing this sequence, TMI was able to measure characteristics of the sea surface. Hurricane Bonnie’s track is shown by the white dots, and Hurricane Danielle’s track is shown by the gray dots. Image courtesy of NASA TRMM Microwave Imager.

FIGURE 2.8 (A) Microwave imagery at 10 GHz supplied by the NASA Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) showing a cold wake (blue region near the white dots) produced by Hurricane Bonnie on August 24-26, 1998. (B) The cold wake was not seen by the visible and infrared Advanced Very High Resolution Radiometer (AVHRR) imager because of the areas of persistent rain and cloud cover (white patches) over the 3-day period. When Danielle crossed Bonnie’s cold wake on August 29, Danielle’s intensity dropped. Although the cloud cover prevented AVHRR from observing this sequence, TMI was able to measure characteristics of the sea surface. Hurricane Bonnie’s track is shown by the white dots, and Hurricane Danielle’s track is shown by the gray dots. Image courtesy of NASA TRMM Microwave Imager.

The cold wake was invisible to AVHRR because of persistent clouds and rain. A retrospective analysis showed that the magnitude of the cold wake left by Hurricane Bonnie was critical to being able to predict the weakening of the second hurricane, Danielle, a few days later and could not have been done without the microwave measurements of sea surface temperature by TMI.8 The strong dependence of hurricane growth on local sea surface temperatures makes such measurements through hurricane cloud shields important, particularly since the overturning of the water by the hurricane itself can alter those temperatures rapidly.

An example of the ability of satellite-based passive microwave sensors to observe the high wind speeds of a hurricane is provided in Figure 2.9, an image of the wind speed of Hurricane Katrina as it made landfall near New Orleans on August 28, 2005. In addition, an airborne system, the Stepped Frequency Microwave Radiometer (SFMR), is currently included in NOAA’s hurricane-observing research aircraft. Measurements from this system contributed to 23 hurricane advisories in 2005, including the landfall intensity advisories of Hurricanes Katrina

8

F.J. Wentz, D.S. Gentemann, and D. Chelton, “Satellite Measurements of Sea Surface Temperature Through Clouds,” Science, 288(5467): 847-850 (May 5, 2000).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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FIGURE 2.9 (Left) Image of the wind speed of Hurricane Katrina (in knots), observed by passive microwave radiometers on WindSat, a Naval Research Laboratory satellite, as Katrina makes landfall near New Orleans on August 28, 2005. (Right) Output from a model that combines data from WindSat and other remote sensing instruments. The model provides information on the hurricane’s wind speed. The values over land are extrapolations. Courtesy of the U.S. Naval Research Laboratory.

FIGURE 2.9 (Left) Image of the wind speed of Hurricane Katrina (in knots), observed by passive microwave radiometers on WindSat, a Naval Research Laboratory satellite, as Katrina makes landfall near New Orleans on August 28, 2005. (Right) Output from a model that combines data from WindSat and other remote sensing instruments. The model provides information on the hurricane’s wind speed. The values over land are extrapolations. Courtesy of the U.S. Naval Research Laboratory.

and Rita. The passive microwave technique is so effective that the U.S. Congress mandated SFMR instruments for the fleet of U.S. Air Force WC-130J Hercules operational weather-monitoring aircraft.

Key impact areas for passive microwave observations of natural disasters include hurricane observations and the forecasting and monitoring of severe regional weather and both drought and flood activity. The general usefulness of passive microwave observations in observing global meteorology also aids in the monitoring of other natural disasters and helps with the associated public-safety requirements.

Climate and Global Change

Perhaps the most significant global issue of the early 21st century is the possibility of global environmental change in response to human activity. The potential consequences of global change in its various manifestations (sea-level rise, sea ice loss, global warming and drought, coral bleaching, tropical ecosystem collapse, and other interrelated environmental problems) can be associated with societal costs from a reduction of 1 to 5.5 percent in global gross domestic product by

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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2050, depending on the carbon dioxide (CO2) stabilization level.9 Such costs demand that the most precise and relevant information on global environmental processes be available to decision makers. In many cases the measurement of key climate-related geophysical variables on a global scale is required, and space-based passive microwave radiometry is often the only reasonable means to collect these measurements.

Atmospheric Temperature Profile and Clouds

Among the most important of human influences on climate is the production of greenhouse gases, including CO2, methane (CH4), and various chlorofluorocarbon (CFC; hydrochlorofluorocarbon [HCFC]) and hydrofluorocarbon (HFC) compounds. Of these, CO2 and CH4 rapidly become well mixed in the lower atmosphere and affect Earth’s radiation budget by trapping infrared radiation that would otherwise be expelled to space. Although CO2 and CH4 are themselves potent greenhouse gases and the primary cause of observed global warming, their indirect influence on atmospheric water vapor—a more potent and less predictable greenhouse gas—is perhaps even more important. Tropospheric water vapor provides a feedback mechanism through which increased global warming adds to the capacity of the atmosphere to contain water vapor while simultaneously elevating evaporation rates. The monitoring of water vapor and cloud water content and their effects on global radiation fluxes is thus critical to understanding the causes of climate change and predicting future climates. Currently, cloud coverage and type are the most significant sources of uncertainty in global climate modeling. Radar observations are strongly dependent on unknown drop size distributions, and optical sensors do not penetrate clouds well; thus microwave radiometers on all types of platforms (satellite, aircraft, ships, and ground sites) are essential to making water vapor measurements and thus to the science of climate change.

The ability of passive microwave sensors to observe through clouds, in combination with frequent global microwave measurements of average mid-tropospheric and stratospheric temperatures near 54 GHz, has provided a unique record of global atmospheric change over the past two decades that validates other measures. The observed long-term warming of the mid-troposphere is roughly 0.2 ± 0.04 K per decade.10

9

Intergovernmental Panel on Climate Change, Climate Change 2007: Synthesis Report, Geneva, Switzerland, 2007.

10

C.A. Mears and F.J. Wentz, “The Effects of Diurnal Correction on Satellite-Derived Lower Tropospheric Temperature,” Science, 309(5740): 1548-1551 (September 2, 2005).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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Cloud Ice Water Path

Cloud ice water path (IWP) is the vertically summed mass of cloudborne ice particles per unit of area. As ice clouds can reflect a significant amount of sunlight, their impact on global radiative energy fluxes and hence climate change is considerable. Future global IWP measurements from space using passive microwave techniques at frequencies from 89 GHz up to approximately 1 THz could characterize the coupling of the global hydrologic and energy cycles through upper tropospheric cloud processes.11 Such measurements would enable the development and testing of new self-consistent parameterizations of ice cloud processes and cloud systems, which could in turn guide improvements in ice cloud representation in global Earth system models. These improvements would significantly advance the understanding of the hydrological cycle and climate predictability.

Ozone Depletion and Trace Gases

Climate is also strongly affected by trace gases in the upper troposphere, stratosphere, and mesosphere; some of these trace gases also facilitate the destruction of stratospheric ozone.12 A diminished ozone layer allows harmful ultraviolet-B (UV-B) radiation from the Sun to reach Earth’s surface, where it significantly enhances the probability of the occurrence of basal and squamous cell skin cancers and cataracts. The underlying chemical reactions that cause ozone depletion require chlorine and bromine to be present in sufficient quantities in the stratosphere.13 This revelation was central to the framing of the 1987 Montreal Protocol on Substances That Deplete the Ozone Layer, which explicitly identified ozone-depleting substances that were subsequently banned in a series of international treaties—in 1989, 1990, 1991, 1992, 1993, 1995, 1997, and 1999. The U.S. Environmental Protection Agency estimated in 1999 that the provisions of the Montreal Protocol, which sought to arrest runaway ozone depletion, would save 6.3 million lives from reduced levels of skin cancer, prevent 299 million cases of nonfatal skin cancers, and prevent the development of 27.5 million cases of cataracts in the United States alone between 1990 and

11

K.F. Evans, J.R. Wang, P. Racette, G. Heymsfield, and L. Li, “Ice Cloud Retrievals and Analysis with Data from the Compact Scanning Submillimeter Imaging Radiometer and the Cloud Radar System During CRYSTAL-FACE,” Journal of Applied Meteorology, 44: 839-859 (2005).

12

J.R. Holton, P.H. Haynes, M.E. McIntyre, A.R. Douglass, R.B. Rood, and L. Pfister, “Stratospheric-Tropospheric Exchange,” Rev. Geophys., 33: 403-439 (1995); P.M. de F. Forster and K.P. Shine, “Radiative Forcing and Temperature Trends from Stratospheric Ozone Changes,” Journal of Geophysical Research, 102: 10841-10857 (1997).

13

M.J. Molina and F.S. Rowland, “Stratospheric Sink for Chlorofluoromethanes: Chlorine AtomCatalysed Destruction of Ozone,” Nature, 249: 810-812 (June 28, 1974).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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2165.14 Passive microwave observations provide a valuable means for monitoring the distribution and concentration of ozone and other trace gases.

Ocean Altimetry and Sea Surface Variables

Microwave remote sensing plays a crucial role in monitoring the global ocean, with radiometry, altimetry, scatterometry, and synthetic aperture radar observations all having important climate applications. Ocean altimetry maps the topography of the ocean surface, from which ocean currents and atmospheric surface pressure can be derived. Maps of the currents are routinely used to help route commercial and military naval vessels and, in the commercial fishing industry, to help locate large fish populations. Sea-level anomalies in the tropical Pacific, derived from altimeters, are perhaps the most sensitive precursor indicators of El Niño and La Niña events up to 1 year in advance.15 The recent series of satellite altimeter missions—Topography Experiment (TOPEX)/Poseidon, Jason-1, and the Ocean Surface Topography Mission (or Jason-2)—has been able to monitor the rise in global sea level, thus providing an important means of verifying the expansion of the oceans in response to climate change. These missions have also contributed significantly to the ability to forecast the occurrence of El Niño events as much as 1 year in advance.16 For each radar observation, coincident passive microwave radiometer measurements are needed in order to correct the radar altimeters’ determination of sea level for the variations in integrated atmospheric refractivity due to tropospheric water vapor.17 These refractivity radiometers operate near 19, 23 and 34 GHz and require measurements of brightness temperature that are free of radio frequency interference (RFI).18

More generally, the Global Climate Observing System (GCOS) implementation plan19 includes sustained observations of sea surface temperature, ocean wind

14

U.S. Environmental Protection Agency, The Benefits and Costs of the Clean Air Act, 1990 to 2010, EPA-410-R-99-001, prepared for the U.S. Congress by EPA Office of Air and Radiation/Office of Policy, November 1999, p. 64.

15

D. Chen, “Applying Satellite Remote Sensing to Predicting 1999-2000 La Nina,” Remote Sensing of Environment, 77(3): 275 (2001).

16

D. Chen, “Application of Altimeter Observation to El Niño Prediction,” International Journal of Remote Sensing, 22(13): 2621-2626 (2001).

17

S.J. Keihm, M.A. Janssen, and C.S. Ruf, “TOPEX/POSEIDON Microwave Radiometer (TMR): III. Wet Tropospheric Range Correction and Pre-Launch Error Budget,” IEEE Transactions on Geoscience and Remote Sensing, 33(1): 147-161 (1995).

18

Ibid.

19

Global Climate Observing System Secretariat, “Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update),” Draft v1.0, November 13, 2009, Geneva, Switzerland. Available at http://www.wmo.int/pages/prog/gcos/documents/GCOSIP-10_DRAFTv1.0_131109.pdf, accessed 12/30/09.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

vector, and total columnar integrated atmospheric water vapor in the list of essential climate variables (ECVs) for satellite-based climate studies. All of these climate variables can be sensed simultaneously through the use of polarimetric, multiple-frequency microwave radiometry, as practiced using NRL’s WindSat sensor.

Rainfall and Snowfall Rates

Rainfall and snowfall rates and total amounts of precipitation are highly valuable measurements that can be determined by on-orbit and ground-based microwave and millimeter wave radiometers.20,21 Knowledge of these quantities is important to the prediction of floods, of crop health and yield, and of catchment replenishment for hydroelectric, irrigation, and domestic uses, and for other societal benefits and impacts.

Snow

Information about snow and frozen ground is critical for understanding fundamental hydrological processes and for detecting environmental change, assessing its impact, and validating environmental models. Snow cover and snow water equivalent (SWE) data are derived using microwave imagery that is sensitive to emission from different snow depths and structure, in combination with visible imagery. In 2004, a global monthly SWE climatology data set that blended Scanning Multi-Channel Microwave Radiometer (SMMR) and SSM/I passive-microwave-derived SWE with NOAA optical sensor snow maps was completed. The data set serves as an important tool for climate research (see Figure 2.10). Snow cover and SWE are also important parameters for analyzing and improving numerical models of the atmosphere, including surface and atmosphere exchange processes, diagnostics, and forecasting.

20

F. Marzano, P. Ciotti, D. Cimini, and R. Ware, “Modeling and Measurement of Rainfall by Ground-Based Multispectral Microwave Radiometry,” IEEE Transactions on Geoscience and Remote Sensing, 43: 1000-1011 (May 2005); and F.S. Marzano, D. Cimini, and R. Ware, “Monitoring of Rainfall by Ground-Based Passive Microwave Systems: Models, Measurements, and Applications,” Advances in Geosciences, 2: 259-265 (2005).

21

See C. Surussavadee and D.H. Staelin, “Global Satellite Millimeter-Wave Precipitation Retrievals Trained with a Cloud-Resolving Numerical Weather Prediction Model: Part II: Performance Evaluation,” IEEE Transactions on Geoscience and Remote Sensing, 46(1): 109-118 (2008), which evaluates satellite observations of both rain and snowfall rates from satellites. A good conical scanning reference is C. Kummerow, J. Simpson, O. Thiele, W. Barnes, A.T.C. Chang, E. Stocker, R.F. Adler, A. Hou, R. Kakar, F. Wentz, P. Ashcroft, T. Kozu, Y. Hong, K. Okamoto, T. Iguchi, E. Kuroiwa, E. Im, Z. Haddad, G. Huffman, B. Ferrier, W.S. Olson, E. Zipser, E.A. Smith, T.T. Wilheit, G. North, T. Krishnamurti, and K. Nakamura, “The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit,” Journal of Applied Meteorology, 39(12): 1965-1982 (2000).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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FIGURE 2.10 National Snow and Ice Data Center global monthly Equal-Area Scalable Earth (EASE)-Grid snow water equivalent (SWE) climatology for the Northern Hemisphere, December 2004. The overall data set in which this climatology appears comprises monthly satellite-derived SWE climatologies from November 1978 through June 2003. The global data are gridded to the Northern and Southern 25 kilometer EASE-Grids. Available at http://nsidc.org/research/projects/Armstrong_SWE.html. Provided by the National Snow and Ice Data Center.

FIGURE 2.10 National Snow and Ice Data Center global monthly Equal-Area Scalable Earth (EASE)-Grid snow water equivalent (SWE) climatology for the Northern Hemisphere, December 2004. The overall data set in which this climatology appears comprises monthly satellite-derived SWE climatologies from November 1978 through June 2003. The global data are gridded to the Northern and Southern 25 kilometer EASE-Grids. Available at http://nsidc.org/research/projects/Armstrong_SWE.html. Provided by the National Snow and Ice Data Center.

Glaciers

Passive microwave sensors can perform spatial mapping of the amount of snow overburden and the melt state of large ice sheets such as those over Greenland and Antarctica. Annual mapping of the ablation zone of the Greenland ice sheet is particularly important as a sensitive means of determining the melt state of the glacial margins and the region of continued deposition of snow.22 Passive

22

W. Abdalati and K. Steffen, “Snowmelt on the Greenland Ice Sheet as Derived from Passive Microwave Satellite Data,” Journal of Climate, 10(2): 165-175 (1997).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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microwave window channels from approximately 10 GHz to approximately 90 GHz are sensitive to reflection caused by melting ice water and have been used to study subtle, regionally dependent climate trends in Greenland and Antarctica for nearly two decades. Knowledge of snow overburden is important as a means of estimating the heat transfer from the glacier to the atmosphere, as snow is a good thermal insulator. Passive microwave channels at 18 and 37 GHz are useful for measuring snow depth by virtue of the differential scattering signature available using these two bands.

Sea Surface Salinity

Sea surface salinity (SSS) is a critical missing parameter that scientists need in order to meet climate research goals. Measuring global SSS over time will contribute to scientists’ understanding of change in the global Earth system and of how the system responds to natural and human-induced change. Global measurements of SSS can be achieved to approximately 0.2 practical salinity units using space-based passive microwave radiometry at 1.4 GHz and radar scatterometry at 1.26 GHz. These measurements can provide significant new information on how global precipitation, evaporation, and the water cycle are changing. Global SSS variability provides key insight regarding freshwater flow into and out of the ocean associated with precipitation, evaporation, ice melt, and river runoff. Global SSS measurements will also provide important background about how climate variation induces changes in global ocean circulation. The combination of global sea surface salinity and sea surface temperature measurements can be used to determine seawater density, which regulates ocean circulation and the formation of water masses.

Sea Ice

One of the first applications of space-based passive microwave imagery was that of monitoring the location, extent, and thickness of sea ice. The ESMR data set provides the earliest all-weather, all-season imagery of polar sea ice. Some satellite data of sea ice in the visible and infrared wavelengths were available in the late 1960s and early 1970s (before the introduction of space-based passive microwave observations), but because the polar regions are either dark or cloud-covered for much of the year, the generation of consistent, long-term data records from visible and infrared sensing was not practical.

Passive microwave data introduced a major advance in the usefulness of satellite sea ice imaging. The value of passive microwave data for sea ice studies derives from the large contrast in microwave emissivities between sea ice and open water. At 19.35 GHz, open water has an emissivity of approximately 0.44, whereas various sea ice types have emissivities ranging from approximately 0.8 to 0.97. The

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

resulting contrast in microwave brightness temperatures allows accurate estimates of sea ice concentrations (percentages of ocean area covered by sea ice) and hence identification of sea ice distributions throughout the region of observation, as well as temporal variations of these distributions throughout the time period of observation.

Freeze-Thaw Transition

An area of study similar to sea ice involves the seasonal freeze-thaw transition of the Northern Hemisphere, which is a significant source and sink of atmospheric CO2. The exact timing of the spring thaw and the resulting length of the growing season can fundamentally affect the net carbon exchange budget between land and atmosphere.23 The thawing of polar tundra also results in more solar absorption and heating, with the possible runaway production of methane from the anaerobic decomposition of subsurface biomass. Passive microwave observations from space—augmented by radar—are the primary means for observing the freeze-thaw transition on a global scale.24 Determining the freeze-thaw transition requires the use of all of the primary atmospheric window channels between 1.4 and 90 GHz, up to and exceeding the EESS allocated bandwidth on primary or secondary basis.25

Biomass

Earth’s vegetation canopy, or biomass, is a significant component of the global carbon inventory. It is also a major contributor to the net long-wave/short-wave albedo of the planet and hence to Earth’s energy balance and temperature. For these reasons, climate change can both be affected by and can itself affect the global distribution of biomass. The ability to perform comprehensive inventories of biomass from space is recognized as a critical step toward modeling and understanding Earth’s climate system.26 Passive microwave observations operating in all of the

23

S. Frolking, M.L. Goulden, S.C. Wofsy, et al., “Modeling Temporal Variability in the Carbon Balance of a Spruce/Moss Boreal Forest,” Global Change Biology, 2: 343-366 (1996); J.T. Randerson, C.B. Field, I.Y. Fung, and P.P. Tans, “Increases in Early Season Ecosystem Uptake Explain Recent Changes in the Seasonal Cycle of Atmospheric CO2 at High Northern Latitudes,” Geophysical Research Letters, 26(17): 2765-2768 (1999); T.A. Black, W.J. Chen, et al., “Increased Carbon Sequestration by a Boreal Deciduous Forest in Years with Warm Springs,” Geophysical Research Letters, 27(9): 1271-1274 (2000).

24

T. Zhang and R.L. Armstrong, “Soil Freeze/Thaw Cycles over Snowfree Land Detected by Passive Microwave Remote Sensing,” Geophysical Research Letters, 28(5): 763-766 (2001).

25

National Research Council, Handbook of Frequency Allocations and Spectrum Protection for Scientific Uses, Washington, D.C.: The National Academies Press, 2007.

26

National Research Council, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond Washington, D.C.: The National Academies Press, 2007.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

primary atmospheric window channels between 1.4 and 90 GHz are valuable for monitoring the full range of vegetation canopy water content found in nature and are complementary to optical and synthetic aperture radar techniques. Improved techniques for biomass estimation using passive microwave methods are continuously being developed.27

Resource Management

Another application for EESS measurements involves the management of water, energy, and land use, including agriculture and urbanization. All of these applications use observations from multiple sources, including satellites as well as aerial and ground-based measurements. Passive microwave remote sensing is particularly important for assessing phenomena such as soil type and moisture, which can then be related to lake, wetlands, and reservoir storage, to river discharge, and to linkages in the water, energy, and carbon cycles. Other passive microwave products can be used to monitor the size, nutrient status, and other health measures of forests, crops, and vegetation; changes in vegetation type, deforestation, and other land cover; and geographic characterization of the “footprints” of urban areas. Urban and suburban areas play an often-overlooked but important role in Earth’s physical and ecological systems, adding to the understanding of mesoscale climatic, hydrologic, and ecologic processes.28

Box 2.2 summarizes typical uses of EESS data in reservoir management, the deployment of renewable energy systems, and agricultural forecasting, as reported in a recent evaluation of uses of Earth observations by the U.S. Climate Change Science Program. An additional, long-standing use of data includes the assessment of food security—for instance, in the Famine Early Warning System Network of the U.S. Agency for International Development29 (see an overview of and details about the network in a report published by the National Research Council in 200730 ).

One of the most recent applications of EESS data involves the use of information about water quality, vegetation health, population distribution, and other observations as pathways by which to track disease vectors and their implica-

27

S. Paloscia and P. Pampaloni, “Microwave Vegetation Indexes for Detecting Biomass and Water Conditions of Agricultural Crops,” Remote Sensing of Environment, 40: 15-26 (1992); G. Macelloni, S. Paloscia, P. Pampaloni, and E. Santi, “Global Scale Monitoring of Soil and Vegetation Using Active and Passive Sensors,” International Journal of Remote Sensing, 24(12): 2409-2425 (2003).

28

People and Pixels: Linking Remote Sensing and Social Science, Washington, D.C.: National Academy Press, 1998.

29

Gregory E. Glass, “Rainy with a Chance of Plague: Forecasting Disease Outbreaks from Satellites,” Future Virology, 2(5): 225-229 (2007).

30

National Research Council, Contributions of Land Remote Sensing for Decisions About Food Security and Human Health, Washington, D.C.: The National Academies Press, 2007.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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BOX 2.2

Examples of Earth Exploration-Satellite Service Measurements in Managing Water, Energy, and Agriculture

Reservoir Management


RiverWare is a river basin modeling system that integrates features of reservoirs (recreation, navigation, flood control, and water quality and supply) and electric utility requirements in order to provide basin managers and power managers with a method for planning, forecasting, and scheduling reservoir operations. Inputs to RiverWare include microwave data from the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) and data from other sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). RiverWare is a collaborative project among the Center for Advanced Decision Support for Water and Environmental Systems at the University of Colorado at Boulder, the Bureau of Reclamation, the Tennessee Valley Authority, and the Army Corps of Engineers.


Renewable Energy Deployment


The U.S. Department of Energy’s National Renewable Energy Laboratory uses data from MODIS, the Multiangle Imaging Spectroradiometer (MISR), Advanced Very High Resolution Radiometer, Special Sensor Microwave/Imager, and a host of weather and other data—including measurements of ocean wind, solar and geothermal resources, upper air, and digital terrain/land cover—to assist in the deployment of renewable energy technologies. This model, the Hybrid Optimization Model for Electric Renewables (HOMER), is used to design grid-connected and off-grid renewable energy systems.


Agricultural Forecasting


Agricultural management has long employed moderate-resolution optical imagery, beginning with the Agriculture and Resources Inventory Surveys and continuing with both the Aerospace Remote Sensing and the Large Area Crop Inventory Experiment programs during the 1970s and 1980s. Passive microwave data (from the SSM/I and AMSR-E and other systems) are now routinely incorporated into new agricultural applications. Perhaps most prominent among these is the system run by the U.S. Department of Agriculture’s Foreign Agriculture Service (USDA/FAS): the Production Estimate and Crop Assessment Division’s Crop Condition Data Retrieval and Evaluation (PECAD/CADRE) system. The FAS collects and analyzes global crop intelligence information and provides estimates to inform official USDA forecasts for the agricultural market. That market includes farmers, agribusiness enterprises, commodity traders, and researchers, as well as federal, state, and local agencies. PECAD/CADRE is one of the largest users of data from EESS agriculture-related measurements.


SOURCE: U.S. Climate Change Science Program, Synthesis and Assessment Product 5.1, “Uses and Limitations of Observations, Data, Forecasts, and Other Projections in Decision Support for Selected Sectors and Regions,” November 7, 2008. Available at http://www.climatescience.gov/Library/sap/sap5-1/final-report/.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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tions for human health.31 Glass, in Future Virology, discusses the challenges and opportunities provided by EESS data and notes the potential for EESS to advance health assessment beyond the monitoring of disease outbreaks to the forecasting of outbreaks.32 The advance notice provided by accurate forecasting of outbreaks could allow better deployment of health resources to minimize the spread and impact of disease.

Passive microwave observations of the hydrosphere and the cryosphere are similarly important. A scientific understanding of the mechanism of cycling of freshwater and the amount and distribution of the world’s frozen water stores is essential for human survival. Again, passive microwave measurements made at a number of frequencies and from a number of platforms are unique in being able to provide this information.

Aviation

For aviation, the most useful passive services are the U.S. and global weather services and forecasts, which benefit greatly from the inclusion of passive microwave data from satellites. Surface-based, upward-looking microwave radiometers have the unique ability to remotely detect supercooled liquid water that adheres to aircraft flight surfaces and helicopter rotors, and which has been responsible for numerous losses of aircraft and lives. These same radiometers also improve the skills of the forecasters of short-term local aviation weather. Currently, a sparse network of balloon-based profiling (i.e., “radiosonde”) sites across the United States, with an average spacing of 315 km, sounds the atmosphere every 12 hours, supplemented by satellite overpasses every several hours. This sparse sampling severely limits short-term forecasting, especially that of severe weather. Ground-based radiometers can duplicate continuously, autonomously, and with minimal ongoing costs many of the data-providing functions of radiosondes (except for measuring winds aloft and providing high vertical resolution).

Fog events have a significant effect on aviation, slowing or halting airport air traffic operations and causing diversions of incoming air traffic.33 These events are seasonally chronic at some locations and infrequent at others. The onset, duration, and dissipation of fog are difficult to measure and to predict with currently employed technologies (radars, radiosondes, visibility and surface meteorology systems). Ground-based radiometers can measure the vertical profiles of temperature,

31

Ibid.

32

Gregory E. Glass, “Rainy with a Chance of Plague: Forecasting Disease Outbreaks from Satellites,” Future Virology, 2(5): 225-229 (2007).

33

For more information, see “Airline Regulators Grapple with Engine-Shutdown Peril,” Wall Street Journal, Monday, April 7, 2008.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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water vapor, and fog liquid water and therefore have the ability to characterize such fog events. Dubai in the United Arab Emirates has recently had a highly capable three-dimensional fog prediction and monitoring system installed at its airport; the system is based on a microwave radiometer, a wind profiler, surface meteorology, and a computer system.

As an example of the disruptions that fog events can create, on February 15, 2001, a surface-based temperature, water vapor, and cloud liquid water microwave radiometer detected precursors and the onset of meteorological conditions characteristic of persistent ground fog. This fog subsequently shut down Denver International Airport (DIA) for 18 hours at tremendous cost, stranded thousands of passengers, and caused a ripple across the entire air traffic scheduling and flow system. When the situation, including the microwave radiometric temperature, water vapor, and cloud liquid profile data, was replayed into the so-called “MM5” NWP model at the National Center for Atmospheric Research, the model accurately predicted the onset, persistence, and dissipation of this fog.

In another episode, on March 4, 2003, light, freezing drizzle was foreseen, detected, and tracked by a research microwave radiometer monitoring surface-based temperature, water vapor, and cloud liquid water. This condition caused the failure of six jet engines owing to the ingestion of ice on United Airlines (UAL) 737s that were taxiing for takeoff at Denver International Airport, grounding the six aircraft. The direct cost to UAL was reported to be $1.2 million, with an unknown cost resulting from the grounding of the aircraft for engine repairs, other resultant flight cancellations, and further associated costs. In April 2007, this same meteorological condition was foreseen by microwave radiometers, whereupon the radiometer operator unsuccessfully attempted to contact UAL at DIA to forewarn the airline. Two more UAL aircraft lost engines and were grounded. Such losses should diminish as these sensors become operational. To date, UAL has reportedly lost 18 engines at DIA as a result of this meteorological condition. An operational system would have been able to forecast and nowcast this condition, allowing ample warning time for the implementation of preventative procedures.

Beyond aviation issues, fog often causes hazardous surface transportation conditions. It was the cause of a 78-vehicle pileup on Interstate 5 near Fresno, California, in 2002, as well as the precipitator of a number of recent multiple-vehicle accidents across the United States.34 Ground-based radiometers are being installed in Europe at problem locations for predicting and monitoring fog events.

34

Gary Sanger, “Winter Weather Summary,” available at http://newweb.wrh.noaa.gov/hnx/newslet/spring02/summary.htm; accessed June 22, 2008.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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Defense and Public Safety

Although the passive EESS bands are not specifically allocated for defense purposes, they are extensively used by meteorological satellites that support analyses and forecasts serving many defense needs. In fact, many radiometers on operational meteorological satellites are or have been part of the Defense Meteorological Satellite Program (DMSP) satellite constellation operated by the U.S. Department of Defense (as listed in Table 2.2 later in this chapter). For example, the Special Sensor Microwave/Imager Sounder radiometer, which is aboard a DSMP satellite, measures atmospheric temperature and moisture profiles, sea surface winds, cloud liquid content, and land surface parameters on a continuous basis from low Earth orbit. This military meteorological polar-orbiting satellite program has been merged with those of NOAA and NASA in the NOAA Integrated Program Office to form the NPOESS program, which will soon launch its first satellite. Microwave meteorological satellites improve forecasts of the following: (1) weather, which influences essentially all combat missions in the air, on the ground, and at sea; (2) the dispersal and transport of released chemical, biological, or radiological agents, where such knowledge supports defensive measures; (3) the monitoring of the ducting of radio waves over the ocean caused by high gradients in the refractivity of the boundary layer, where such ducting can make ships and aircraft visible to radar at anomalously large distances or invisible at normal distances; (4) the traversability of muddy roads, tundra, or pack ice; (5) battlefield visibility; and (6) trajectory corrections for artillery and other projectiles. In addition there are nonmeteorological covert defense applications of passive sensors: for example, the passive detection of metallic objects, such as tanks and trucks concealed by foliage or camouflage or ships shrouded in fog.

Ground-based microwave radiometers can accurately and precisely measure (to better than 0.5°C in most cases) the temperature profile in the tropospheric boundary layer on a continuous basis. This capability is being used to measure and track inversions that trap clouds, pollution, and aerosols. Knowledge of boundary layer temperature profiles is also important in predicting the transport and spread of accidental or hostile releases of biological agents, nerve agents, and radioactive agents. Such radiometers are being used for continuous monitoring at nuclear power plants in Switzerland, Las Vegas, Beijing, Taiwan, and elsewhere. These radiometers can also measure the water vapor and cloud liquid profile in the boundary layer. Such data are highly important because of the interaction of clouds with aerosols and other gases to form smog. Radiometers can also be used to continuously monitor the atmospheric effects associated with large urban heat islands that can impact health, public utility loads, and human activities.35

35

Mikhail N. Khaikin, Iren Kuznetsova, Evgeny N. Kadygrov, and Evgeny A. Miller, “Investigation of Temporal-Spatial Parameters of an Urban Heat Island on the Basis of Passive Microwave Remote Sensing,” Theoretical and Applied Climatology, 84(1-3): 161-169 (2006).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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International Partnerships

It has long been known that sound management of the Earth system, in both its natural and human aspects, requires information that is timely, of known quality, long term in its availability, and global. In 2003, the United States hosted a ministerial-level Earth Observation Summit in Washington, D.C., to promote joint multilateral action that would lead to the continuous monitoring of the state of Earth in order “to increase understanding of dynamic Earth processes, to enhance prediction of the Earth system, and to further implement our international environmental treaty obligations.”36 An ensuing series of summits established a mandate for the development of the Global Earth Observation System of Systems (GEOSS).37 GEOSS is a complex system of sensors, communication devices, storage systems, and computational and other devices used to observe Earth and to gather the data needed for a better understanding and enhanced prediction of Earth’s processes. GEOSS is a “system of systems” consisting of existing and future Earth observation systems contributing to an international and interoperable data network. The emphasis of GEOSS is on societal benefits in nine key areas:

  • Disasters: Reducing loss of life and property from natural and human-induced disasters;

  • Health: Understanding environmental factors affecting human health and well-being;

  • Energy: Improving the management of energy resources;

  • Climate: Understanding, assessing, predicting, mitigating, and adapting to climate variability and change;

  • Water : Improving water resource management through better understanding of the water cycle;

  • Weather: Improving weather information, forecasting, and warning;

  • Ecosystems: Improving the management and protection of terrestrial, coastal, and marine resources;

  • Agriculture: Supporting sustainable agriculture and combating desertification; and

  • Biodiversity: Understanding, monitoring, and conserving biodiversity.

The United States is a key signatory to the development of GEOSS through the international Group on Earth Observations (GEO).

36

Group on Earth Observations, “Declaration of the Earth Observation Summit,” Washington, D.C., July 31, 2003, available at http://earthobservations.org/docs/Declaration-final%207-31-03.pdf; accessed December 30, 2009.

37

See http://www.earthobservations.org/about_geo.shtml; accessed March 31, 2008.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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While GEOSS data originate from a variety of sources, there are many important environmental parameters needed by GEOSS users that can be measured only by passive microwave sensors. These include global ocean salinity; sea ice characteristics; soil moisture; rain, cloud, and related atmospheric hydrometric variables; water vapor and temperature profiles under clouds; and trace gases. Without the protection offered by EESS passive radio allocations, the international community would be denied information vital to achieving the goals of GEOSS designed for the benefit of society.


Finding: Passive remote sensing observations are essential for monitoring Earth’s natural systems and are therefore critical to human safety, the day-to-day operations of the government and the private sector, and the policy-making processes governing many sectors of the U.S. economy.

Education and Technology

A large number of engineers working in the U.S. telecommunications and defense electronics industry have learned basic radio science skills through graduate or early-career work on any of a number of DOD, NASA, NOAA, National Science Foundation, or Department of Energy passive microwave sensor programs. Examples include spaceborne, airborne, shipborne, and ground-based sensor programs for environmental observation. Not all students trained in the passive microwave area continue their careers in the field, but the importance ascribed to precise instrument calibration, the detection of low signal levels, and innovative signal and image processing provides unusually strong training for careers in many other economically important technology areas. The same can be said for students trained in radio astronomy (see §3.6). Accordingly, Earth remote sensing contributes indirectly to those economic sectors that are critical to U.S. global competitiveness and defense.

In addition to radio science education, the application of passive microwave radiometry to environmental monitoring provides a key means of training Earth scientists. Whether they work in the U.S. government or in organizations around the globe, the next generation of students entering this discipline will need global experience in environmental stewardship and sustainability. The interconnectedness of regions, states, countries, and continents by environmental ties makes U.S. prosperity ever more contingent on the capabilities of environmental scientists, engineers, and managers outside the nation’s borders. To this end, valuable global experience in environmental observation is provided through satellite-based passive microwave studies.

Technological spin-offs from passive microwave Earth remote sensing studies are numerous. They include new techniques for instrument calibration, image pro-

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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cessing, and data-assimilation capabilities that extend beyond the fields of weather forecasting, radio detection methods using statistical moments, and radio imaging techniques for aircraft navigation in all-weather conditions and for homeland security needs. Additionally, the technology underlying passive Earth remote sensing has led to new submillimeter-wave imaging capabilities for detecting hidden weapons. This technology is now beginning to make its way into screening operations at airports across the United States. Also, the design of cost-effective, stable, integrated microwave receivers has been furthered as a result of needs for such receivers within the passive remote sensing community. Such receiver technology is now found in active communications and radar sensing devices. Finally, the requirement of extremely high main-beam efficiency antennas in passive remote sensing has engendered the development of antennas with low sidelobes for other commercial and defense applications.


Finding: Passive microwave Earth remote sensing provides a diverse and valuable set of educational opportunities.


Finding: In addition to the intellectual benefits that they provide, passive microwave remote sensing studies provide many technological benefits to American society.

2.2
BRIGHTNESS TEMPERATURES, GEOPHYSICAL MEASUREMENTS, AND MISSIONS

Section 2.1 established the range of applications and the importance of passive microwave radiometry. This section describes the processes by which these sensors operate, providing detailed information on the specific geophysical measurements that result. In addition, a summary of previous and future radiometer missions is presented in order to provide context for the current state of passive microwave sensing.

Fundamentals of Microwave Radiometry for EESS Applications

All matter emits low levels of electromagnetic radiation. This radiated “thermal noise” is determined by the temperature and electromagnetic properties of the emitting medium, including its ability to absorb, emit, and scatter electromagnetic waves. Geophysical properties of the medium can be inferred from microwave radiometer measurements of emitted thermal noise power to the extent that those properties are related to the bulk electromagnetic properties of the medium. The thermal noise power radiated at a given frequency is commonly expressed as a “brightness temperature.” This is the physical temperature of an ideal emitter

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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(called a blackbody) that would radiate the same amount of noise power at that frequency. The brightness temperature of a scene (reported in units of kelvin by a microwave radiometer) contains the geophysical information of interest. Although multiple geophysical parameters may affect the brightness temperature—for example, the temperature and moisture level of Earth’s surface and the temperature, humidity, and cloud properties of the atmosphere—these parameters can be distinguished when they have distinctive frequency and/or polarimetric signatures, so that simultaneous observations of the brightness temperature at multiple frequencies and polarizations enable simultaneous solutions for the geophysical properties of interest. There exists a long history of innovation in passive microwave EESS observations for solving this multiple-parameter estimation problem. For many applications, it is necessary for simultaneous measurements to be made over several octaves of the microwave spectrum in order to distinguish adequately the contributions to the brightness temperature made by the surface and the atmosphere.

Traditional radiometer receivers simply estimate the thermal noise power (in watts) received within a particular radio band by a noncoherent radio receiver consisting of (typically) an antenna, a low-noise amplifier, a filter that limits the observed portion of the frequency spectrum, and a square-law detector that provides a measurement of power in the channel. The output of the square-law detector is averaged over time and then recorded and processed to yield geophysical information.

It is well known that the uncertainty in measurements of brightness temperature is reduced by using larger bandwidths (insofar as is permitted by spectral allocations) and longer integration times (constrained for spaceborne EESS observations by satellite orbit and coverage requirements). While calibration accuracy and internal noise once commonly dominated overall system uncertainty, continuing instrument improvements now often achieve the fundamental sensitivity determined by the time-bandwidth product, thus reaching the maximum achievable sensitivity of the estimated geophysical parameters.38 Therefore, radio interference to passive systems must be compared to this fundamental limit. In contrast, modern communications systems have yet to approach this so-called Shannon limit. In other words, further improvement in EESS sensor technology will, in general, have minimal impact on measurement accuracy compared to greater time-bandwidth product usage. This is especially true for the most important measurements currently being carried out on an operational basis in EESS. However, technological improvements in the use of spectrum for communications systems are still possible.

38

See §3.4, “Sensitivity Requirements,” for more information on the signal-to-noise ratio of passive microwave measurements.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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Although the physics that determines brightness-temperature signatures can be complex, usually just a few principal effects dominate. The absorption and emission of radio waves propagating through the terrestrial atmosphere are strong functions of frequency owing to resonant absorption by atmospheric gases. Figure 1.2 in Chapter 1 shows the total zenith attenuation of microwaves propagating upward through a clear standard atmosphere from sea level. Gas resonances are apparent near 23, 60, 118, 183, and 325 GHz. The primary atmospheric absorbers below 350 GHz are molecular oxygen (resonances near 60 and 118 GHz) and water vapor (23, 183, and 325 GHz). Above the troposphere, absorption and emission by trace gases become more pronounced—for example, HNO3 at 182 GHz, N2O at 201 GHz, ClO at 204 GHz, and O3 at 206 GHz. Radio frequencies used for the Earth Exploration-Satellite Service are usually designated either as “windows” used for observing the surface or total atmospheric attenuation, or as “opaque” and used for estimating atmospheric profiles of temperature or composition. Since radio astronomy uses these same windows to observe the universe from the ground, there is much spectrum compatibility between the two sciences.

Systems for sensing atmospheric properties can be designed to exploit atmospheric absorption and emission resonances. For example, many radiometers include observations near the semitransparent window frequencies 23 and 37 GHz in order to estimate the integrated columnar water vapor and liquid water content of the atmosphere. It is possible to estimate these two unknown abundances because the lower frequency is near the 22.235 GHz water vapor resonance, whereas at 37 GHz cloud absorption is relatively stronger. Observations at the two bands yield two relations that can be inverted to find the two unknowns, that is, the amounts of water vapor and liquid water. It is furthermore possible to estimate atmospheric temperature and/or molecular abundance versus altitude (i.e., temperature or abundance “profiles”) by measuring atmospheric brightness temperature as a function of frequency near a resonance. Frequencies in the more transparent regions farther from any resonance generally see deeper into the atmosphere, whereas frequencies near the more opaque core of a resonance sense only conditions relatively near the sensor. Comparing such measurements permits the temperature profile to be determined if the composition is known, or the composition if the temperature profile is known. By combining measurements of different spectral lines, both temperature and composition can be determined simultaneously.

A few underlying physical principles characterize the capabilities of most passive microwave sensors operating in the “window” channels. First, lower-frequency waves generally penetrate intervening media better and sense deeper beneath the surface. Thus low frequencies such as 1.4 GHz are preferred when sensing subsurface soil moisture beneath vegetative canopies. Second, the influence of surface roughness tends to be largest when the length scales of the roughness are compa-

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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rable to the electromagnetic wavelength. This fact motivates the use of X-band or higher frequencies in attempting to sense the short sea waves (capillary waves) that are most sensitive to sea surface winds at low wind speeds. Third, the dielectric constant of water is a strong function of frequency, temperature, and the water’s phase (i.e., ice, liquid, or vapor). A result is that the frequencies most sensitive to sea surface salinity are below approximately 2 GHz, whereas those most sensitive to sea surface temperature lie nearer to 5-10 GHz. Figures 2.11 and 2.12 illustrate for sea and land scenes, respectively, typical sensitivities of microwave radiometers to various environmental properties versus frequency.

Since multiple geophysical properties typically contribute to the observed brightness at a given frequency, multiple frequencies must be observed simultaneously in order to estimate them separately. Because all window channels exhibit some atmospheric absorption and emission, and even atmospheric resonant fre-

FIGURE 2.11 Ocean scene: relative sensitivity of sea surface salinity, sea surface temperature, cloud liquid water, and integrated water vapor as a function of frequency for space-based measurements. Original figure by Thomas T. Wilheit, NASA-GSFC.

FIGURE 2.11 Ocean scene: relative sensitivity of sea surface salinity, sea surface temperature, cloud liquid water, and integrated water vapor as a function of frequency for space-based measurements. Original figure by Thomas T. Wilheit, NASA-GSFC.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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FIGURE 2.12 Land scene: relative sensitivity of the brightness temperature to soil moisture, cloud liquid water, and integrated water vapor as a function of frequency for space-based measurements.

FIGURE 2.12 Land scene: relative sensitivity of the brightness temperature to soil moisture, cloud liquid water, and integrated water vapor as a function of frequency for space-based measurements.

quencies are often not completely opaque, most instruments incorporate both window and opaque channels.


Finding: Effective passive microwave band allocations are necessary for the performance of environmental observation functions.


Finding: Radio wave bands (10 MHz to 3 THz) are indispensable for collecting environmental information associated with specific physical phenomena. Often the same bands are similarly indispensable for radio astronomy, and the passive nature of both services enables them to share the spectrum productively.

Measurement of Specific Geophysical Parameters

Whereas Figure 1.2 in Chapter 1 presents the basic physics of observations through Earth’s atmosphere for passive microwave spectral observations, Figures 2.11 and 2.12 also take into account fundamental characteristics of the measured parameters. Because the estimates of the geophysical parameters, also called Environmental Data Records (EDRs), are computed as a function of observed brightness temperatures, it is possible to find the average ratio of a change in a specific EDR to the corresponding change in a particular brightness temperature. This ratio is called the “sensitivity” of the EDR (as distinguished from the radiometric uncertainty of the original radiometer measurement). For example, the sensitivity of surface wind speeds over ocean is expressed in units of ms–1K–1. While the numerous

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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channels used in retrieving many EDRs can make this a complicated quantity to determine exactly, the values in Figures 2.11 and 2.12 generally reflect the sensitivity from the primary channels influencing errors in a particular EDR. This ratio permits the accuracy requirements of a particular EDR to be related to the accuracy requirements of the associated radiometric system. Alternately, radio frequency interference levels (K) can be related to resulting errors in EDRs. For example, the sensitivity of sea surface temperature (SST) to the vertically polarized 5 GHz brightness temperature is roughly 0.5 K(Tb)/K(SST). Since current scientific requirements for climate studies include retrievals of SST accurate to within 0.5 K or better, radio frequency interference that causes a 0.25 K change in 5 GHz brightness temperatures would pose a major problem for the retrieval of accurate sea surface temperatures. Similar quantitative statements can be made regarding other EDRs.

It is important to recognize that the EDR products shown in Figures 2.11 and 2.12 are simply unavailable on a global scale from any other type of sensor, particularly for all-weather conditions. These products include critical atmospheric parameters for NWP such as atmospheric temperature, humidity profiles, and precipitation rate. Considering global cloud conditions, surface infrared measurements are possible over an average of 5 percent of Earth’s surface and over 30 percent of Earth for the upper troposphere. At somewhat higher altitudes, atmospheric temperature and moisture profiles from microwave measurements (e.g., AMSU) are possible over 70 percent of Earth’s surface and 95 percent for the upper troposphere.39

Table 2.1 provides a summary of the common geophysical products and the microwave frequencies used for their measurement in current, future, and proposed missions, with an indication of the potential impact of those measurements from radio frequency interference (RFI) based on the current radio frequency environment.

2.3
CURRENT AND FUTURE SPACE MISSIONS, ACTIVITIES, AND SPECTRUM USAGE

Because of the wide range of EESS applications of microwave radiometry, numerous space-based missions are currently in operation or are planned for the near future. Table 2.2 provides a detailed list of such missions, including their planned spectrum usage and intended applications of Environmental Data Records. It is evident that microwave radiometry is widely used by the space agencies of the United States and other nations for sensing both atmospheric and surface properties and that passive microwave radiometry will continue to be widely

39

R. Saunders, “Use of Microwave Radiances for Weather Forecasting,” presentation at the 24th Annual Space Frequency Coordination Group Meeting, France, September 20, 2004.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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TABLE 2.1 Summary of Common Geophysical Products and the Microwave Frequencies Used for Their Measurement in Current, Future, and Proposed Missions, Including the Potential Impact of Those Measurements from Radio Frequency Interference Based on the Currently Known Radio Frequency Environment

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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NOTE: Additional detail on Earth Exploration-Satellite Service parameters related to this table is provided in Appendix E. In the columns “Earth Exploration-Satellite Service Passive Microwave Frequencies” and “Summary of RFI Potential,” red indicates high RFI potential, yellow indicates moderate RFI potential, and green indicates low RFI potential. The colors in between (red-yellow and yellow-green) indicate moderate-high and moderate-low RFI potential.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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TABLE 2.2 Past, Current, Future, and Proposed Operational and Scientific Earth Exploration-Satellite Service Missions Providing Critical Operational Data for Weather Forecasting and for Military and Civil Operations in Which the United States Has Participated

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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NOTE: SST (*) indicates reduced capability in colder regions (less than about 12°C). The number of each type of EESS radiometer currently in operation, planned for operation, or proposed for operation is included in parentheses with the listing of its U.S.-based associated missions. Acronyms are defined in Appendix F. In the column “Radio Frequency Interference (RFI) Experiences,”/”RFI Susceptibility,” red indicates high RFI potential, yellow indicates moderate RFI potential, and green indicates low RFI potential. The colors in between (red-yellow and yellow-green) indicate moderate-high and moderate-low RFI potential.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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employed. Of particular note are the Soil Moisture Ocean Salinity (SMOS) (launch: 2009) and Aquarius (estimated launch: 2010) missions, which will provide the first demonstration of space-based sensing of sea salinity; the Soil Moisture Active Passive mission (estimated launch: 2013 or 2014) for the measurement of global soil moisture; and the NPOESS sensor suite (including the Advanced Technology Microwave Sounder [ATMS] and the Microwave Imager/Sounder system currently being designed) that will provide a wide range of EDR records.

The first U.S. passive microwave radiometer missions date back to 1972. Since then, EESS has continued to fly passive microwave radiometers with ever-increasing capability and covering an expanding range of frequencies. Of note is the current interest in measurements of 1.4 GHz and 6.8 GHz brightness temperature to support sea surface salinity and soil moisture measurements, critical to the continued improvement of weather and climate measurements as described in §2.1, with additional background supplied in Appendix E. In Table 2.2, the number of each type of EESS radiometer currently in operation is included in parentheses with the listing of its U.S.-based associated missions. There is currently a total of 30 missions. Including international missions in which the United States is not involved, the total number is more than 44 missions. Planned and proposed missions include at least 18 more space-based passive radiometers. The complete list of missions represents substantial national and international investment in passive radiometry. Table 2.2 also indicates that several of these new and existing measurements are either currently being impacted by RFI or are highly likely to be impacted by RFI in the near future. A description of the RFI problem at each of the frequencies indicated in either red or yellow in Table 2.2 can be found in §2.5.


Finding: Scientific advances have required increasing measurement precision by passive radio and microwave facilities in order to obtain more accurate and thus more useful data sets. This need for precision will continue to increase.


Finding: Large investments have been made in satellite sensors and sensor networks and in major radio observatories. New facilities costing billions of dollars are under construction or are being designed.


Finding: Radio frequency interference threatens the scientific understanding of key variables in Earth’s natural system, now and in the future.

2.4
CURRENT AND FUTURE NON-SPACE-BASED ACTIVITIES AND SPECTRUM USAGE

Although satellites are now the primary data source driving global numerical weather prediction models, over the United States, ground-based meteorological

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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sensors and radiosondes launched at 12 hour intervals from 80 sites have long been the primary source. However, the ever-increasing power of computers leaves NWP models without data between radiosonde sites and launch times, thus limiting models’ forecasting capabilities. Moreover, the annual cost per radiosonde launch site is approximately $200,000. To address the problem of cost and sampling density in time and space, less-expensive, continuously operating autonomous ground-based microwave sensors are being developed to augment or replace parts of the present U.S. radiosonde network and thereby to reduce the number of potentially serious unexpected meteorological events that can arise between sample times and places.40 Moreover, such cloud-penetrating sensors can help calibrate those spaceborne sensors observing water vapor and cloud water content, parameters that vary so rapidly in time and space that they are difficult to validate.

Because of their reliability, economy, and simplicity of deployment, as well as the value of their observations, ground-based radiometers are being implemented in networks in Korea, China, and Europe and are included in a current request for proposals by the National Weather Service. Operational installations are being considered for oil platforms in the Gulf of Mexico. Ground-based radiometers were also deployed around the 2008 Olympics site in Beijing to improve short-term weather forecasts.

Ground-based radiometers can also continuously and locally generate valuable predictive meteorological parameters such as connective available potential energy (CAPE), K-index, total of totals index (TTI), lifted index (LI), and a dozen or so other indices, many of which are associated with severe and sudden-onset weather events.

An example of a current program for the monitoring of global change is the U.S. Department of Energy’s Atmospheric Radiation Monitoring (ARM) program, which employs ground-based up-looking passive microwave sensors to characterize the global radiation budget and clouds. These unattended systems continuously measure water vapor profiles and cloud liquid water accurately and inexpensively, relative to radiosondes. Moreover, they provide an integrated measurement that is thought to be more representative of the large-scale behavior of the atmosphere than are the measurements returned by radiosondes. Tropospheric water vapor profiles are measured using a number of bands near the water vapor lines at 22.235 or 183.310 GHz. Bands near the 22.235 GHz water vapor line yield integrated precipitable water vapor (PWV). For 15 years, ARM has used microwave radiometers installed in the tropical western Pacific and at locations up to 70 degrees north latitude for fundamental measurements of atmospheric water vapor. These obser-

40

Knupp, R. Ware, P. Herzegh, F. Vandenberghe, J. Vivekanandan, and E. Westwater, “Ground-Based Radiometric Profiling During Dynamic Weather Conditions,” Journal of Atmospheric and Oceanic Technology, 26(6):1057-1073 (2009).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
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vations have also helped to calibrate radiosondes around the world for weather forecasting and the generation of climate records.

2.5
THE IMPACT OF RADIO FREQUENCY INTERFERENCE ON EARTH EXPLORATION-SATELLITE SERVICE OBSERVATIONS

Microwave radiometers are, by necessity, extremely sensitive radio receivers and are thus very sensitive to radiation from communications, navigation, and other active radio systems. Most radiometers measure total power (brightness temperature) and have no means for distinguishing between naturally emitted thermal noise and the noise-like signals produced by other sources.

Interference can be detected if it is strong enough to be clearly distinguishable from the natural variations in scene brightness temperatures. Lower amounts of interference (i.e., comparable to the geophysical brightness variability) are much more difficult to identify and separate and can therefore compromise the accuracy of the retrieved geophysical information. Although efforts are underway to enhance the abilities of radiometers to detect and suppress interference (as described in Chapter 4), such improvements generally increase costs, data rates, and power consumption while achieving only limited success because of the indistinguishable components of the interference. The following discussion details the process by which human-made sources interfere with radiometry. It presents both specific examples of RFI impacts on Earth observations and justified concerns about future sources of interference.

Introduction to the Problem of Radio Frequency Interference: Immediate Impacts on EESS

EESS radiometers measure the naturally generated background brightness temperature (noise power) of Earth. Since the received power is very small, these radiometers are, by necessity, extremely sensitive instruments. This complicates their design because the background noise temperature that is being measured is so faint that interference power levels of far less than even 10−12 W can cause significant measurement errors. Additionally, for spaceborne instruments, the spot size for each individual observation is typically between 12 and 100 km, although smaller spot sizes exist: AMSR-E’s spatial resolution is 5 km at 89 GHz. As a result of these spot sizes, pinpointing the precise location of interferers is extremely difficult after launch.

Signals emitted from transmitters operating at frequencies within or adjacent to the passbands of EESS receivers (hereafter to include not only ground-based but also airborne radiometers for Earth observation) are the primary causes of radio frequency interference in EESS measurements. In many cases the interference is

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

due to spurious or out-of-band (OOB) emissions from transmitters operating in bands allocated for other radio services rather than due to signals that are intentionally transmitted in EESS bands. In yet other cases (for example, RFI observed within the 1400-1427 MHz EESS band), it is not always clear whether inadequate filtering within the EESS system or OOB or spurious emissions from active users are the cause, although it is noted that most EESS systems employ state-of-the-art filtering technology that cannot easily be improved.

Spurious and OOB transmitter emissions from commercial devices typically are neither precisely controlled during manufacture nor essential to the devices’ intended purposes. The ultimate impact of such emissions on a specific EESS geophysical measurement depends on the sensitivity of the geophysical parameter to changes in brightness temperature, as discussed in §2.2. The high radiometric accuracy and sensitivity achieved by current EESS systems result in commensurately high sensitivity to RFI that can cause errors in the retrieved geophysical parameters. The maximum signal-power contamination that can exist without impacting the information contained in the EESS measurement has been derived by EESS scientists for each of the EESS allocated bands and is documented in International Telecommunication Union-Radio (ITU-R) Recommendation RS.1029-2. Even when false measurements due to RFI are detected and eliminated, forecasts are degraded by the loss of data. Appendix C provides a derivation of the errors in EESS measurements of brightness temperature caused by a collection of anthropogenic sources within the EESS radiometer antenna footprint and frequency passband. Tables 2.1 and 2.2 also provide qualitative assessments of the RFI threat at particular frequencies and for particular missions, respectively.

The RFI threat is especially serious at frequencies lower than 50 GHz, where the atmosphere is largely transparent to radio waves and where frequency bands are widely used by EESS to provide information about environmental parameters. In the first attempts at direct radiance assimilation,41 only oceanic observations at such transparent frequencies were assimilated into NWP models because the cold microwave background signature of the ocean strongly contrasts with that of the atmosphere. Assimilation of radiances over land at these frequencies was not attempted owing to the relatively poor geophysical signature caused by the high emissivity of land. Recently, though, it has been demonstrated that with increas-

41

Direct radiance assimilation (sometimes just called radiance assimilation) involves the direct use of satellite brightness-temperature measurements to drive the internal state of an environmental model (e.g., a numerical weather prediction model). Now being widely adopted for forecasting purposes, this technique contrasts with the more established technique of performing a retrieval of an environmental parameter using the data. It is generally preferable to retrievals because it uses all available data to achieve the highest forecast accuracy. See, for example, L. Phalippou, “Variational Retrieval of Humidity Profile, Wind Speed, and Cloud Liquid-Water Path with the SSM/I: Potential for Numerical Weather Prediction,” Quarterly Journal of the Royal Meteorological Society, 122: 327-355 (1996).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

ingly accurate land-surface emission models, radiance assimilation at 23.8 and 31 GHz improves both forecasts and quality control of data from other bands. As a result, RFI as weak as 0.1 K or less can limit the use of these bands over land. A similar situation is anticipated with channels in the 1.4 GHz and 6 GHz bands, which are particularly sensitive to surface soil moisture. RFI below 10 GHz threatens to compromise or even eliminate the utility of these bands, which are unique in their ability to provide soil moisture information.

Since ground-based microwave radiometers are valuable for obtaining region-specific temperature and humidity profile data on the lower atmosphere for both nowcasting (typically out to 6 hours) and forecasting, and because they have the unique capability of obtaining low-resolution profiles of cloud liquid water, they are the instruments commonly used in urban areas and at airports where RFI is more likely. However, the tolerable interference levels are quite low for ground-based atmospheric sounding. For example, a 1 W isotropic transmitter at 1 km distance will contribute about 10 K of RFI to a typical uplooking microwave radiometer observing near the assemblage of oxygen lines centered at 60 GHz with a 15 cm antenna aperture, a 300 MHz bandpass filter, and 50 dB antenna sidelobes near the horizon. For a ground-based radiometer, even a 1 K RFI-induced perturbation in a typical seven-channel oxygen band temperature-profiling radiometer can yield an unacceptable 1.4 K error in the retrieved temperature profile. In practice, root mean square (RMS) instrument errors in oxygen-band radiometer measurements are as low as 0.5 K (or lower), and the nominal tolerable RFI level for these systems is 0.05 K. Increasing the number of observation channels in this wave band can mitigate, but not remove, the effect of narrowband RFI.

Evidence of Impact of Radio Frequency Interference on EESS Observations

The corruption of EESS data products by radio frequency interference, including impacts on EESS observations made solely within protected portions of the radio spectrum, has been extensively noted. Typical examples of interference within protected bands and nearby bands follow.

Protected Bands
L-Band (1.400-1.427 GHz)

Observations at 1.4 GHz over land by ground-based and airborne systems in support of remote soil moisture and sea surface salinity estimation are often compromised by what can be identified as OOB emissions from active systems. Total in-band emissions must remain below approximately −140 dBm from 1400-1427 MHz to ensure that anthropogenic (i.e., human-made) emissions do not influence SSS observations to more than a fraction of the necessary stability of 0.05 K that is

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

required to obtain 0.2 psu (practical salinity unit) SSS measurement uncertainty.42 The RFI contamination that can be tolerated for SM measurement is greater than that for salinity by approximately an order of magnitude; however, the density of transmitters over land is far greater than over ocean. Accordingly, slightly higher RFI contamination levels can be tolerated for 1.4 GHz SM measurements. But in both cases, the maximum tolerable interference level is lower than typical in-band interference from OOB emissions by legal radar transmissions in adjacent spectrum (e.g., at 1.385 GHz). Normal OOB emission limitations determined by the applicable OOB emission mask at 1 percent away from the center bandwidth (e.g., 1400 vs. 1385 MHz) are only slightly below −40 dBc. Using this value, signals within the adjacent EESS band arising from radars within the radiometer antenna footprint can easily exceed the maximum allowed emission level (set at about −140 dBm; see Appendix D).43

While few space-based L-band observations have been obtained to date, airborne and ground-based sensors have provided evidence of RFI corruption at levels that prevent geophysical measurements. A recent summary of data measured within the 1400-1427 MHz protected band in April 2005 using the EMIRAD L-band radiometer of the Technical University of Denmark showed significant daily changes in the RFI environment. The percentage of EMIRAD ocean observations impacted by RFI were as low as 1 to 2 percent on most days, but reached 40 to 50 percent in some cases. Repeated occurrences of RFI using the Electronically-Scanned Thinned Array Radiometer (ESTAR) L-band airborne EESS hybrid synthetic-and-real aperture radiometer in the protected 1400-1427 MHz band have been noted in flights over the Eastern Shore region of Virginia in 1999 and over Oklahoma City, Oklahoma, in 1997.44 These observations have shown clear instances of RFI (Figures 2.13 and 2.14).

A key concern at L-band is the possible influence of long-range air surveillance radar systems in nearby bands. Appendix D presents estimates for the RFI impact on future high-quality soil moisture measurements made by a space-based L-band

42

The stability figure of 0.05 K cited here is a conservative estimate of what is needed to achieve 0.2 psu based on cold water temperatures. D.M. Levine, “Aquarius: An Instrument to Monitor Sea Surface Salinity from Space,” IEEE Transactions on Geoscience and Remote Sensing, 45(7): 2040-2050 (July 2007), proposes a somewhat higher stability figure of 0.13 K based on measurements averaged over a 7 day window.

43

N. Skou, S. Misra, S. Sobjaerg, J. Balling, and S. Kristensen, “RFI as Experienced During Preparations for the SMOS Mission,” Proceedings of 2008 URSI General Assembly, Chicago, Ill., August 9-16, 2008.

44

D. Le Vine, “ESTAR Experience with RFI at L-Band and Implications for Future Passive Microwave Remote Sensing from Space,” Proceedings of the 2002 International Geoscience and Remote Sensing Symposium. (IGARSS), Toronto, Ontario, Canada, 2002, pp. 847-849; D. Le Vine and M. Haken, “RFI at L-Band in Synthetic Aperture Radiometers,” Proceedings of the 2003 International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 2003, pp. 1742-1744.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.13 An example of interference to an airborne EESS radiometer system operating at 1413 MHz from air-traffic radar operating in adjacent segments of spectrum that is possibly due to a combination of spurious emissions from the radar and limitations of adjacent signal rejection in the EESS radiometer. (A) Image from the Electronically-Scanned Thinned Array Radiometer (ESTAR) showing the effects of radio frequency interference (RFI) at 1413 MHz in the vicinity of Richmond, Virginia. The small vertical stripes are artifacts in the image due to strong RFI. (B) The signal is the output of the total power channel. These data were recorded at the location of the arrow in part (A). SOURCE: D. Le Vine, “ESTAR Experience with RFI at L-Band and Implications for Future Passive Microwave Remote Sensing from Space,” in IEEE Int. Geosci. and Remote Sens. Symp. Proc. (IGARSS), Toronto, Ontario, Canada, 2002, pp. 847-849, Figures 1 and 2. © 2002 IEEE.

FIGURE 2.13 An example of interference to an airborne EESS radiometer system operating at 1413 MHz from air-traffic radar operating in adjacent segments of spectrum that is possibly due to a combination of spurious emissions from the radar and limitations of adjacent signal rejection in the EESS radiometer. (A) Image from the Electronically-Scanned Thinned Array Radiometer (ESTAR) showing the effects of radio frequency interference (RFI) at 1413 MHz in the vicinity of Richmond, Virginia. The small vertical stripes are artifacts in the image due to strong RFI. (B) The signal is the output of the total power channel. These data were recorded at the location of the arrow in part (A). SOURCE: D. Le Vine, “ESTAR Experience with RFI at L-Band and Implications for Future Passive Microwave Remote Sensing from Space,” in IEEE Int. Geosci. and Remote Sens. Symp. Proc. (IGARSS), Toronto, Ontario, Canada, 2002, pp. 847-849, Figures 1 and 2. © 2002 IEEE.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.14 (A) Electronically-Scanned Thinned Array Radiometer (ESTAR) image at 1413 MHz from the Southern Great Plains experiment (SGP97). The vertical lines west of Oklahoma City are distortions due to radio frequency interference (RFI). (B) Example of RFI in the vicinity of Oklahoma City during SGP97. The signal represents total power and was recorded west of the arrow in part (A). SOURCE: D. Le Vine, “ESTAR Experience with RFI at L-Band and Implications for Future Passive Microwave Remote Sensing from Space,” in IEEE Int. Geosci. and Remote Sens. Symp. Proc. (IGARSS), Toronto, Ontario, Canada, 2002, pp. 847-849, Figures 3 and 4. © 2002 IEEE.

FIGURE 2.14 (A) Electronically-Scanned Thinned Array Radiometer (ESTAR) image at 1413 MHz from the Southern Great Plains experiment (SGP97). The vertical lines west of Oklahoma City are distortions due to radio frequency interference (RFI). (B) Example of RFI in the vicinity of Oklahoma City during SGP97. The signal represents total power and was recorded west of the arrow in part (A). SOURCE: D. Le Vine, “ESTAR Experience with RFI at L-Band and Implications for Future Passive Microwave Remote Sensing from Space,” in IEEE Int. Geosci. and Remote Sens. Symp. Proc. (IGARSS), Toronto, Ontario, Canada, 2002, pp. 847-849, Figures 3 and 4. © 2002 IEEE.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

radiometer, assuming various spurious emission levels at the EESS radiometer at 1413 MHz (the center of this EESS frequency allocation).45 The results indicate that over the United States where the density of radars is high, RFI would be a significant problem. Synthetic Aperture Interferometric Radiometers (SAIRs) have a wide field of view that, relative to real aperture antennas, increases their vulnerability to strong interference from outside the synthesized antenna beam. Such persistent RFI is a cause for concern for planned space-based EESS systems—for example, the European Space Agency’s SMOS sensor.

In both Figure 2.13 and Figure 2.14, it is unclear if the observed RFI was dominated by spurious emissions that fell within the EESS band or by limitations of the EESS passband filtering of emissions in adjacent channels. Regardless, these data demonstrate the need for the mitigation of interference and/or the regulation of OOB emissions radiated in adjacent bands, particularly in L-band. Since the rejection of high-power radar signals in adjacent spectrum is critical to EESS, high-performance front-end filters and other RFI mitigation schemes are essential and have been developed by the EESS community. However, the implementation of filtering schemes, if they are able to suppress RFI to manageable levels, also increases the EESS measurement uncertainty, reduces system sensitivity, increases EESS system cost, and impacts the geophysical data availability. Accordingly, there are practical limitations to minimizing band separation between EESS and active services that need to be considered in developing spectrum usage policy. In addition, in order to design effective RFI mitigation for EESS or prescribe equitable spectrum policy, the interfering signal parameters need to be precisely known. However, only limited information about interfering signals is currently available.

X-Band (10.6-10.7 GHz)

Passive microwave observations at X-band are critical for measurements of sea surface winds (useful for weather prediction and storm tracking) and precipitation (useful for climate and weather monitoring). They are also important for the correction of the effects of land cover on lower frequency (e.g., 1.4 GHz) measurements of soil moisture (useful for climate and weather forecasting). Within X-band, only the sub-band from 10.68-10.70 GHz is protected in the United States and globally for EESS by the ITU, although the wider (and more useful) 10.6-10.7 GHz sub-band has a shared primary allocation within the United States and globally. In addition, observations are also often made including the adjacent sub-band 10.7-10.8 GHz, or including even wider sub-bands on an as-available basis with active services. An example of the use of a wider total band is the Naval Research Laboratory’s WindSat sensor, which uses 10.55-10.85 GHz.

45

Ibid.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

Currently, X-band passive microwave imagery over North America appears to be free of obvious RFI from anthropogenic emissions, as illustrated by the example in Figure 2.15 from AMSR-E. The EESS measurements in this band required the use of the full allocated bandwidth of 100 MHz (10.6-10.7 GHz). It is important to note that all but the top 20 MHz of the EESS allocated band is shared with the Fixed Service (FS; point-to-point transmissions, such as radio relay towers); thus, based on Figure 2.15, it appears that U.S. frequency assignments have avoided the 10.6-10.68 GHz segment, which has been beneficial to EESS. However, as the need for spectrum for active services continues to expand, there is concern that significant usage of the 10.6-10.68 GHz band (currently shared with FS) could lead to a scenario at X-band that would resemble the worsening RFI environment at C-band observed between 1987 and 2003 (depicted in Figure 2.23 later in this chapter). A comparable degradation at X-band would be highly detrimental to EESS measurements and their associated data products. Similar concerns also exist at K-band (18.6-18.8 GHz), wherein EESS measurements have begun to display occasional RFI, as can be observed in WindSat imagery (see Figures 2.19 and 2.20 later in this chapter).

FIGURE 2.15 Brightness temperature as measured by the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) at 10.6 GHz with horizontal polarization over the United States. This observation appears to be free from interference. (L. Li, E. Njoku, E. Im, P. Chang, and K. St. German, “Frequency Interference over the U.S. in Aqua AMSR-E Data,” IEEE Transactions on Geoscience and Remote Sensing, 42(2): 380-390 (February 2004), from Figure 1.) AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

FIGURE 2.15 Brightness temperature as measured by the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) at 10.6 GHz with horizontal polarization over the United States. This observation appears to be free from interference. (L. Li, E. Njoku, E. Im, P. Chang, and K. St. German, “Frequency Interference over the U.S. in Aqua AMSR-E Data,” IEEE Transactions on Geoscience and Remote Sensing, 42(2): 380-390 (February 2004), from Figure 1.) AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

RFI in global 10.7 GHz brightness-temperature measurements was first detected by the TMI radiometer in 1997 during observations over both urban and remote locations of Japan. Subsequently, AMSR-E, launched in May 2002, showed substantial RFI in several European locations that were not observable by TMI due to its near-equatorial orbit (Figures 2.16 and 2.17). Currently, about 2 percent of the land area of Europe is unavailable to AMSR-E for measurements at 10.7 GHz, and an unknown, larger fraction may be adversely affected below the threshold of obvious detectability. However, the looming problem of RFI at X-band is not confined to land areas. Data at 10.7 GHz, such as those provided by WindSat and AMSR-E for SST, ocean wind, and maritime precipitation measurements, often experience substantial RFI from geostationary transmitters operating immediately adjacent to the upper edge of the 10.7 GHz EESS band segment. This maritime RFI is caused by downward-propagating geosynchronous broadcast signals reflecting from the ocean surface into the antenna beam of the EESS sensor. The RFI results in areas of the Mediterranean Sea, the eastern Atlantic Ocean north of the equator, and

FIGURE 2.16 Passive microwave imagery from the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) on the NASA Earth Observing System Aqua at 10.7 GHz over Europe. Strong emissions over the United Kingdom and portions of Italy are seen as saturated brightness temperatures (black spots). These areas, and nearby yellow and red areas in this example, cannot be used for the retrieval of geophysical parameters such as soil moisture, precipitation, and cloud water. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

FIGURE 2.16 Passive microwave imagery from the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) on the NASA Earth Observing System Aqua at 10.7 GHz over Europe. Strong emissions over the United Kingdom and portions of Italy are seen as saturated brightness temperatures (black spots). These areas, and nearby yellow and red areas in this example, cannot be used for the retrieval of geophysical parameters such as soil moisture, precipitation, and cloud water. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.17 Expanded region of Europe shown by the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) brightness temperatures at 10.65 GHz indicating the dependence of radio frequency interference (RFI) on political boundaries. RFI can be seen in England, Italy, and Belarus, whereas other countries appear to show none. These instances show the critical role of informed frequency managers and assigners within their respective jurisdictions for limiting impact between services of shared spectrum segments. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

FIGURE 2.17 Expanded region of Europe shown by the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) brightness temperatures at 10.65 GHz indicating the dependence of radio frequency interference (RFI) on political boundaries. RFI can be seen in England, Italy, and Belarus, whereas other countries appear to show none. These instances show the critical role of informed frequency managers and assigners within their respective jurisdictions for limiting impact between services of shared spectrum segments. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

the western Atlantic off the coast of Brazil being unavailable for sea surface wind, temperature, and heavy rain measurements, as shown in Figure 2.18.46 Southerly views of upwelling microwave brightness temperatures are typically measured by polar-orbiting EESS satellites in the descending phases of their orbits, so such RFI is typically observed in half of all such data over the Mediterranean Sea. The problem also manifests itself as RFI-corrupted calibration views of what should otherwise be cold space during portions of the WindSat orbit.

Analysis of the WindSat polarimetric channels has shown that significant RFI is occurring within the sub-band 10.55-10.85 GHz. Based on earlier measurements

46

Hotbird 4 channels 110 (10.71918 GHz), 111 (10.72713 GHz), and 112 (10.75754 GHz) are likely candidates.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.18 Example of radio frequency interference (RFI; areas in green and yellow) occurring at X-band from oceanic reflections of geosynchronous broadcasts in bands adjacent to those observed by the Advanced Microwave Scanning Radiometer-Earth (AMSR-E). In this example AMSR-E is operating in the EESS band 10.6-10.7 GHz and is experiencing perturbations higher than 40 K in measured brightness temperature during its descending phase. This level of RFI is far greater than approximately 0.2 K, the minimum level of perturbation that degrades environmental models that use sea surface temperature data derived from AMSR-E. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

FIGURE 2.18 Example of radio frequency interference (RFI; areas in green and yellow) occurring at X-band from oceanic reflections of geosynchronous broadcasts in bands adjacent to those observed by the Advanced Microwave Scanning Radiometer-Earth (AMSR-E). In this example AMSR-E is operating in the EESS band 10.6-10.7 GHz and is experiencing perturbations higher than 40 K in measured brightness temperature during its descending phase. This level of RFI is far greater than approximately 0.2 K, the minimum level of perturbation that degrades environmental models that use sea surface temperature data derived from AMSR-E. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

using SMMR compared with recent measurements using WindSat, strong X-band RFI in Europe and Japan appears to be increasing over time. The X-band channels of the airborne Polarimetric Scanning Radiometer (PSR) have also detected RFI over the United States, although to a lesser degree than at C-band. The high-resolution PSR mapping capabilities permit pinpointing the location of sources of RFI, but only within limited data sets.


Finding: Whereas most frequency regulations for active services are defined on local or regional bases, passive EESS observations are global by nature. As a result,

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

a high level of international cooperation is required to maintain and enforce passive allocations.

K-Band (18.6-18.8 GHz)

The 18.6-18.8 GHz band is a critical resource for EESS that supports many operational environmental products, such as snow cover, sea surface wind speed, and soil moisture measurements. Snow water equivalent measurements, which are increasingly important for water management, specifically require the use of observations at a frequency near this band. Thus there is a global primary allocation for EESS at 18 GHz.

Evidence of RFI has been found in 18 GHz WindSat space-based observations, as shown in Figure 2.19 for the Paris and London metropolitan areas. Sparse but recurring RFI at 18 GHz has been observed on nearly every continent, as shown in Figure 2.20. As a result, scientists are concerned that increasing use of the spectrum near 18 GHz will increase RFI for WindSat and other EESS radiometers.

K-Band (23.6-24.0 GHz)

Space- and airborne radiometric observations of the weak water vapor resonance near 22.235 GHz are at risk owing to recent rule changes that allow automo-

FIGURE 2.19 Brightness-temperature data from the WindSat 18.7 ±45º channels (both left) and 18.7 R/LCP channels (both right) showing strong radio frequency interference over Paris and London. Courtesy of the U.S. Naval Research Laboratory.

FIGURE 2.19 Brightness-temperature data from the WindSat 18.7 ±45º channels (both left) and 18.7 R/LCP channels (both right) showing strong radio frequency interference over Paris and London. Courtesy of the U.S. Naval Research Laboratory.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.20 Cumulative analysis over a 5 year period of WindSat 18.6-18.8 GHz horizontally polarized data indicates sparse occurrences of strong radio frequency interference impacting 18 GHz brightness-temperature measurements over land: (A) North America, (B) Europe, (C) Central Africa, and (D) Southeast Asia/Oceania. Courtesy of the U.S. Naval Research Laboratory.

FIGURE 2.20 Cumulative analysis over a 5 year period of WindSat 18.6-18.8 GHz horizontally polarized data indicates sparse occurrences of strong radio frequency interference impacting 18 GHz brightness-temperature measurements over land: (A) North America, (B) Europe, (C) Central Africa, and (D) Southeast Asia/Oceania. Courtesy of the U.S. Naval Research Laboratory.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

tive anticollision radar to operate within the bands from 22 to 27 GHz, despite the allocation of the 23.6-24.0 GHz band to the passive services by both the Federal Communications Commission in the United States and ITU globally. Observations at 23.6-24.0 GHz and nearby bands provide the primary data used to estimate atmospheric integrated water vapor, an EDR that drives important atmospheric modes related to severe weather within NWP models (see §2.1).

For a typical five-channel, 22 GHz ground-based upward-looking water vapor profiling radiometer, 1 K of RFI in a channel near the center of the water vapor line can induce a 10 percent error in retrieved water vapor abundance in the lower and mid-level troposphere. This error is comparable to the current performance of such a current technology microwave profiler, and the tolerable RFI level is therefore about 0.1 K. The tolerable RFI level near 31 GHz for total integrated (as opposed to profiles of) water vapor/cloud liquid measurements within the midlatitude coastal environment is about 0.6 K on humid days. Higher RFI levels of up to 1 K can be tolerated for observations of integrated liquid water in clouds and rain where the atmospheric signals are higher.

To date, only little evidence of the impact of RFI at 23.6-24.0 GHz has been documented, partly because automobile radars are still quite new and not yet widespread. In spite of the nascent state of automotive radar, ground-based measurements within 23.6-24.0 GHz have shown the presence of such transmissions. This topic is discussed in further detail in the subsection below entitled “Potential Future Radio Frequency Interference and Its Impact on EESS Observations.”


Finding: The rules for out-of-band and spurious emissions in the primary allocated Earth Exploration-Satellite Service (EESS) bands (e.g., 1400-1427 MHz) do not provide adequate interference protection for EESS purposes.


The rules that pertain to the finding above are given in Appendix D.

Unprotected Band
C-Band (6.2-7.5 GHz)

Current space-based observations within C-band, specifically near 6.8 GHz, are used to measure global sea surface temperature and soil moisture. In addition, airborne observations in C-band are used for high-resolution SM mapping for research purposes. Data from flood-prone areas in Texas in 2007 have suggested that airborne mapping at C-band may also be useful for flood forecasting in disaster management. Because there is no EESS allocation within C-band and this portion of the spectrum is heavily used by the Fixed Service, brightness-temperature measurements at C-band over land are currently considered observations of opportunity. The observed area can contain many sources of RFI that require mitigation

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

in order for the data to be useful. Simulated data based on current active spectrum usage have shown that frequency diversity can facilitate effective RFI mitigation in this spectral region. The careful design of receivers and retrieval algorithms can also help facilitate mitigation, but mitigation techniques applied to data from current space-based radiometers are limited in their effectiveness.

The NASA AMSR-E and the WindSat spaceborne radiometers have shown clear evidence of active use impacting C-band EESS measurements (see Figure 2.21) over large portions of global land area. However, the SMMR C-band channel that operated from June 1978 to August 1987 showed little to no evidence of transmissions over North America (Figure 2.22) in this EESS band of opportunity. While the precise bands for these three instruments differ slightly, it has also been qualitatively observed in repeated airborne observations over central Oklahoma in 1999 and 2006 using the same instrument (the PSR/C airborne scanning radiometer) that obvious instances of RFI have tended to increase over time. The major increase in the active usage of C-band spectrum occurring from 1987 to 2003 has reduced the ability to perform EESS observations of opportunity over land. C-band measurements from AMSR-E and WindSat currently provide critical SST products over ocean sufficiently far from the coasts. Ongoing improvements in maritime product accuracies, particularly in near-shore sea surface temperature measurements improved to 0.1-0.2 K accuracy, may thus become limited in the near future by RFI, even far out at sea.

In the examples given in Figures 2.21 and 2.22, AMSR-E imagery illustrates the prevalence and growth of RFI to EESS at C-band. Shortly after the launch of AMSR-E aboard NASA’s Aqua satellite in May 2002, it was discovered that the 6.9 GHz passes over land (both ascending and descending and in both V and H polarizations) exhibited anomalous brightness-temperature (TB) “hot-spots” exceeding 310-320 K that were clearly unrelated to natural surface emission. TB values also appeared elevated by several degrees over large areas relative to expected values. The RFI not only biased the soil moisture retrievals toward dryness, but caused the multiple-channel iterative algorithm used at launch to fail frequently. Several orbits of data were analyzed, focusing on the United States where the problem appeared to be worst, to see if a simple brightness-temperature index could be devised to detect RFI so that contaminated observations could be ignored. It was found that a simple RFI index could identify the major RFI locations, but low-level RFI covered very large areas and could not be unambiguously distinguished from natural geophysical signals. The AMSR-E RFI was later analyzed globally using a more sophisticated set of indices and statistics.47 RFI was found at 6.9 GHz over

47

E.G. Njoku, P. Ashcroft, T.K. Chan and L. Li, “Global Survey and Statistics of Radio-Frequency Interference in AMSR-E Land Observations,” IEEE Transactions on Geoscience and Remote Sensing, 43(5): 938-947 (2005).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.21 (Top panel) An example of interference to EESS observations of opportunity at 6.925 GHz primarily from in-band signals arriving via the sidelobes of the main antenna beam of Fixed Service transmitters in legal operation. Passive microwave imagery at 6.9 GHz from AMSR-E on the NASA EOS Aqua platform. The black spots represent high levels of anthropogenic emission that saturate the AMSR-E radiometer primarily over regions of California and Arizona. The red spots over most of the remaining areas of the United States represent contaminated brightness-temperature measurements. (Bottom panel) Radio frequency interference (RFI) is displayed as the perturbation from a zero mean (natural emission) level. Perturbations of up to 50 K are common across the United States, affecting more than 50 percent of the total land area with RFI greater than 5 K. The pervasive nature of the interference makes impossible the retrieval of soil moisture using AMSR-E 6.9 GHz data. SOURCE (top and bottom): L. Li, E. Njoku, E. Im, P. Chang, and K. St. German, “Frequency Interference over the U.S. in Aqua AMSR-E Data,” IEEE Transactions on Geoscience and Remote Sensing, 42(2): 380-390 (2004), from Figure 8. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

FIGURE 2.21 (Top panel) An example of interference to EESS observations of opportunity at 6.925 GHz primarily from in-band signals arriving via the sidelobes of the main antenna beam of Fixed Service transmitters in legal operation. Passive microwave imagery at 6.9 GHz from AMSR-E on the NASA EOS Aqua platform. The black spots represent high levels of anthropogenic emission that saturate the AMSR-E radiometer primarily over regions of California and Arizona. The red spots over most of the remaining areas of the United States represent contaminated brightness-temperature measurements. (Bottom panel) Radio frequency interference (RFI) is displayed as the perturbation from a zero mean (natural emission) level. Perturbations of up to 50 K are common across the United States, affecting more than 50 percent of the total land area with RFI greater than 5 K. The pervasive nature of the interference makes impossible the retrieval of soil moisture using AMSR-E 6.9 GHz data. SOURCE (top and bottom): L. Li, E. Njoku, E. Im, P. Chang, and K. St. German, “Frequency Interference over the U.S. in Aqua AMSR-E Data,” IEEE Transactions on Geoscience and Remote Sensing, 42(2): 380-390 (2004), from Figure 8. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.22 An example of interference to EESS observations of opportunity at 6.6 GHz. Passive microwave imagery at 6.6 GHz from the Scanning Multi-channel Microwave Radiometer (SMMR) from (A) 1979 and (B) 1987, showing no noticeable brightness temperature from radio frequency interference (RFI). In contrast, passive microwave imagery from the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) on NASA Earth Observing System Aqua from 2003 (C) and 2004 (D) shows substantial RFI. The black spots represent high levels of anthropogenic emission that saturate the AMSR-E radiometer, primarily over regions of California and Arizona. The red spots over most of the remaining areas of the United States represent contaminated brightness-temperature measurements. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

FIGURE 2.22 An example of interference to EESS observations of opportunity at 6.6 GHz. Passive microwave imagery at 6.6 GHz from the Scanning Multi-channel Microwave Radiometer (SMMR) from (A) 1979 and (B) 1987, showing no noticeable brightness temperature from radio frequency interference (RFI). In contrast, passive microwave imagery from the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) on NASA Earth Observing System Aqua from 2003 (C) and 2004 (D) shows substantial RFI. The black spots represent high levels of anthropogenic emission that saturate the AMSR-E radiometer, primarily over regions of California and Arizona. The red spots over most of the remaining areas of the United States represent contaminated brightness-temperature measurements. AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

large parts of the Middle East, Asia, and Japan, and even sophisticated statistical procedures could not adequately distinguish RFI from the background of natural brightness variability, nor filter it out in post-processing of the data.48 Because the 6.9 GHz RFI was so prevalent and difficult to identify and mitigate over the United States, this instrument channel was subsequently ignored in the global AMSR-E algorithm used for the production processing and data archiving of SM data. Reliance was instead placed on the higher-frequency AMSR-E channels that are less sensitive to SM. Over those parts of Europe and Japan where the 10.7 GHz channels were also affected by RFI, no AMSR-E soil moisture retrievals at all were possible.

48

Ibid.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

On a research basis (separate from the global production algorithm), it is still possible to use the 6.9 GHz brightness data for soil moisture retrieval over significant RFI-free global areas such as most of Africa, South America, and Australia.

Extensive analysis of AMSR-E and WindSat data provides a clear picture and plausible explanation for RFI at C-band, but not in other parts of the spectrum. Other RFI surveys have been inconclusive, tied to a single location, and/or have not been able to provide much insight regarding the global status of potential RFI to EESS. The duty cycle, waveforms, emitter spatial distribution, transmitter power, and spectral utilization of the RFI need to be measured to effectively and optimally design RFI mitigation strategies into EESS radiometer systems and to further develop equitable spectrum usage policies. 49 In short, inadequate data on spectrum usage exist. The Federal Communications Commission’s (FCC’s) 2002 Spectrum Policy Task Force came to this same conclusion:


More information, however, is needed in order to quantify and characterize spectrum usage more accurately so that the Commission can adopt spectrum policies that take advantage of these spectrum white spaces. Currently, no federal agency or other organization systematically measures temporal spectrum use.50


Finding: Better utilization of the spectrum and reduced radio frequency interference for scientific as well as commercial applications are possible with better knowledge of actual spectrum usage.


Progress toward the goal of improved spectrum usage could be made by gathering more information through improved and continuous spectral monitoring. Such monitoring would benefit both the scientific community and commercial interests by allowing more efficient use of the spectrum for communications.

Interference mitigation at C-band has been demonstrated on a limited basis and for particularly strong (and therefore relatively obvious) interference in airborne images of thermal emission at C-band.51 The radiometer and algorithm were designed to detect spectral variations that were not of natural origin by fitting the spectrum to a standard model, then rejecting channels that compromised the fit

49

J.R. Piepmeier, “Radio Frequency Survey of the 21-cm Wavelength (1.4 GHz) Allocation for Passive Microwave Observing,” in Proceedings of the 2003 International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France, 2003, pp. 1739-1741; and presentation by Dennis Roberson, Illinois Institute of Technology, to the committee on September 29, 2007, in Irvine, California.

50

Federal Communications Commission, Report of the Spectrum Policy Task Force, November 2002, p. 10.

51

A.J. Gasiewski, M. Klein, A.Yevgrafov, and V. Leuskiy, “Interference Mitigation in Passive Microwave Radiometry,” Proceedings of the 2002 International Geoscience and Remote Sensing Symposium (IGARSS), Toronto, Ontario, Canada, June 24-28, 2002.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
FIGURE 2.23 Polarimetric Scanning Radiometer C-band maps from a swath segment observed during SP99 on July 14, 1999, over central Oklahoma: (A) raw calibrated brightness maps for front and back looks for four subbands and (B) interference-corrected maps using a spectral sub-band algorithm (A.J. Gasiewski, M. Klein, A.Yevgrafov, and V. Leuskiy, “Interference Mitigation in Passive Microwave Radiometry,” Proceedings of the 2002 International Geoscience and Remote Sensing Symposium [IGARSS], Toronto, Ontario, Canada, June 24-28, 2002). AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

FIGURE 2.23 Polarimetric Scanning Radiometer C-band maps from a swath segment observed during SP99 on July 14, 1999, over central Oklahoma: (A) raw calibrated brightness maps for front and back looks for four subbands and (B) interference-corrected maps using a spectral sub-band algorithm (A.J. Gasiewski, M. Klein, A.Yevgrafov, and V. Leuskiy, “Interference Mitigation in Passive Microwave Radiometry,” Proceedings of the 2002 International Geoscience and Remote Sensing Symposium [IGARSS], Toronto, Ontario, Canada, June 24-28, 2002). AMSR-E data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the AMSR-E Science Team. Data are available at www.remss.com.

to this natural model. The techniques have proven effective at mitigating large-amplitude interference (Figure 2.23). However, they provide no guarantee that interference of amplitudes on the order of the system noise level can be detected and mitigated.


Finding: There is currently inadequate protected spectrum in C-band and X-band for operational passive microwave observations of sea surface temperature, soil moisture, and ocean surface wind speed and direction.


Finding: While unilateral radio frequency interference mitigation techniques are a potentially valuable means of facilitating spectrum sharing, they are not a substitute for primary allocated passive spectrum and the enforcement of regulations.


Finding: Important scientific inquiry and applications enabled by the EESS are significantly impeded or precluded by radio frequency interference (RFI). Such RFI

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

has reduced the societal and scientific return of EESS observatories and necessitates costly interference mitigation, which is often insufficient to prevent RFI damage.

Potential Future Radio Frequency Interference and Its Impact on EESS Observations

Ultrawideband Devices and Anticollision Radar (1-24 GHz)

A major concern for future EESS observations is the proliferation of ultrawideband (UWB) devices that radiate over wide bandwidths at low power, typically in the 2-10 GHz and 22-27 GHz ranges. Automotive collision-avoidance radars that employ the entire 22-27 GHz range have recently been included on new vehicles and are becoming widespread. In particular, the FCC’s 2002 approval of the use of UWB devices in the 3-10.6 GHz band and of anticollision radar operation as Part 15 devices near 24 GHz has alarmed the EESS community.52,53 These sources produce broadband signals that resemble thermal noise, making them difficult to distinguish from natural emissions. The potential for large-scale market penetration of such devices further exacerbates the problem, particularly if they are permitted to radiate across protected frequency bands (particularly in the protected 1.400-1.427 GHz and 23.6-24.0 GHz bands). Emissions from UWB sources in these protected spectral bands present a serious problem, and action will need to be taken to prevent such emissions and limit the numbers of such devices.

Scenarios involving RFI to EESS systems from multiple low-level emitters within the passband and footprint of EESS measurements must be analyzed on a cumulative basis as outlined in Appendix C. In these scenarios the maximum output power of each transmitter and their number per square kilometer are critical factors in EESS compatibility studies. Examples include UWB at 6 GHz and point-to-point transmitters near 57 GHz (see V-band scenarios later in this section).

A study analyzing the impact of losing the protected 23.6-24.0 GHz channel suggests that although the ideal level of RFI in the band is zero, 0.03 K might be established as its maximum permissible value, which is equivalent to −126.84 dBm of RFI within a 500 MHz band.54 More serious is the fact that unless the RFI level is 10 K or more, the NWP applications cannot reliably flag the data as

52

See the Glossary in this report for a definition of a Part 15 device.

53

FCC Press Release, “New Public Safety Applications and Broadband Internet Access Among Uses Envisioned by FCC Authorization of Ultra-Wideband Technology,” February 12, 2002, available at http://www.fcc.gov/Bureaus/Engineering_Technology/News_Releases/2002/nret0203.html; accessed January 7, 2010.

54

S. English, Assessment of the Requirement for 23.6-24.0 GHz Observations for Weather Forecasting, Forecasting Research Technical Report No. 440, Exeter, U.K.: Met Office, 2006.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

erroneous, thereby degrading forecasts within and downstream of any regions where intermediate-level RFI is present. Such intermediate-level interference is difficult to detect with any confidence except in locations where its effects become extreme. Since automobiles are nearly ubiquitous over land and especially within populated regions where forecasts have the greatest economic value, the problem is endemic to users who rely the most on forecast data. This final point is sufficient to support the exclusion of all intended emissions near the protected EESS band, consistent with the intent of the original regulation.

In addition, there is great concern for the future of EESS measurements of opportunity at C-band. This band covers much of the spectral region commonly used by EESS for measurements of sea surface temperature and soil moisture on an as-available basis. These measurements are critical for accurate weather forecasting, severe weather prediction, and drought prediction, among other applications. The wide proliferation of low-level UWB devices within C-band is a significant concern of the EESS operational and scientific communities (see Appendix C for the density of interferers analysis). Since RFI in EESS operations is cumulative, there is no protection from the impact of a high density of low-level emitters resulting from the strong market penetration of unlicensed products. In these scenarios, all mitigation techniques for AMSR-E and WindSat data would be rendered useless, and important future C-band observations would not be possible without mandatory bilateral mitigation strategies (as described in Chapter 4 of this report).

It is instructive to contrast the scenarios at C-band for EESS, where a large number of emitters contribute to RFI within a single pixel of AMSR-E and WindSat data (especially over populated areas), with the RFI scenario outlined in Appendix D. In the latter case, the impact of RFI on EESS measurements from one or more radars is considered. For cases where only a few high-level emitters in adjacent bands are present (for example, in L-band radar RFI), the measured brightness temperatures are increased by spurious and/or OOB emissions. Such emissions contribute directly to the maximum allowed in-band emissions for EESS; however, the RFI is the result of a single emitter rather than the cumulative effect of many in-band emitters. Although current regulations—if enforced—could preclude the effects of cumulative in-band emissions on EESS systems operating in allocated bands (e.g. 1.400-1.427 GHz and 10.6-10.7 GHz), they are largely ineffective in their present form in limiting OOB and spurious emissions. In considering these scenarios, it should be noted that the present specifications on OOB and spurious emissions were established decades ago, before heavy use was made of bands adjacent to where critical EESS measurements are now conducted and prior to major advances in microwave signal processing and filtering technology. Considerations of new technologies must be made in reassessing the effects of and in regulating OOB and spurious emissions.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×
Ground-Based Atmospheric Sounding (23.8 GHz, 31.5 GHz, 50-60 GHz, 89 GHz, 183 GHz)

Ground-based microwave radiometers are being used increasingly for the temperature, humidity, and cloud liquid profiles in the lower troposphere for both nowcasting and forecasting. Thus, they are being incorporated into weather observing networks as a replacement and augmentation of the global radiosonde network. It is expected that RMS instrument errors in oxygen-band temperature profiling radiometer measurements will be as low as 0.2 K (or lower) in the future and that the nominal tolerable RFI level for these systems will be 0.02 K.

For a typical five-channel 22 GHz to 30 GHz upward-looking water vapor profiling radiometer, 1 K of RFI in a channel near the center of the 22.235 GHz water vapor line can induce a 10 percent error in retrieved water vapor abundance in the lower and mid-level troposphere. This error is comparable to the current performance of such a profiler, and the tolerable RFI level is therefore about 0.1 K. It is expected, however, that the absolute accuracy of ground-based systems will increase as the models and instruments improve, possibly attaining an absolute accuracy of 0.2 mm of precipitable water vapor (PWV). Since each millimeter of PWV produces approximately 1.4 K of signal at 23.8 GHz, RFI must be less than 0.03 K, assuming a maximum tolerable interference of 10 percent of the sensitivity of the instrument. Higher RFI levels of up to 1 K can be tolerated for observations of integrated liquid water in clouds and rain.

Wideband anticollision radars are being licensed and produced in the 22-26 GHz region of the 22-30 GHz wave band, which spans the radio astronomy reserved quiet band at 23.6-24 GHz. These active sources are difficult to discriminate from thermal noise, even with elegant and costly detection methods, and are expected to be an ever-increasing problem to ground-based water vapor (humidity) profiling.

Ground-based radiometers receiving around 89 GHz are important in that they are used to discriminate between cloud liquid water and ice. The transitions between the ice-liquid-vapor phases of water drive the thermodynamic energy transport cycles of the atmosphere and are therefore important for monitoring and predicting weather. Knowledge of these three phases is also critical to understanding planetary albedo and planetary radiative transfer, and therefore climate change and global warming, as well. There is a protected primary radio astronomy band at 86-92 GHz, but as mentioned elsewhere in this report, it is difficult to enforce against intrusions by spurious and out-of-band transmissions. Active technologies up to 110 GHz are being developed, in part due to military interest in and funding for active radars around 94 GHz. The growing availability of these high-frequency technologies in this wave band will undoubtedly result in problems from RFI for EESS observations.

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

The strong water vapor line centered at 183 GHz is observed for water vapor profiling in dry climates such as high-altitude astronomical observatories and arctic and desert regions. Because of the level of technology required at these high frequencies, little interference in this region is foreseen in the near future.

Other Concerns
SST Measurements at C-Band and X-Band (5-10 GHz)

Of particular future concern is RFI affecting continuous all-weather microwave sea surface temperature measurements in littoral regions that are critical for severe storm forecasting and weather and climate studies (see Figure 2.8). These measurements rely principally on observations at 5-10 GHz, which are generally sensitive to surface temperature changes while being insensitive to clouds. Active services using spectrum adjacent to and within the EESS allocation at 10.6-10.7 GHz can make SST measurements difficult or impossible at this band. UWB devices that radiate in the 2-10 GHz range could be particularly problematic in the future. It is also important to note that 10.6-10.68 GHz is shared with the Fixed Service, and in several areas worldwide, significant interference has been measured and continues to increase. Several EESS satellites have improved on TMI’s 10 GHz measurements of SST by including observations of C-band microwave brightness temperatures, typically near 6.8 GHz. These measurements specifically improve the accuracy of all-weather SST measurements in cold regions and are less prone to being affected by heavy clouds and precipitation. However, uncontaminated measurements of environmental parameters near 6 GHz are becoming more difficult to obtain owing to the high usage of the C-band spectrum and the lack of any EESS allocation adequate to support SST measurements. While the problem of contamination of 5-10 GHz SST measurements exists over all of the global oceans, it is particularly an issue in littoral regions where severe weather is economically important and population density (including ship traffic) is high (see also §2.1).

V-Band (50-64 GHz)

A number of currently operating space-based instruments use the atmospheric oxygen absorption band (50-64 GHz) to estimate profiles of atmospheric temperature and moisture. These measurements are central to NWP, severe weather forecasting, and climate analysis. International frequency allocations provide a shared “primary” status to EESS in the 57.0-59.3 GHz range, and these frequencies are currently used by several space-based radiometers, including the Advanced Microwave Sounding Unit and the Special Sensor Microwave Imager/Sounder. Both of these sensors operate on multiple satellites to provide full global coverage every few hours (see Table 2.2). AMSU sensors operating in the 50-59 GHz band may

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

be the single most important data source enabling useful global weather forecasts up to 7 days in advance.

In response to a growing interest in the active use of this part of the spectrum, EESS scientists have begun analyzing the potential for future interference to remote sensing measurements at V-band. The wide bandwidth available and small device sizes that can be manufactured make this potentially fertile ground for commercial interests.55 A recent FCC notice of public rule making (NPRM) requested an allowance for increased power emission levels for sources operating within 57-64 GHz, which includes the ITU-protected 57.0-59.3 GHz portion used for weather-related sensing by many satellites and weather forecasting services.56 Unfortunately, the FCC NPRM included no analysis of the potential impact of these increased power levels on essential EESS passive measurements from AMSU or related instruments, even though it is currently envisioned that wireless systems operating near 60 GHz will become ubiquitous consumer devices for applications such as local DVD broadcasts and personal networking. While atmospheric absorption limits the range of active users’ transmissions, attenuation from the surface to the top of the atmosphere is not complete (as shown in Figure 1.2). A sufficiently high spatial density of low-power emitters on the ground can affect spaceborne microwave observations. Members of the EESS passive community raised this issue in comments filed in response to the FCC’s NPRM, and the FCC’s decision is still forthcoming as of the time of this writing.57 The community is also interacting with IEEE standards organizations to determine the possible impact of such wireless systems on future EESS observations.58

It is clear that RFI degradation of EESS measurements and weather forecasting services appears to be likely if widespread unlicensed transmissions in these bands begin. Consideration should be given to limiting the strength and density of transmitters in this band (see Appendix C) in order to address the concerns of EESS. It may well be that no practical limit exists if such devices are sold as unlicensed

55

B. Bosco, “Emerging Commercial Applications Using the 60 GHz Band,” IEEE Wireless and Microwave Technology conference (WAMICON) 2006, proceedings; B. Razavi, “Gadgets Gab at 60 GHz,” IEEE Spectrum, February 2008.

56

In the Matter of Revision of the Commission’s Rules Regarding Operation in the 57-64 GHz Band, Notice of Proposed Rulemaking, 22 FCC Rcd 10505 (2007).

57

IEEE Geoscience and Remote Sensing Society, “Comments to the proposed revision of the Commission’s Rules Regarding Operation in the 57-64 GHz Band,” available at http://fjallfoss.fcc.gov/prod/ecfs/retrieve.cgi?native_or_pdf=pdf&id_document=6519741794; accessed June 9, 2009.

58

It is noted that while considerable resources are often available to be applied toward legal filings by active users of the spectrum, the nongovernmental scientific community has had little or no financial support for pursuing such legal matters. Virtually all responses from the nongovernmental EESS and RAS communities to NPRMs are the result of either voluntary efforts (in the case of university personnel) or are in direct reaction to threats to the viability of the passive services (in the case of industry personnel).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

and thus potentially used without limit. However, there is no apparent technical reason why the wider band 59.3-64 GHz could not alternatively satisfy essentially all commercial requirements for ubiquitous devices since such bandwidths in a single device far exceed the capacities of most home fiber and cable systems that offer hundreds of television channels and other services.

High Frequencies (>100 GHz)

In order to improve the understanding of the chemistry associated with stratospheric ozone depletion, it is necessary to observe the global distributions of a wide array of trace gases.59 Measurements are made by observing narrow spectral line emissions. The frequency requirements of those measurements are dictated by molecular quantum transitions of the gases under consideration. Trace gases of particular interest include ozone, chlorine, hydrogen, bromine, and water vapor. NASA’s Microwave Limb Sounder (MLS) and associated follow-on instruments have been designed for trace gas observations.60 The EOS version of MLS operates in five primary spectral bands near 118, 190, 240, 640, and 2500 GHz.61 The specific passbands and minimum detectable signals for MLS are listed in Table 2.3. RFI should be kept at or below one-tenth of the minimum detectable signals levels noted in the table. While no RFI has been reported to date, it is envisioned that the bands above 100 GHz may become commercially useful to the active services in the coming decades.

In the near term, the Submillimeter Infrared Radiometer Ice Cloud Experiment (SIRICE) mission is being designed to measure cloud ice water path using passive channels above 100 GHz. SIRICE is currently in pre-Phase A development at NASA. Design studies have identified three channels (including frequencies, bandwidths, and rms measurement errors) for SIRICE required to retrieve IWP with the necessary accuracy and precision. The spectral requirements are summarized in Table 2.4. RFI contamination of SIRICE observations should be at or below one-tenth of the NEΔT levels noted in the table if the scientific integrity of the IWP retrievals is to be maintained.

59

S. Solomon, “Stratospheric Ozone Depletion: A Review of Concepts and History,” Reviews of Geophysics, 37(3): 275–316 (1999).

60

J.W. Waters, W.G. Read, L. Froidevaux, and R.F Jarnot, “The UARS and EOS Microwave Limb Sounder (MLS) Experiments,” Journal of Atmospheric Science, 56: 194-217 (1999).

61

J.W. Waters et al., “The Earth Observing System Microwave Limb Sounder (EOS MLS) on the Aura Satellite,” IEEE Transactions on Geoscience and Remote Sensing, 44(5): 1075-1092 (2006).

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

TABLE 2.3 EOS Microwave Limb Sounder Instrument Spectral Coverage and Sensitivity for Measurement of Trace Gases in the Upper Atmosphere

Passband (GHz)

Minimum Detectable Signal (K)

115.3-122.0

0.1

177.2-206.2

0.03

221.4-240.5

0.1

606.7-657.5

0.1

2481.9-2506.0

0.1

SOURCE: J. Waters, R.E. Cofield, M.J. Filipiak, D.A. Flower, N.J. Livesey, G.L. Manney, H.C. Pumphrey, M.L. Santee, P.H. Siegel, and D.L. Wu, “An Overview of the EOS MLS Experiment,” NASA EOS MLS DRL 601 (part 1), ATBD-MLS-01, JPL D-15745/CL#04-2323, ver. 2.0, January 7, 2005.

TABLE 2.4 Submillimeter Infrared Radiometer Ice Cloud Experiment (SIRICE) Instrument Spectral Coverage and Sensitivity Requirements for Measurement of Ice Water Path

Center Frequency ± Double Sideband Offset (GHz)

Bandwidth (GHz)

NEΔT (K)

Polarization

183.31±1.5

1.4

0.7

Vertical

183.31±3.5

2.0

0.6

Vertical

183.31±7.0

3.0

0.5

Vertical

325.15±1.5

1.6

1.8

Vertical

325.15±3.5

2.4

1.4

Vertical

325.15±9.5

3.0

1.3

Vertical

448.00±1.4

1.2

2.3

Vertical

448.00±3.0

2.0

1.8

Vertical

448.00±7.2

3.0

1.5

Vertical

642.90±6.7

2.8

1.9

Vertical

642.90±6.7

2.8

1.9

Horizontal

874.40±4.5

6.0

1.9

Vertical

2.6
SUMMARY OF THE IMPORTANCE OF AND RISKS TO CONTINUED CONTRIBUTIONS OF THE EARTH EXPLORATION-SATELLITE SERVICE IN THE FUTURE

The Earth Exploration-Satellite Service (EESS) provides critical and unique measurements that support (1) day-to-day weather and other environmental operations, (2) climate research, and (3) model development and other scientific advances in Earth observation. EESS measurements are currently impacted by RFI at all key frequencies up to 19 GHz, and likely at 24 GHz and higher frequencies soon. There is also potential for significant future interference to EESS systems operating at 50-60 GHz. This interference occurs whether the band of concern is assigned to the passive services exclusively, shared with other services, or not assigned to EESS but

Suggested Citation:"2 The Earth Exploration-Satellite Service." National Research Council. 2010. Spectrum Management for Science in the 21st Century. Washington, DC: The National Academies Press. doi: 10.17226/12800.
×

has unique physical properties that demand observation when interference is absent. Unless these issues are addressed in a timely manner, the effectiveness and utility of EESS will likely be increasingly compromised, particularly as wireless services and unlicensed devices proliferate. Most problematic are future ubiquitous unlicensed ultrawideband consumer devices that can proliferate without limit.

Box 2.3 illustrates a sporadic record of achievement in appropriately allocating spectrum and/or coordinating technology development between EESS and competing active services. A technology advisory body, incorporating members from all relevant services, could help mitigate such failures. Such an entity would link EESS and other relevant active and passive communities in an early identification of issues and opportunities regarding competing spectral needs and shared standards development. Such a holistic body would supplement the more adversarial and segmented bodies that currently provide most such advice.

BOX 2.3

Illustrative Examples of Successes and Failures in Frequency Coordination That Affect the Earth Exploration-Satellite Service (EESS)

Successes

  • European and Japanese transition to 77 GHz band for automobile radar, avoiding 23-24 GHz.

  • The development of airborne sub-band-based radio frequency interference (RFI) mitigation methods that delete single strong interference signals, although not weak or diffuse interference.

  • The International Telecommunication Union trade-off of allocations to obtain stronger protection at more important bands at 50-57 GHz.

  • The migration of new instrument specifications toward protected bands (Advanced Technology Microwave Sounder, Special Sensor Microwave/Imager, Special Sensor Microwave/Imager Sounder, Conical Microwave Imager Sounder, and Microwave Imager/Sounder).

Failures

  • The lack of engagement between the auto radar community, Earth Exploration-Satellite Service (EESS), and regulators during the technology’s early development.

  • The lack of accepted remedies when unlicensed devices producing limited EESS interference multiply in numbers so as to collectively damage EESS and other services.

  • The lack of global exclusive EESS allocations at 18.7 and 10.65 GHz; critical bands experiencing RFI.

  • No allocation of a protected band at C-band.

  • The difficulty in effectively employing lower-frequency bands (e.g., 1400-1427 MHz) owing to RFI; apparent inadequate protection for EESS operation in the exclusively passive 1400-1427 MHz band.

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Spectrum Management for Science in the 21st Century Get This Book
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Radio observations of the cosmos are gathered by geoscientists using complex earth-orbiting satellites and ground-based equipment, and by radio astronomers using large ground-based radio telescopes. Signals from natural radio emissions are extremely weak, and the equipment used to measure them is becoming ever-more sophisticated and sensitive.

The radio spectrum is also being used by radiating, or "active," services, ranging from aircraft radars to rapidly expanding consumer services such as cellular telephones and wireless internet. These valuable active services transmit radio waves and thereby potentially interfere with the receive-only, or "passive," scientific services. Transmitters for the active services create an artificial "electronic fog" which can cause confusion, and, in severe cases, totally blinds the passive receivers.

Both the active and the passive services are increasing their use of the spectrum, and so the potential for interference, already strong, is also increasing. This book addresses the tension between the active services' demand for greater spectrum use and the passive users' need for quiet spectrum. The included recommendations provide a pathway for putting in place the regulatory mechanisms and associated supporting research activities necessary to meet the demands of both users.

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