The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies

The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) was developed and provided to the National Aeronautics and Space Administration's EOS Aqua satellite by the National Space Development Agency of Japan, as one of the indispensable instruments for Aqua's mission. AMSR-E is a modified version of AMSR that was launched December 2002 aboard the Advanced Earth Observing Satellite-II (ADEOS-II). It is a six-frequency dual-polarized total-power passive microwave radiometer that observes water-related geophysical parameters supporting global change science and monitoring efforts. The hardware improvements over existing spaceborne microwave radiometers for Earth imaging include the largest main reflector of its kind and addition of 6.925-GHz channels. These improvements provide finer spatial resolution and the capability to retrieve sea surface temperature and soil moisture information on a global basis. This paper provides an overview of the instrument characteristics, mission objectives, and data products.     

Retrieval of atmospheric and surface parameters from satellite microwave radiometers over the Mediterranean Sea

A procedure to estimate atmospheric and sea surface parameters in the Mediterranean area from satellite microwave radiometric measurements is described. The method is founded on a simulator of brightness temperatures at the top of the atmosphere. The simulator is based on microwave sea emissivity and scattering model functions, derived from the outputs of the SEAWIND software, which implements a two-scale microwave sea surface model and a radiative transfer scheme in a nonscattering atmosphere. The development of the model functions aims to reduce the SEAWIND computational time, still maintaining its sensitivity to the main geophysical variables. Different adaptations of the simulation model have been performed to better reproduce the radiometric data in the region of interest. A comparison between the simulations and the Special Sensor Microwave/Imager (SSM/I) observations acquired throughout year 2000 over the Mediterranean Sea has permitted us to refine the model functions as well as to assess the whole simulation procedure. As for the inversion problem, a regression analysis has been applied to two different synthetic datasets to retrieve integrated precipitable water vapor, liquid water path and wind speed. The first dataset simulates the observations of SSM/I, whilst the second one concerns the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Both have been generated by using the ECMWF atmospheric profiles and the measurements of the SeaWinds scatterometer aboard QuickSCAT. The SSM/I data have been used to carry out a statistical validation of the estimators. AMSR-E observations of a Tramontane-Mistral event, typical of the Mediterranean Sea, have been analyzed to evaluate the benefits of its expanded channel capability.     

An Ocean Surface Wind Vector Model Function for a Spaceborne Microwave Radiometer

Surface wind vector measurements over the oceans are vital for scientists and forecasters to understand the Earth's global weather and climate. In the last two decades, operational measurements of global ocean wind speeds were obtained from passive microwave radiometers (Special Sensor Microwave/ Imagers); and over this period, full ocean surface wind vector data were obtained from several National Aeronautics and Space Administration and European Space Agency scatterometry missions. However, since SeaSat-A in 1978, there have not been other combined active and passive wind measurements on the same satellite until the launch of Japan Aerospace Exploration Agency's Advanced Earth Observing Satellite-II in 2002. This mission provided a unique data set of coincident measurements between the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer (AMSR). The AMSR instrument measured linearly polarized brightness temperatures (TB) over the ocean. Although these measurements contained wind direction information, the overlying atmospheric influence obscured this signal and made wind direction retrievals not feasible. However, for radiometer channels between 10 and 37 GHz, a certain linear combination of vertical and horizontal brightness temperatures causes the atmospheric dependence to cancel and surface parameters such as wind speed and direction and sea surface temperature to dominate the resulting signal. In this paper, an empirical relationship between AMSR T_B's (specifically A ^. T_BV - T_BH) and surface wind vectors (inferred from SeaWinds' retrievals) is established for three microwave frequencies: 10, 18, and 37 GHz. This newly developed wind vector model function for microwave radiometers can serve as a basis for wind vector retrievals either separately or in combination with active scatterometer measurements.     

Soil moisture retrieval from AMSR-E

The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.     

Sensitivity of passive microwave snow depth retrievals to weather effects and snow evolution

Snow fall and snow accumulation are key climate parameters due to the snow's high albedo, its thermal insulation, and its importance to the global water cycle. Satellite passive microwave radiometers currently provide the only means for the retrieval of snow depth and/or snow water equivalent (SWE) over land as well as over sea ice from space. All algorithms make use of the frequency-dependent amount of scattering of snow over a high-emissivity surface. Specifically, the difference between 37- and 19-GHz brightness temperatures is used to determine the depth of the snow or the SWE. With the availability of the Advanced Microwave Scanning Radiometer (AMSR-E) on the National Aeronautics and Space Administration's Earth Observing System Aqua satellite (launched in May 2002), a wider range of frequencies can be utilized. In this study we investigate, using model simulations, how snow depth retrievals are affected by the evolution of the physical properties of the snow (mainly grain size growth and densification), how they are affected by variations in atmospheric conditions and, finally, how the additional channels may help to reduce errors in passive microwave snow retrievals. The sensitivity of snow depth retrievals to atmospheric water vapor is confirmed through the comparison with precipitable water retrievals from the National Oceanic and Atmospheric Administration's Advanced Microwave Sounding Unit (AMSU-B). The results suggest that a combination of the 10-, 19-, 37-, and 89-GHz channels may significantly improve retrieval accuracy. Additionally, the development of a multisensor algorithm utilizing AMSR-E and AMSU-B data may help to obtain weather-corrected snow retrievals.     

Water vapor profiling over ocean surface from airborne 90 and 183GHz radiometric measurements under clear and cloudy conditions

Radiometric measurements at 90 GHz and three sideband frequencies near the water vapor absorption line at 183.3 GHz were made with the Advanced Microwave Moisture Sounder (AMMS) aboard the NASA DC-8 aircraft over some regions of the Pacific Ocean during November 1989. These measurements were used to retrieve atmospheric water vapor profiles over ocean surface using the algorithm developed by T.T. Wilheit (1979). The algorithm incorporates a mechanism to estimate cloud liquid water when the estimated relative humidity is greater than 95%. The results are compared with the estimated values from the measurements of Special Sensor Microwave Imager (SSMI) and TIROS Operational Vertical Sounder (TOVS). The water vapor profiles estimated from AMMS are generally higher at low altitudes and lower at high altitudes compared to those from the TOVS measurements. Values of total precipitable water estimated from the AMMS and SSM/I are in general agreement. Cloud liquid water vapor profiles retrieved from the AMMS show more fluctuations than those from SSM/I     

A multifrequency algorithm for the retrieval of soil moisture on alarge scale using microwave data from SMMR and SSM/I satellites

The sensitivity of microwave emission at different frequencies to soil moisture in bare and vegetated soils has been investigated using experimental data. Since the best frequency for the measurement of soil moisture (L-band) is absent in current satellite sensors, it is necessary to seek alternative solutions. An algorithm is proposed for the retrieval of soil moisture based on the sensitivity to moisture of both the brightness temperature and the polarization index at C-band, one that is able to correct for the effect of vegetation by means of the polarization index at X-band. The algorithm has been tested by using experimental data collected with airborne microwave radiometers on agricultural areas and validated by using the data sets of special sensor microwave/imager (SMM/I) and scanning multichannel microwave radiometer (SMMR). These research activities are planned in view of coming new satellites: AQUA (NASA) and ADEOS-II (NASDA), which will be launched by the end of 2001. These will have new generation microwave radiometers (AMSR-E and AMSR) onboard, which show much better characteristics with respect to the previous sensors, in particular an enhanced spatial resolution     

A Wind and Rain Backscatter Model Derived From AMSR and SeaWinds Data

The SeaWinds scatterometer was originally designed to measure wind vectors over the ocean by exploiting the relationship between wind-induced surface roughening and the normalized radar backscatter cross section. Rain can degrade scatterometer wind estimation; however, the simultaneous wind/rain (SWR) algorithm was developed to enable SeaWinds to simultaneously retrieve wind and rain rate data. This algorithm is based on colocating data from the Precipitation Radar on the Tropical Rainfall Measuring Mission and SeaWinds on QuikSCAT. This paper develops a new wind and rain radar backscatter model for SWR using colocated data from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds aboard the Advanced Earth Observing Satellite II. This paper accounts for rain height in the model in order to calculate surface rain rate from the integrated rain rate. The performance of SWR using the new wind/rain model is measured by comparison of wind vectors and rain rates to the previous SWR algorithm, AMSR rain rates, and National Center for Environmental Prediction numerical weather prediction winds. The new SWR algorithm produces more accurate rain estimates and improved winds, and detects rain with a low false alarm rate.      

Sea ice concentration, ice temperature, and snow depth using AMSR-E data

A summary of the theoretical basis and initial performance of the algorithms that are used to derive sea ice concentration, ice temperature, and snow depth on sea ice from newly acquired Earth Observing System-Aqua/Advanced Microwave Scanning Radiometer-EOS (AMSR-E) radiances is presented. The algorithms have been developed and tested using historical satellite passive microwave data and are expected to provide more accurate products, since they are designed to take advantage of the wider range of frequencies and higher spatial resolution of the AMSR-E microwave instrument. Validation programs involving coordinated satellite, aircraft, and surface measurements to determine the accuracies of these sea ice products and to improve further our capability to monitor global sea ice are currently underway.     

NOAA operational hydrological products derived from the advanced microwave sounding unit

With the launch of the NOAA-15 satellite in May 1998, a new generation of passive microwave sounders was initiated. The Advanced Microwave Sounding Unit (AMSU), with 20 channels spanning the frequency range from 23-183 GHz, offers enhanced temperature and moisture sounding capability well beyond its predecessor, the Microwave Sounding Unit (MSU). In addition, by utilizing a number of window channels on the AMSU, the National Oceanic and Atmospheric Administration (NOAA) expanded the capability of the AMSU beyond this original purpose and developed a new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS). This includes precipitation rate, total precipitable water, land surface emissivity, and snow cover. Details on the current status of the retrieval algorithms (as of September 2004) are presented. These products are complimentary to similar products obtained from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (SSMI) and the Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E). Due to the close orbital equatorial crossing time between NOAA-16 and the Aqua satellites, comparisons between several of the MSPPS products are made with AMSR-E. Finally, several application examples are presented that demonstrate their importance to weather forecasting and analysis, and climate monitoring.     

Use of the scanning multichannel microwave radiometer (SMMR) to retrieve soil moisture and surface temperature over the central United States

The 6.6-, 10.7-, and 18-GHz data from the Scanning Multichannel Microwave Radiometer (SMMR) for 1979, 1980, and 1982 have been used to derive soil moisture and surface temperature for the south central United States. The 1979 data have been used to calibrate the radiative transfer model parameters, and the 1980 and 1982 data were used to derive soil moisture and surface temperature that have been compared with the corresponding values from the National Centers for Environmental Prediction (NCEP) reanalyses model outputs. These comparisons have shown that SMMR is able to qualitatively predict the seasonal cycle of land surface hydrological variability, and this information can be used for studies involving land-atmosphere interaction and hydrology. This study is of particular importance with the presence of both the Aqua satellite and the Advanced Earth Observing Satellite II that carry onboard the Advanced Microwave Scanning Radiometer (AMSR), which has channels similar to the SMMR, but with better spatial resolution. The results of this study will help us to plan for AMSR retrievals of soil moisture and surface temperature.     

Global survey and statistics of radio-frequency interference in AMSR-E land observations

Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz AMSR-E channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RFI at 6.9 GHz is most densely concentrated in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is concentrated mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing.     

Evaluation of late summer passive microwave Arctic sea iceretrievals

The melt period of the Arctic sea ice cover is of particular interest in studies of climate change due to the albedo feedback mechanisms associated with meltponds and openings in the ice pack. The traditionally used satellite passive microwave sea ice concentration algorithms have deficiencies during the summer months due to the period's highly variable surface properties. A newly developed ice concentration algorithm overcomes some of these deficiencies. It corrects for low ice concentration biases caused by surface effects through the use of 85 GHz data in addition to the commonly used 19 and 37 GHz data and, thus, the definition of an additional ice type representing layering and inhomogeneities in the snow layer. This new algorithm will be the standard algorithm for Arctic sea ice concentration retrievals with the EOS Aqua advanced microwave scanning radiometer (AMSR-E) instrument. In this paper, we evaluate the performance of this algorithm for the summer period of 1996 using data from the special sensor microwave imager (SSM/I) which has frequencies similar to the AMSR instrument. The temporal evolution of summertime passive microwave sea ice signatures are investigated and sea ice concentration retrievals from the standard NASA team and the new algorithm are compared. The results show that the introduction of the additional sea ice type in the new algorithm leads to improved summertime sea ice concentrations. The SSM/I sea ice retrievals are validated using SAR-derived ice concentrations that have been convolved with the SSM/I antenna pattern to ensure an appropriate comparison. For the marginal ice zone, with ice concentrations ranging from 40% to 100%, the correlation coefficient of SAR and SSM/I retrievals is 0.66 with a bias of 5% toward higher SAR ice concentrations. For the central Arctic, where ice concentrations varied between 60% and 100%, the correlation coefficient is 0.87 with a negligible bias     

Ocean surface wind speed and direction retrievals from the SSM/I

A semiempirical model is developed that retrieves ocean surface wind direction information in addition to improved wind speeds from Special Sensor Microwave/Imager (SSM/I) measurements. Radiative transfer and neural network techniques were combined in the authors' approach. The model was trained and tested using clear sky cases, but atmospheric transmittance is retrieved so that retrieval in other than clear sky conditions is possible. With two SSM/I instruments currently providing operational ocean surface wind speed retrievals, the addition of wind direction information and improved wind speed retrievals will enhance the impact of this data in weather prediction models and marine weather forecasting     

Interpretation of SSM/I measurements over Greenland

Multispectral brightness temperature (T_B) measurements over Greenland are obtained from the Special Sensor Microwave Imager (SSM/I), which are flown aboard the DMSP satellites. This paper examines the different spectral characteristics over Greenland throughout the year. Although snow covers the vast majority of Greenland, the southern regions rarely exhibit the spectral characteristics associated with snowcover (i.e., T_B decreases at higher frequencies). In fact, the SSM/I polarization and frequency measurements over southern Greenland are more indicative of water than a snow-covered surface (i.e., T_B increases at higher frequencies). A simplified physical model is developed to help explain the anomalous measurements over southern Greenland. Model results indicate that high frequency radiation is mainly scattered by snow grains residing above the subsurface ice layers, whereas low frequency radiation is scattered throughout a much greater depth. Since low frequencies are scattered throughout a greater volume, they are depressed relative to high frequencies, and the typical snowcover signature is absent     

The Humidity Sounder for Brazil - an international partnership

The Humidity Sounder for Brazil (HSB) is a crucial component of the Atmospheric Infrared Sounder (AIRS) sounding suite and a valuable contribution from Brazil to the National Aeronautics and Space Administration's Aqua mission. Its design and functionality are practically identical to that of the Advanced Microwave Sounding Unit-B - proven on the latest National Oceanic and Atmospheric Administration's weather satellites. We briefly discuss its heritage, the measurement concept and principle of operation, and the design. The complex multinational acquisition process employed for HSB is described, along with the approach to be used for operations, derivation of radiometric and geophysical data products, and validation of those products - with emphasis on activities in Brazil. We also describe postlaunch research plans and discuss the importance of humidity observations in tropical regions like Brazil.     

Freeze/thaw classification for prairie soils using SSM/Iradiobrightnesses

Data from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) have been used to classify snow-free soils in the northern Great Plains as either frozen or thawed. The technique is based on differing sensitivities among SMMR radiobrightness frequencies to liquid moisture and volume scattering in the upper few millimeters of bare soil. The SMMR is no longer active. A current near-equivalent is the Special Sensor Microwave/Imager (SSM/I). The authors demonstrate that SSM/I radiobrightnesses also exhibit differential sensitivities to liquid water and volume scattering in frozen soil despite their higher frequencies. They find that the best classification discriminants for SSM/I data are a combination of the 37-GHz V-pol radiobrightnesses and the 19-to-37-GHz V-pol spectral gradients. They also examine the sensitivity of the classification to atmospheric emission and absorption and find little effect     

Global identification of snowcover using SSM/I measurements

Visible satellite sensors have monitored snowcover throughout the Northern Hemisphere for almost thirty years. These sensors can detect snowcover during daylight, cloud-free conditions. The operational procedure developed by NOAA/NESDIS requires an analyst to manually view the images in order to subjectively distinguish between clouds and snowcover. Because this procedure is manually intensive, it is only performed weekly. Since microwave sensors see through nonprecipitating clouds, snowcover can be determined objectively without the intervention of an analyst. Furthermore, microwave sensors can provide daily analysis of snowcover in real-time, which is essential for operational forecast models and regional hydrologic monitoring. Snowcover measurements are obtained from the Special Sensor Microwave Imager (SSM/I), flown aboard the DMSP satellites. A decision tree, containing various filters, is used to separate the scattering signature of snowcover from other scattering signatures. Problem areas are discussed and when possible, a filter is developed to eliminate biases. The finalized decision tree is an objective algorithm to monitor the global distribution of snowcover. Comparisons are made between the SSM/I snowcover product and the NOAA/NESDIS subjectively analyzed weekly product     

Evaluation of SSM/I and AMSR-E Sea Ice Concentrations in the Antarctic Spring Using KOMPSAT-1 EOC Images

To evaluate sea ice concentrations (SICs) from the special sensor microwave/imager (SSM/I) and advanced microwave scanning radiometer-EOS (AMSR-E), we observed sea ice with the 6-m-resolution panchromatic electronic optical camera (EOC) sensor onboard the Korea Multi-Purpose Satellite-1 (KOMPSAT-1). A total of 68 cloud-free EOC images were obtained across the Antarctic continental edges from September to November 2005. Sea ice types in the EOC images were classified into white ice (W), gray ice (G), and dark-gray ice (D) and then compared with SSM/I and AMSR-E SICs. Spatiotemporal standard deviation of passive microwave SIC proved useful in selecting temporally stable and spatially homogeneous SICs to overcome the diurnal variation of sea ice in the analysis of data from multiple satellites. In the Antarctic spring, the EOC SIC of W + G showed the best fit to SSM/I SIC calculated by the NASA Team (NT) algorithm (mean difference of -2.3% and rmse of 3.2%), whereas that of W + G + D showed the best fit to AMSR-E SIC calculated by the NT2 algorithm (mean difference of 0.3% and rmse of 1.4%). It is concluded that the SSM/I NT algorithm responds to young ice in addition to the ice types A and B, whereas the AMSR-E NT2 algorithm detects ice type C and thin ice as well. The 4.7% difference of SICs between AMSR-E and SSM/I was attributed to the enhanced detection of ice type C (2.1%) and thin ice (2.6%) of the AMSR-E NT2 algorithm.     

Automatic detection and validity of the sea-ice edge: an application of enhanced-resolution QuikScat/SeaWinds data

Sea-ice edge detection is an essential task at the different national ice services to secure navigation in ice-covered seas. Comparison between the Remund and Long ice mask image from enhanced-resolution QuikScat/SeaWinds (QS) products and the analyzed ice edge from high-resolution RADARSAT synthetic aperture radar has shown that the automatically determined QS ice mask underestimates the Arctic ice extent. QS data was statistically analyzed by colocating the data with ice charts around Greenland and with the National Aeronautics and Space Administration Team's Special Sensor Microwave/Imager (SSM/I) ice concentration algorithm over the whole Arctic region. All variables, i.e., the backscatter in vertical and horizontal polarization, the active polarization ratio (APR) and the daily standard deviation, are sensitive to ice types and are strongly correlated with ice concentration when the relationship is expressed in exponential form. Our study showed that the APR is especially suitable for ice-ocean separation, and a threshold of -0.02 was determined. An ice edge algorithm based on this APR threshold was developed using the other variables with conservative season-dependent thresholds to eliminate additional ocean noise. Also, the history of the ice cover is considered in order to detect single ice fields that are separated from the main Arctic pack ice. Validation with RADARSAT 1 and with the Advanced Very High Resolution Radiometer showed that the new algorithm successfully detects very low ice concentrations of about 10% during the entire year. The validity of the detected ice edge for near-real-time issues is also discussed in relation to the ice motion in the Marginal Ice Zone and the integration time necessary to produce the enhanced-resolution images. The new algorithm improves the automatic global ice edge resolution by a factor of two when compared to SSM/I products and could be used in both model initialization and data assimilation.