Advanced Microwave Scanning Radiometer-Earth Observing System Research Articles

An Assessment of Sea-Ice Thickness Along the Labrador Coast From AMSR-E and MODIS Data for Operational Data Assimilation

In this paper, sea-ice thickness values are calculated along the Labrador coast using data from two sensors representative of those available for operational data assimilation. Data from the first sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), are used to calculate the ice thickness using a heat balance equation. Relationships between the MODIS ice thickness and polarization ratio from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are used to calculate the thickness of thin ice (less than 0.2 m) from the AMSR-E data. This is done for each frequency on the AMSR-E sensor in the range of 6.9-36.5 GHz. Through comparison with data from ice charts, it is found that the errors are lowest for thickness values calculated from low-frequency AMSR-E data. The accuracies of the ice thickness from MODIS, AMSR-E, operational ice charts, and two moored upward looking sonars are further assessed using the triple collocation method. It is found that the error associated with ice thickness from AMSR-E is the lowest and the error associated with ice thickness from MODIS is the highest. While the MODIS data represent the small-scale variability of the sea-ice thickness better than the AMSR-E data, the MODIS data can produce spurious values of ice thickness due to unmasked clouds. To use ice thickness from MODIS in an automated algorithm, quality control would need to be applied to the MODIS data to remove unmasked clouds which lead to spurious values of thick ice. The errors calculated for the ice thickness from AMSR-E, which are calculated based on a relationship calibrated with MODIS ice thickness from a clear-sky day, indicate that these data would be useful for operational data assimilation.

Three way validation of MODIS and AMSR-E sea surface temperatures

The estimation of retrieval uncertainty and stability are essential for the accurate interpretation of data in scientific research, use in analyses, or numerical models. The primary uncertainty sources of satellite SST retrievals are due to errors in spacecraft navigation, sensor calibration, sensor noise, retrieval algorithms, and incomplete identification of corrupted retrievals. In this study, comparisons to in situ data are utilized to investigate retrieval accuracies of microwave (MW) SSTs from the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) and infrared (IR) SSTs from the Moderate Resolution Imaging Spectroradiometer (MODIS). The highest quality MODIS data were averaged to 25 km for comparison. The in situ SSTs are used to determine dependencies on environmental parameters, evaluate the identification of erroneous retrievals, and examine biases and standard deviations (STD) for each of the satellite SST data sets. Errors were identified in both the MW and IR SST data sets: (1) at low atmospheric water vapor a posthoc correction added to AMSR-E was incorrectly applied and (2) there is significant cloud contamination of nighttime MODIS retrievals at SST <10°C. A correction is suggested for AMSR-E SSTs that will remove the vapor dependency. For MODIS, once the cloud contaminated data were excluded, errors were reduced but not eliminated. Biases were found to be −0.05°C and −0.13°C and standard deviations to be 0.48°C and 0.58°C for AMSR-E and MODIS, respectively. Using a three-way error analysis, individual standard deviations were determined to be 0.20°C (in situ), 0.28°C (AMSR-E), and 0.38°C (MODIS).

The construction and application of the AMSR-E global microwave emissivity database

Land surface microwave emissivity is an important parameter to describe the characteristics of terrestrial microwave radiation, and is the necessary input amount for inversion various geophysical parameters. We use brightness temperature of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and synchronous land surface temperature and atmospheric temperature-humidity profile data obtained from the MODIS which aboard on satellite AQUA the same as AMSR-E, to retrieved microwave emissivity under clear sky conditions. After quality control, evaluation and design, the global microwave emissivity database of AMSR-E under clear sky conditions is established. This database include 2002–2011 years, different regions, different surface coverage, dual-polarized, 6.9,10.65, 18.7, 23.8, 36.5 and 89GHz, ascending and descending orbit, spatial resolution 25km, global 0.05 degrees, instantaneous and half-month averaged emissivity data. The database can provide the underlying surface information for precipitation algorithm, water-vapor algorithm, and long-resolution mode model (General Circulation Model (GCM) etc.). It also provides underlying surface information for the satellite simulator, and provides basic prior knowledge of land surface radiation for future satellite sensors design. The emissivity database or the fast emissivity obtained can get ready for climate model, energy balance, data assimilation, geophysical model simulation, inversion and estimates of the physical parameters under the cloud cover conditions.

A neural network method for monitoring snowstorm: A case study in southern China

It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learning neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7V and TB36.5V, TB18.7H and TB36.5H, TB23.8V and TB89V, TB23.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.

Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in U.S.

As part of the Soil Moisture and Ocean Salinity (SMOS) validation process, a comparison of the skills of three satellites [SMOS, Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) or Advanced Microwave Scanning Radiometer, and Advanced Scatterometer (ASCAT)], and one-model European Centre for Medium Range Weather Forecasting (ECMWF) soil moisture products is conducted over four watersheds located in the U.S. The four products compared in for 2010 over four soil moisture networks were used for the calibration of AMSR-E. The results indicate that SMOS retrievals are closest to the ground measurements with a low average root mean square error of 0.061 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> for the morning overpass and 0.067 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> for the afternoon overpass, which represents an improvement by a factor of 2-3 compared with the other products. The ECMWF product has good correlation coefficients (around 0.78) but has a constant bias of 0.1-0.2 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> over the four networks. The land parameter retrieval model AMSR-E product gives reasonable results in terms of correlation (around 0.73) but has a variable seasonal bias over the year. The ASCAT soil moisture index is found to be very noisy and unstable.

Retrieving High-Resolution Surface Soil Moisture by Downscaling AMSR-E Brightness Temperature Using MODIS LST and NDVI Data

A method to retrieve soil moisture at high spatial resolution is presented in this paper. The method is based on soil moisture retrieval with passive brightness temperature. The method of retrieving land surface temperature with passive microwave is combined with the relationship between the microwave polarization difference index (MPDI) and normalized difference of vegetation index (NDVI) to obtain high-resolution microwave brightness temperature and soil moisture. Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) 18.7-GHz brightness temperature at 25-km resolution is downscaled to 1-km using high-resolution MODIS visible/infrared (VIS/IR) data. High-resolution soil moisture is retrieved with the downscaled microwave brightness temperature using a single-channel algorithm (SCA) and the Qp model to deal with the influence of roughness. The method is applied to an area in northwest of China. The downscaled high-resolution soil moisture is tested with ground data collected at three sites within the Maqu monitoring network from July 1, 2008 to June 30, 2009. The trend of the time series of the downscaled soil moisture is similar to the ground measurements during this period with root mean-square error (RMSE) less than 0.12. The results show that the method is more suitable to moderate to drier soil conditions with bare surface or covered by sparse vegetation.

A spatially coherent global soil moisture product with improved temporal resolution

Global soil moisture products that are completely independent of any type of ancillary data and solely rely on satellite observations are presented. Additionally, we further develop an existing downscaling technique that enhances the spatial resolution of such products to approximately 11 km. These products are based on internal modules of the Land Parameter Retrieval Model (LPRM), an algorithm that uses the radiative transfer equation to link soil moisture, vegetation optical depth and land surface temperature to observed brightness temperatures. The soil moisture product that is independent of any type of ancillary data uses the internally calculated dielectric constant as a soil moisture proxy. This data product is not influenced by errors associated with coarse-scale global soil property maps or by any other type of forcing (e.g. re-analysis) data and is therefore solely based on satellite microwave observations. The second step builds upon recent developments to increase the spatial resolution of the LPRM retrievals using a smoothing filter downscaling method. With this method we can attain a spatial resolution that can be more useful at the scale of local and regional hydrological studies as well. The steps presented in this paper were applied to observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). The newly derived data sets were validated using ground-based observations from the International Soil Moisture Network (ISMN). The internally calculated dielectric constant product results in significantly more days with valid retrievals than the original soil moisture data products, in particular over arid regions. The dielectric constant product resulted in similar correlations with in situ data as the original soil moisture data product. Together, these findings demonstrate the usefulness of this new dielectric constant product for the hydrological modeling community and climate studies. A case study on the Australian Fitzroy catchment demonstrated that the downscaled data product has a more detailed spatial description of soil moisture, especially during wet and dry conditions with more pronounced dry and wet regions within the catchment. The increased resolution data products could therefore improve runoff predictions and this study demonstrated the potential added value of a transitioning from a spatial resolution of 56 km toward a higher resolution of 11 km. The hydrological implications of these newly developed data records are not only linked to AMSR-E satellite data, but also to the next generation Soil Moisture Active and Passive (SMAP) mission where a 9 km spatial resolution is the target resolution for satellite soil moisture products. The new data products will not replace the current LPRM products, but will be added to the existing array of data products and will become publicly available through our data portals.

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager (FY3B-MWRI) in China. Based on 7-year (2002–2009) observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and snow depth from Chinese meteorological stations, we develop a semi-empirical snow depth retrieval algorithm. When its land cover fraction is larger than 85%, we regard a pixel as pure at the satellite passive microwave remote-sensing scale. A 1-km resolution land use/land cover (LULC) map from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences, is used to determine fractions of four main land cover types (grass, farmland, bare soil, and forest). Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data. Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell. Through evaluation of this algorithm using station measurements from 2006, the root mean square error (RMSE) of snow depth retrieval is about 5.6 cm. In forest regions, snow depth is underestimated relative to ground observation, because stem volume and canopy closure are ignored in current algorithms. In addition, comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover products (MYD10C1) in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%. Finally, we compared snow water equivalence (SWE) derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere. The results show that AMSR-E overestimated SWE in China, which agrees with other validations.

Measurement and Simulation of Topographic Effects on Passive Microwave Remote Sensing Over Mountain Areas: A Case Study From the Tibetan Plateau

Knowledge about the surface soil water content is essential because it controls the surface water dynamics and land-atmosphere interaction. In high mountain areas in particular, soil surface water content controls infiltration and flood events. Although satellite-derived surface soil moisture data from passive microwave sensors are readily available for most regions globally, mountainous areas are often excluded from these data (or at least flagged as biased) due to the strong topographic influence on the retrieved signal. Even though a substantial volume of literature is available dealing with topographic effects on spaceborne brightness temperature, no systematic analysis has been reported. Therefore, we present a comprehensive analysis of topographic effects on brightness temperature at C-band using a two-step approach. First, a well-controlled field experiment is carried out using a mobile truck-mounted C-band radiometer to analyze the impact of geometric and adjacent effects on the radiometer signal. Additionally, a comprehensive radiative transfer model is developed accounting for both effects and tested on the ground-based data. Second, recorded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data over the Tibetan Plateau were used to analyze the error due to the impact of topography using the developed model. The results of the field experiment clearly show that the geometric effect of a single hill has a much larger impact on brightness temperature compared to the adjacent effect of multiple hills, whereby, due to the geometric effect, the bias is up to +20 K for horizontal and -13 K for vertical polarization. For the adjacent effect, the bias is less than 3 K for both polarizations. Additionally, the developed radio transfer model was able to reproduce both effects with high accuracy. For the AMSR-E data, the model shows that the brightness temperature recorded is biased in the same way as the ground-based measurements and that uncertainties induced by the wide existence of atypical mountain regions in the Tibetan Plateau will have a great impact on the retrieving error (maximum 30%). The largest impact on the retrieval error, on the other hand, is calculated for the soil moisture with a maximum relative error of 44%. The negligible impact can be attributed to false parameterization of the soil texture, soil surface temperature, and sky temperature. Finally, the overall absolute error in the estimated water content is quantified on average with 4%, whereby single pixels indicate a maximum absolute error of up to 16%. In conclusion, we show that recorded spaceborne brightness temperatures are highly biased by topographic effects in mountainous regions using a comprehensive radiative transfer model. Additionally, we suggest using this model to invert the effective surface emissivity of mountain areas for standard processing of higher level data products such as surface soil water content.

Intercomparisons of Brightness Temperature Observations Over Land From AMSR-E and WindSat

The Advanced Microwave Scanning Radiometer-EOS (AMSR-E) on Aqua and WindSat on Coriolis instruments have collected multichannel passive microwave data over the global land and oceans since 2002 and 2003, respectively. AMSR-E on Aqua ceased operation in October 2011 due to a malfunction in the antenna scanning mechanism. AMSR-E and WindSat have similar frequencies, bandwidths, polarizations, incidence angles and instantaneous fields of view (IFOVs), but there are some differences in their configurations. The altitudes and local overpass times also differ between the AMSR-E and WindSat sensors. The time series of data from the two instruments have a long period of overlap, which can be used to intercompare and cross-calibrate the instrument data sets taking into account the instrument differences. This would allow retrieval of geophysical parameters using common algorithms that could take advantage of the increased time duration and sampling coverage afforded by combining data from the two sensors. In this paper, we focus on land applications and compare the multichannel data from these two sensors over land. Channels useful primarily for soil moisture and vegetation water content studies (i.e., ~ 6, ~ 10, ~ 18, and ~ 37 GHz at H- and V-pol) are used in the comparisons. To minimize differences caused by surface temperature effects related to local overpass times, only descending passes (with Equator crossing times for AMSR-E of 1:30 a.m. and WindSat 6:00 a.m.) are considered. Homogeneous and temporally stable sites such as Dome-C, Antarctica and the Amazon forest, and a flat and bare region in the Sahara desert are chosen to evaluate similarities and differences among comparable channel observations. Taking into consideration the sensor configurations and geophysical conditions during the descending overpasses, reasonably good agreement is observed between AMSR-E and WindSat measurements over the globe.

Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder

Abstract A physical retrieval algorithm has been developed for deriving the legacy atmospheric profile (LAP) product from infrared radiances of the Advanced Baseline Imager (ABI) on board the next-generation Geostationary Operational Environmental Satellite (GOES-R) series. In this study, the GOES-R ABI LAP retrieval algorithm is applied to the GOES-13 sounder radiance measurements (termed the GOES-13 LAP retrieval algorithm in this study) for its validation as well as for potential transition of the GOES-13 LAP retrieval algorithm for the operational processing of GOES sounder data. The GOES-13 LAP retrievals are compared with five different truth measurements: radiosonde observation (raob) and microwave radiometer–measured total precipitable water (TPW) at the Atmospheric Radiation Measurement Cloud and Radiation Testbed site, conventional raob, TPW measurements from the global positioning system–integrated precipitable water NOAA network, and TPW measurements from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The results show that with the GOES-R ABI LAP retrieval algorithm, the GOES-13 sounder provides better water vapor profiles than the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast fields at the levels between 300 and 700 hPa. The root-mean-square error (RMSE) and standard deviation (STD) of the GOES-13 sounder TPW are consistently reduced from those of the GFS forecast no matter which measurements are used as the truth. These substantial improvements indicate that the GOES-R ABI LAP retrieval algorithm is well prepared to provide continuity of quality to some of the current GOES sounder products, and the algorithm can be transferred to process the current GOES sounder measurements for operational product generation.

Soil moisture at watershed scale: Remote sensing techniques

Summary Soil moisture at high spatial resolution is required for various land processes related studies. However, currently the resolution of passive microwave retrieved soil moisture is low – around 25 km. To solve this problem, a soil moisture disaggregation algorithm based on thermal inertia relationship between daily temperature change and average soil moisture modulated by vegetation conditions has been formulated. This algorithm was applied to the AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) as well as SMOS (Soil Moisture and Ocean Salinity satellite) to produce the 1 km downscaled soil moisture over the Little Washita Watershed in Oklahoma for the growing season in 2010 and 2011.The disaggregated soil moisture has been compared to in situ observations. The results of this approach are very encouraging.

Passive Microwave Soil Moisture Downscaling Using Vegetation Index and Skin Surface Temperature

Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture downscaling algorithm based on a regression relationship between daily temperature changes and daily average soil moisture is developed and presented to produce an enhanced spatial resolution soil moisture product. The algorithm was developed based on the thermal inertial relationship between daily temperature changes and averaged soil moisture under different vegetation conditions, using 1/8° spatial resolution North American Land Data Assimilation System (NLDAS) surface temperature and soil moisture data, as well as 5-km Advanced Very High Resolution Radiometer (AVHRR) (1981–2000) and 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and surface temperature (2002–present) to build the look-up table at 1/8° resolution. This algorithm was applied to the 1-km MODIS land surface temperature to obtain the downscaled soil moisture estimates and then used to correct the soil moisture products from Advanced Microwave Scanning Radiometer–EOS (AMSR-E). The 1-km downscaled soil moisture maps display greater details on the spatial pattern of soil moisture distribution. Two sets of ground-based measurements, the Oklahoma Mesonet and the Little Washita Micronet, were used to validate the algorithm. The overall averaged slope for 1-km downscaled results vs. Mesonet data is 0.219, which is better than AMSR-E and NLDAS, while the spatial standard deviation (0.054 m 3 m −3 ) and unbiased RMSE (0.042 m 3 m −3 ) of 1-km downscaled results are similar to the other two datasets. The overall slope and spatial standard deviation for 1-km downscaled results vs. Micronet data (0.242 and 0.021 m 3 m −3 , respectively) are significantly better than AMSR-E and NLDAS, while the unbiased RMSE (0.026 m 3 m −3 ) is better than NLDAS and further than AMSR-E. In addition, Mesonet comparisons of all three soil moisture datasets demonstrate a stronger statistical significance than Micronet comparisons, and the p value of 1-km downscaled is generally better than the other two soil moisture datasets. The results demonstrate that the AMSR-E soil moisture was successfully disaggregated to 1 km. The enhanced spatial heterogeneity and the accuracy of the soil moisture estimates are superior to the AMSR-E and NLDAS estimates, when compared with in situ observations.

Inter-comparison of satellite sea ice motion with drifting buoy data

In this study, three low-resolution and three medium-resolution ice motion products were compared to ice-tethered profiler (ITP) global positioning system (GPS) data over a 2 year period. The ice motion products were the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), merged Advanced Scatterometer + Special Sensor Microwave/Imager (ASCAT + SSM/I), advanced synthetic aperture radar (ASAR), and Advanced Very High Resolution Radiometer (AVHRR) ice motion data. The results show that the data quality of six satellite products is better than or close to expected values. The error distributions of the satellite ice motion generally have high kurtosis and heavy tails and are not normally distributed. Low-resolution ice motion generally shows large errors in the Fram Strait. AVHRR summer ice motion shows a larger bias, probably affected by inaccurate cloud masking, while the large errors in ASAR ice motion mainly occur due to occasional geolocation errors of near-real-time ASAR images used for ice motion retrieval. Inter-comparison between satellite ice motion products with different time intervals is also discussed.

Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau

Long-term highly accurate surface soil moisture data of TP (Tibetan Plateau) are important to the research of Asian monsoon and global atmospheric circulation. However, due to the sparse in-situ networks, the lack of soil moisture observations has seriously hindered the progress of climate change researches of TP. Based on the Dual-Channel soil moisture retrieval algorithm and the satellite observation data of AMSR-E (Advanced Microwave Scanning Radiometer for EOS), we have produced the surface soil moisture data of TP from 2003 to 2010 and analyzed the seasonal characteristic of the soil moisture spatial distribution and its multi-year changing trend in area of TP. Compared to the in-situ observations, the accuracy of the soil moisture retrieved by the proposed algorithm is evaluated. The evaluation result shows that the new soil moisture product has a better accuracy in the TP region than the official product of AMSR-E. The spatial distribution of the annual mean values of soil moisture and the seasonal variations of the monthly-averaged soil moisture are analyzed. The results show that the soil moisture variations in space and time are consistent with the precipitation distribution and the water vapor transmission path in TP. Based on the new soil moisture product, we also analyzed the spatial distribution of the changing trend of multi-year soil moisture in TP. From the comparisons with the precipitation changing trend obtained from the meteorological observation sites in TP, we found that the spatial pattern of the changing trend of soil moisture coincides with the precipitation as a whole.

Improving soil moisture retrievals from a physically-based radiative transfer model

Near surface soil moisture is being estimated from space-borne passive microwave observations through inverting a physically-based radiative transfer model (RTM), the land surface microwave emission model (LSMEM) at Princeton University for the past several years. The existing retrieval scheme utilizes only the horizontal (H) polarization measurement from a single channel (10.65GHz). This physically-based approach requires a relatively large number of parameters, and it generally suffers from large biases/errors due to the difficulty in determining the correct parameters. This study characterizes these errors in order to improve the retrieval performance. Through model sensitivity analysis, this study finds that a dual polarization approach (using both horizontal and vertical polarizations) is needed to infer the correct vegetation opacity and correct polarization mixing measured by the space-borne sensor. Revisions are then made to the LSMEM formulations and soil moisture retrieval algorithm by 1) combining two vegetation parameters and one roughness parameter into one effective vegetation optical depth (VOD) value; and 2) providing an additional model equation that estimates the effective VOD from both polarizations and an initial guess of soil moisture value. The new retrieval algorithm is implemented to produce a daily 0.25° gridded soil moisture dataset based on observations from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Validations are performed globally against land surface model simulations and at local/point scale against in-situ data within the continental United States. The new retrievals are shown to have good and robust performance over most parts of the world in terms of reproducing the spatial and temporal dynamics of soil moisture.

Research on the Variation in Soil Moisture in Arid Regions in Northern China Based on AMSR-E

Soil moisture plays an important role in agricultural drought monitoring. However, the traditional observed soil moisture data from meteorological stations has not been able to meet the demands for large-scale drought monitoring temporally and spatially. The microwave remote sensing is a new effective way for obtaining near surface soil moisture. The temporal and spatial variation in soil moisture in arid regions in northern China is examined based on the soil moisture data retrieved from AMSR-E (Advanced Microwave Scanning Radiometer-EOS), with which the observed soil moisture data at 10 cm depth from meteorological stations are compared. The results show that there is a small change in soil moisture with seasons for the central and west areas, while a large change for the east areas of the study region and the soil moisture in summer and autumn is greater than that in spring and winter. And that it decreases from southeast to northwest spatially, as is agree with the spatial distribution of precipitation in the study area. Moreover, there is a great difference in spatial distribution between the soil moisture retrieved from AMSR-E and the observed soil moisture data from meteorological stations for the central and west areas, while a small difference for the east areas of the study region.

Retrieving the antarctic sea-ice concentration based on AMSR-E 89 GHz data

Sea-ice concentration is a key item in global climate change research. Recent progress in remotely sensed sea-ice concentration product has been stimulated by the use of a new sensor, advanced microwave scanning radiometer for EOS (AMSR-E), which offers a spatial resolution of 6 km×4 km at 89GHz. A new inversion algorithm named LASI (linear ASI) using AMSR-E 89GHz data was proposed and applied in the antarctic sea areas. And then comparisons between the LASI ice concentration products and those retrieved by the other two standard algorithms, ASI (arctic radiation and turbulence interaction study sea-ice algorithm) and bootstrap, were made. Both the spatial and temporal variability patterns of ice concentration differences, LASI minus ASI and LASI minus bootstrap, were investigated. Comparative data suggest a high result consistency, especially between LASI and ASI. On the other hand, in order to estimate the LASI ice concentration errors introduced by the tie-points uncertainties, a sensitivity analysis was carried out. Additionally an LASI algorithm error estimation based on the field measurements was also completed. The errors suggest that the moderate to high ice concentration areas (>70%) are less affected (never exceeding 10%) than those in the low ice concentration. LASI and ASI consume 75 and 112 s respectively when processing the same AMSR-E time series thourghout the year 2010. To conclude, by using the LASI algorithm, not only the seaice concentration can be retrieved with at least an equal quality as that of the two extensively demonstrated operational algorithms, ASI and bootstrap, but also in a more efficient way than ASI.

Satellite microwave detection of boreal forest recovery from the extreme 2004 wildfires in Alaska and Canada

The rate of vegetation recovery from boreal wildfire influences terrestrial carbon cycle processes and climate feedbacks by affecting the surface energy budget and land-atmosphere carbon exchange. Previous forest recovery assessments using satellite optical-infrared normalized difference vegetation index (NDVI) and tower CO(2) eddy covariance techniques indicate rapid vegetation recovery within 5-10 years, but these techniques are not directly sensitive to changes in vegetation biomass. Alternatively, the vegetation optical depth (VOD) parameter from satellite passive microwave remote sensing can detect changes in canopy biomass structure and may provide a useful metric of post-fire vegetation response to inform regional recovery assessments. We analyzed a multi-year (2003-2010) satellite VOD record from the NASA AMSR-E (Advanced Microwave Scanning Radiometer for EOS) sensor to estimate forest recovery trajectories for 14 large boreal fires from 2004 in Alaska and Canada. The VOD record indicated initial post-fire canopy biomass recovery within 3-7 years, lagging NDVI recovery by 1-5 years. The VOD lag was attributed to slower non-photosynthetic (woody) and photosynthetic (foliar) canopy biomass recovery, relative to the faster canopy greenness response indicated from the NDVI. The duration of VOD recovery to pre-burn conditions was also directly proportional (P < 0.01) to satellite (moderate resolution imaging spectroradiometer) estimated tree cover loss used as a metric of fire severity. Our results indicate that vegetation biomass recovery from boreal fire disturbance is generally slower than reported from previous assessments based solely on satellite optical-infrared remote sensing, while the VOD parameter enables more comprehensive assessments of boreal forest recovery.

Characteristics of global precipitable water in ENSO events revealed by COSMIC measurements

Precipitable water (PW) retrievals from FORMOSAT‐3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Global Positioning System (GPS) radio occultation (RO) measurements were analyzed and compared with those derived from Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR‐E) during the El Niño‐Southern Oscillation (ENSO) events from 2007 to 2011. For the three ENSO events in 2007–2011, monthly mean binned COSMIC PW results are in a very high correlation (up to 0.98) with those of SSM/I and AMSR‐E over the ocean, generally with root‐mean‐square differences less than 4 mm. PW retrievals from the three satellites are also of similar latitudinal variations. However, the PW is slightly underestimated by GPS RO, in particular, in the tropical regions. This underestimate may be caused partially by the fact that not all RO measurements can reach the surface. Inter‐satellite PW anomaly comparisons for the winter months in the ENSO events, with respect to those during the neutral (non‐ENSO) months, show consistent ENSO signals with major PW anomaly near the central Pacific in the warm event and near the Indonesian region and east of Australia in the two cold events. However, the 2007/2008 La Niña is somewhat less correlated for COSMIC with AMSR‐E and SSM/I. For the stronger 2010/2011 La Niña, their PW anomalies are in higher correlations of about 0.8.