Advanced Microwave Scanning Radiometer-Earth Observing System Data Research Articles

Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression

Satellite retrieval can offer global soil moisture information, such as Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data. AMSR-E has been used to provide soil moisture all over the world, with a coarse resolution of 25 km × 25 km. The coarse resolution of the soil moisture dataset often hinders its use in local or regional research. This work proposes a new framework based on the random forest (RF) model while using five auxiliary data to downscale the AMSR-E soil moisture data over North China. The downscaled results with a 1 km spatial resolution are verified against in situ measurements. Compared with AMSR-E data, the correlation coefficient of the downscaled data is increased by 0.17, and the root mean squared error, mean absolute error, and unbiased root mean square error are reduced by 0.02, 0.01, and 0.03 m3/m3, respectively. In addition, the comparison results with Multiple Linear Regression and Support Vector Regression downscaled data show that the proposed method significantly outperforms the other two methods. The feasibility of our model is well supported by the importance analysis and leave-one-out analysis. Our study, which combines RF with spatiotemporal search algorithms and efficient auxiliary data, may provide insights into soil moisture downscaling in large areas with various surface characteristics and climatic conditions.

A continuous global record of near-surface soil freeze/thaw status from AMSR-E and AMSR2 data

ABSTRACTLong term and consistent records of near-surface soil freeze/thaw (F/T) status are required for understanding hydrological, ecological, and biogeochemical responses of land surface to global warming. To create such a record, we compiled and inter-calibrated satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and its successor, AMSR2, using linear regression models, and then applied a discriminant algorithm to the calibrated observations to map global F/T status from 2002 to 2018. The new global F/T dataset was rigorously assessed using in situ air and surface temperatures, and modelled soil temperature. Results show that agreement between remotely sensed F/T status and that determined by in situ or modelled temperature exceeds 85% and 79% for ascending and descending orbits, respectively. Moreover, consistency between the F/T datasets derived from two sensors is around 0.8 after calibration, in nonoverlapping time frames. With such an accuracy and consistency, we calculated frost days and frost trends using the F/T dataset. The mean annual number of frost days of high northern latitudes (>45° N) is 279.2 ± 44.1 days. Based on Mann-Kendall’s tau-b test, 7.7% of global lands show a significant warming trend, and most of which are concentrated in the Western United States, Northern and Eastern Canada, Northern Europe and Western China. Such a spatial distribution was found to be consistent with the global land surface temperature anomalies trend from 2002 to 2018. Both the results of applications and favourable accuracy indicate the potential of this long, consistent F/T record to track global temperature change.

Research on Applicability Analysis of Drought Index in Liaoning Area

Based on brightness temperature data of AMSR-E (advanced microwave scanning radiometer — earth observing system) in 2009 and 2011, the inversion on 8 brightness temperature ratios is performed as alternative drought indexes in this paper. The correlation analysis is made through the soil moisture extracted from inversion drought index and data itself, and 3 kinds of alternative drought that relatively coincide with soil moisture of AMSR-E data itself are selected. And then on this basis, the analysis on the change situation of 3 kinds of microwave moisture indexes in 10 pixel × 10 pixel rectangular region of Shenyang and Chaoyang is made, and the evaluation on the monitoring advantages and disadvantages of 3 kinds of indexes on soil moisture is performed, so as to obtain the optimal index PIv6.9 for drought monitoring. In the end, in order to further study PIv6.9 on soil moisture monitoring situation within the range of Liaoning province, four days with relatively large precipitation are selected according to meteorological station data in 2009, the precipitation data of 51 meteorological stations in Liaoning province are interpolated within the range of the whole province by utilizing Kriging method, and the contrastive analysis on the spatial distribution of precipitation and PIv6.9 index is made. The results show that PIv6.9 can best reflect the spatial distribution characteristics of drought status in Liaoning province.

Assimilation of AMSR-E snow water equivalent data in a spatially-lumped snow model

Summary Accurately initializing snow model states in hydrologic prediction models is important for estimating future snowmelt, water supplies, and flooding potential. While ground-based snow observations give the most reliable information about snowpack conditions, they are spatially limited. In the north-central USA, there are no continual observations of hydrologically critical snow variables. Satellites offer the most likely source of spatial snow data, such as the snow water equivalent (SWE), for this region. In this study, we test the impact of assimilating SWE data from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) instrument into the US National Weather Service (NWS) SNOW17 model for seven watersheds in the Upper Mississippi River basin. The SNOW17 is coupled with the NWS Sacramento Soil Moisture Accounting (SACSMA) model, and both simulated SWE and discharge are evaluated. The ensemble Kalman filter (EnKF) assimilation framework is applied and updating occurs on a daily cycle for water years 2006–2011. Prior to assimilation, AMSR-E data is bias corrected using data from the National Operational Hydrologic Remote Sensing Center (NOHRSC) airborne snow survey program. An average AMSR-E SWE bias of −17.91 mm was found for the study basins. SNOW17 and SAC-SMA model parameters from the North Central River Forecast Center (NCRFC) are used. Compared to a baseline run without assimilation, the SWE assimilation improved discharge for five of the seven study sites, in particular for high discharge magnitudes associated with snow melt runoff. SWE and discharge simulations suggest that the SNOW17 is underestimating SWE and snowmelt rates in the study basins. Deep snow conditions and periods of snowmelt may have introduced error into the assimilation due to difficulty obtaining accurate brightness temperatures under these conditions. Overall results indicate that the AMSR-E data and EnKF are viable and effective solutions for improving simulations of the operational forecast model.

Performance of Global Soil Moisture Products in Crop Growing Region of Central India

The study was carried out to evaluate the global soil moisture product of Advanced Microwave Scanning Radiometer-2 (AMSR-2), Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity (SMOS) and Tropical Rainfall Measuring Mission’s (TRMM) Microwave Imager (TMI) in central region of India for monsoon period of 2011 and 2013. The evaluation was done using 100, 75 in-situ soil moisture measurements of 2011, 2013 respectively. The in-situ measurements were spread across ~10,000 sq.km area which is roughly equal to 12 pixels of SMOS, AMSR-2 AMSR-E and TMI data. The result shows reasonable correlation of r 2 = 0.506, 0.467 between SMOS and in-situ soil moisture for 2011, 2013 respectively. TMI exhibits good correlation of r 2 = 0.55 in 2013 but failed to do the same in 2011. AMSR-2 soil moisture products shows moderate correlation r 2 = 0.32 in 2011 but poorly correlated in 2013.

The Development of Microwave Vegetation Indices from WindSat Data

The microwave vegetation indices (MVIs), including parameter A ( $\mathbf{MVIs}\_\mathbf{A}$ ) and parameter B ( $\mathbf{MVIs}\_\mathbf{B}$ ), have been recently developed based on advanced microwave scanning radiometer-earth observing system (AMSR-E) measurements. Coriolis/WindSat (WindSat) is a space-borne multifrequency polarimetric microwave radiometer with similar frequencies to the AMSR-E. Unlike the AMSR-E instrument configuration, the WindSat observation angles vary at the different frequencies and range from 49.9° to 55.3°. This variation results in significant uncertainty in deriving MVIs using WindSat data. In this study, we extended our algorithm for deriving MVIs from AMSR-E to that under WindSat sensor configuration by considering the measurements from different observation angles. We will present the theoretical basis for this new algorithm and then compare the two MVIs derived from these two sensors’ data at global and pixel scales, respectively. We found that the MVIs from WindSat data and AMSR-E data share similar global distribution patterns and temporal trends. The $\mathbf{MVIs}\_\mathbf{B}$ at the 6.8- and 10.7-GHz frequency pair [ $\mathbf{MVIs}\_\mathbf{B}$ (6.8,10.7)] from WindSat data is somewhat higher than that from AMSR-E data, whereas the $\mathbf{MVIs}\_\mathbf{B}$ at the 10.7- and 18.7-GHz frequency pair [ $\mathbf{MVIs}\_\mathbf{B}$ (10.7,18.7)] is higher for AMSR-E. The MVIs from both the WindSat data and the AMSR-E data can be affected by satellite overpass times. The extended MVIs are expected to provide possible complementary information and contribute to vegetation monitoring, vegetation water content, biomass and soil moisture retrieval, and global climate change research.

Thin Ice Detection in the Barents and Kara Seas With AMSR-E and SSMIS Radiometer Data

We have studied thin ice detection using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Special Sensor Microwave Imager/Sounder radiometer data acquired over the Barents and Kara Seas during three winters (November-April) in 2008-2011. Moderate Resolution Imaging Spectroradiometer-based ice thickness charts were used as reference data. Thin ice detection was studied using polarization and spectral gradient ratios (PR and GR) calculated from the 36/37 and 89/91 GHz radiometer data. Thresholds for thin ice detection and maximum thicknesses for the detected thin ice (hT) were determined, as were error rates for misdetections. The results for different 1-D PR and GR parameters led to the conclusion that the AMSR-E PR36 and H-polarized GR8936 would be the best parameters for a 2-D classifier. We adopted the linear discrimination analysis (LDA) as a statistical tool. Thin ice areas with hT of 30 cm could be separated from thicker ice fields with approximately 20% error level. In our large data set, the estimation of thin ice thickness was not possible with reasonable accuracy due to the large scatter between ice thickness and the PR and GR signatures. This is likely due to a large data set, besides thin ice in polynyas also thin ice in the marginal ice zone and thin ice from freeze-up period. The optimal LDA parameters in the classifier and hT depended on the daily mean air temperature ((T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">am</sub> )). We could not yet parameterize the classifier optimally according to (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">am</sub> ), but the constructed classifier worked rather robustly as indicated by the relative small error rate variation between the three analyzed winters.

Evaluation of satellite soil moisture products over Norway using ground-based observations

In this study we evaluate satellite soil moisture products from the advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) over Norway using ground-based observations from the Norwegian water resources and energy directorate. The ASCAT data are produced using the change detection approach of Wagner et al. (1999), and the AMSR-E data are produced using the VUA-NASA algorithm (Owe et al., 2001, 2008). Although satellite and ground-based soil moisture data for Norway have been available for several years, hitherto, such an evaluation has not been performed. This is partly because satellite measurements of soil moisture over Norway are complicated owing to the presence of snow, ice, water bodies, orography, rocks, and a very high coastline-to-area ratio. This work extends the European areas over which satellite soil moisture is validated to the Nordic regions. Owing to the challenging conditions for soil moisture measurements over Norway, the work described in this paper provides a stringent test of the capabilities of satellite sensors to measure soil moisture remotely. We show that the satellite and in situ data agree well, with averaged correlation (R) values of 0.72 and 0.68 for ASCAT descending and ascending data vs in situ data, and 0.64 and 0.52 for AMSR-E descending and ascending data vs in situ data for the summer/autumn season (1 June–15 October), over a period of 3 years (2009–2011). This level of agreement indicates that, generally, the ASCAT and AMSR-E soil moisture products over Norway have high quality, and would be useful for various applications, including land surface monitoring, weather forecasting, hydrological modelling, and climate studies. The increasing emphasis on coupled approaches to study the earth system, including the interactions between the land surface and the atmosphere, will benefit from the availability of validated and improved soil moisture satellite datasets, including those over the Nordic regions.

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground conditions and precipitation regimes (from natural to agricultural surfaces and from desert to humid regions) and provide long-term in-situ data. One of the algorithms is the artificial neural network-based algorithm developed by the Institute of Applied Physics of the National Research Council (IFAC-CNR) (HydroAlgo) and the second one is the Single Channel Algorithm (SCA) developed by USDA-ARS (US Department of Agriculture-Agricultural Research Service). Both algorithms are based on the same radiative transfer equations but are implemented very differently. Both made use of datasets provided by the Japanese Aerospace Exploration Agency (JAXA), within the framework of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Global Change Observation Mission–Water GCOM/AMSR-2 programs. Results demonstrated that both algorithms perform better than the mission specified accuracy, with Root Mean Square Error (RMSE) ≤0.06 m3/m3 and Bias <0.02 m3/m3. These results expand on previous investigations using different algorithms and sites. The novelty of the paper consists of the fact that it is the first intercomparison of the HydroAlgo algorithm with a more traditional retrieval algorithm, which offers an approach to higher spatial resolution products.

Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery

This study compared the classification accuracies of three soil freeze–thaw discrimination algorithms in China using Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) passive microwave imagery from 2008. The algorithms used were the dual-index algorithm (DIA), the decision tree algorithm (DTA), and the discriminant function algorithm (DFA). The comparison was conducted based on 0 cm land-surface temperature data from 756 meteorological stations across China by constructing error meta-matrices, and it is divided into two parts. The first part compared the overall classification accuracies from two aspects: temporal variation and spatial distribution. In the second part, the classification accuracies of frozen and thawed soils were evaluated. Results showed that both SSMIS and AMSR-E data can be applied to the DIA, DTA, and DFA algorithms, although they were originally developed from different satellite data sets. However, each of the three algorithms has its own advantages and disadvantages. Possible improvements in the three algorithms for future work are also discussed.

Evaluation of the Surface Water Distribution in North-Central Namibia Based on MODIS and AMSR Series

Semi-arid North-central Namibia has high potential for rice cultivation because large seasonal wetlands (oshana) form during the rainy season. Evaluating the distribution of surface water would reveal the area potentially suitable for rice cultivation. In this study, we detected the distribution of surface water with high spatial and temporal resolution by using two types of complementary satellite data: MODIS (MODerate-resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer–Earth Observing System), using AMSR2 after AMSR-E became unavailable. We combined the modified normalized-difference water index (MNDWI) from the MODIS data with the normalized-difference polarization index (NDPI) from the AMSR-E and AMSR2 data to determine the area of surface water. We developed a simple gap-filling method (“database unmixing”) with the two indices, thereby providing daily 500-m-resolution MNDWI maps of north-central Namibia regardless of whether the sky was clear. Moreover, through receiver-operator characteristics (ROC) analysis, we determined the threshold MNDWI (−0.316) for wetlands. Using ROC analysis, MNDWI had moderate performance (the area under the ROC curve was 0.747), and the recognition error for seasonal wetlands and dry land was 21.2%. The threshold MNDWI let us calculate probability of water presence (PWP) maps for the rainy season and the whole year. The PWP maps revealed the total area potentially suitable for rice cultivation: 1255 km2 (1.6% of the study area).

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.

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.

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.

Atmospheric corrections of passive microwave data for estimating land surface temperature

Quantitative analysis of the atmospheric effects on observations made by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) has been performed. The differences between observed brightness temperatures at the top of the atmosphere and at the bottom of the atmosphere were analyzed using a database of simulated observations, which were configured to replicate AMSR-E data. The differences between observed brightness temperatures at the top of the atmosphere and land surface-emitted brightness temperatures were also computed. Quantitative results show that the atmosphere has different effects on brightness temperatures in different AMSR-E channels. Atmospheric effects can be neglected at 6.925 and 10.65 GHz, when the standard deviation is less than 1 K. However, at other frequencies and polarizations, atmospheric effects on observations should not be neglected. An atmospheric correction algorithm was developed at 18.7 GHz vertical polarization, based on the classic split-window algorithm used in thermal remote sensing. Land surface emission can be estimated with RMSE = 0.99 K using the proposed method. Using the known land surface emissivity, Land Surface Temperature (LST) can be retrieved. The RMSE of retrieved LST is 1.17 K using the simulated data.

Estimation of thin ice thickness from AMSR-E data in the Chukchi Sea

In this study, we have developed an algorithm for estimating thin ice thickness in the Chukchi Sea of the Arctic Ocean using Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) data. The algorithm is based on comparisons between the polarization ratio (PR) of AMSR-E brightness temperatures from the 89 and 36 GHz channels (PR89 and PR36) and the thermal ice thickness. The thermal ice thickness is estimated from a heat budget calculation using the ice surface temperature from clear-sky Moderate-Resolution Imaging Spectroradiometer (MODIS) infrared data. Whereas coastal polynyas have been the main target of previous algorithms, this algorithm is also applicable for marginal ice zones. AMSR-E has twice the spatial resolution of Special Sensor Microwave/Imager (SSM/I) data and can therefore resolve polynyas at a smaller scale. Although the spatial resolution of the 89 GHz data (6.25 km) is twice that of the 36 GHz data (12.5 km), the 89 GHz data can be contaminated by atmospheric water vapour. We propose an exclusion method of data affected by water vapour to resolve this issue. A combined algorithm of thin ice and ice concentration is also discussed, in which the ice thickness can be estimated independently from the open water fraction in grid cells with less than 100% ice concentration. The PR–thickness relationship in this study is somewhat different from previous studies, which is likely due to the difference in prevailing ice types caused by background environmental conditions.

RETRIEVING SURFACE SOIL MOISTURE FROM MODIS AND AMSR-E DATA: A CASE STUDY IN TAIWAN

Abstract. Soil moisture is a key factor that controls the exchange of water between land surface evaporation and plant transpiration. Information on surface soil moisture variations in both time and spatial domains is important for numerous applications, especially agricultural and environmental monitoring. This study aimed at retrieving surface soil moisture from daily MODIS and AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) data. A case study was conducted in Taiwan for 2009. Data were processed using the Temperature Vegetation Dryness Index (TVDI). This index is developed based on an empirical analysis of the relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) data. The comparison between the TVDI results and the daily precipitation data collected from meteorological stations throughout the study area indicated that there were close relationships between the two datasets. The TDVI results (values range from 0 to 1) were converted to the same unit with the AMSR-E soil moisture data (i.e., g cm-3) by linear regression analysis between these two datasets. The results achieved by this analysis were soil moisture maps that had a better spatial resolution (1 km × 1 km) than the AMSE-E soil moisture data (25 km × 25 km). The soil moisture achieved by TVDI – AMSR-E regression analysis showed the comparable spatial patterns with those from the AMSR-E soil moisture data. A quantitative analysis between the soil moisture (deduced from TVDI-AMSR-E analysis) and the AMSR-E soil moisture data also reaffirmed significant correlations between the two datasets. This study has demonstrated a method of surface soil moisture retrieval from MODIS and AMSR-E data.

An AMSR-E data unmixing method for monitoring flood and waterlogging disaster

Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster. Although spectral remote sensing data have many advantages for ground information observation, such as real time and high spatial resolution, they are often interfered by clouds, haze and rain. As a result, it is very difficult to retrieve ground information from spectral remote sensing data under those conditions. Compared with spectral remote sensing technique, passive microwave remote sensing technique has obvious superiority in most weather conditions. However, the main drawback of passive microwave remote sensing is the extreme low spatial resolution. Considering the wide application of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data, an AMSR-E data unmixing method was proposed in this paper based on Bellerby’s algorithm. By utilizing the surface type classification results with high spatial resolution, the proposed unmixing method can obtain the component brightness temperature and corresponding spatial position distribution, which effectively improve the spatial resolution of passive microwave remote sensing data. Through researching the AMSR-E unmixed data of Yongji County, Jilin Provinc, Northeast China after the worst flood and waterlogging disaster occurred on July 28, 2010, the experimental results demonstrated that the AMSR-E unmixed data could effectively evaluate the flood and waterlogging disaster.

Initializing a regional climate model with satellite-derived soil moisture

Regional climate simulations over Europe were initialized with soil moisture derived from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) in order to assess the model accuracy in predicting soil moisture and other components of the hydrological cycle. The AMSR-E soil moisture initially showed systematic differences with model-predicted soil moisture. For proper initialization the AMSR-E product had to be rescaled and after that vertically profiled. To retrieve a root zone soil moisture profile, we tested the application of an exponential filter. The smoothing through the layers of the ERA-Interim soil moisture profile was applied to the rescaled AMSR-E surface soil moisture. The filter performed very well for that part of the data set where the top layer is positively correlated with the deeper layers. After the preparation of the soil moisture fields, several sensitivity simulations were performed. The model's soil moisture was replaced with the vertically profiled AMSR-E soil moisture at different initial times for a dry summer (2003) and a wet summer (2005). In general, the surplus of soil moisture in the AMSR-E data resulted in a better performance in predicting temperature when compared with observations. This finding was more pronounced in the dry summer of 2003 when the model results appeared very sensitive to land-atmosphere feedbacks. Our results suggest that in dry years, the use of appropriate observed soil moisture may help more to reduce modeled surface temperatures than inducing additional rainfall in the model. Using the AMSR-E product led to a decrease in areal extent sensitive to land-atmosphere interactions. Copyright 2011 by the American Geophysical Union.

Comparative analysis of microwave brightness temperature data in Northeast China using AMSR-E and MWRI products

With such significant advantages as all-day observation, penetrability and all-weather coverage, passive microwave remote sensing technique has been widely applied in the research of global environmental change. As the satellite-based passive microwave remote sensor, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) loaded on NASA’s (National Aeronautics and Space Administration of USA) Aqua satellite has been popularly used in the field of microwave observation. The Microwave Radiation Imager (MWRI) loaded on the Chinese FengYun-3A (FY-3A) satellite is an AMSR-E-like conical scanning microwave sensor, but there are few reports about MWRI data. This paper firstly proposed an optimal spatial position matching algorithm from rough to exact for the position matching between AMSR-E and MWRI data, then taking Northeast China as an example, comparatively analyzed the microwave brightness temperature data derived from AMSR-E and MWRI. The results show that when the antenna footprints of the two sensors are filled with either full water, or full land, or mixed land and water with approximate proportion, the errors of brightness temperature between AMSR-E and MWRI are usually in the range from −10 K to +10 K. In general, the residual values of brightness temperature between the two microwave sensors with the same spatial resolution are in the range of ±3 K. Because the spatial resolution of AMSR-E is three times as high as that of MWRI, the results indicate that the quality of MWRI data is better. The research can provide useful information for the MWRI data application and microwave unmixing method in the future.