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Sea ice monitoring is key to analyzing the Earth's climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused by differences in PMRs should be eliminated to obtain objective SIE trends. Most sea ice products have been analyzed for long-term trends with a bias adjustment based on the coarse resolution special sensor microwave imager (SSM/I) in operation for the longest period. However, since 2002, Japanese microwave radiometers of the Advanced Microwave Scanning Radiometer (AMSR) series, which have the highest spatial resolution in PMR, have been available. In this study, we developed standardization techniques for processing SIE including calibration of the brightness temperature (TB), tuning the sea ice concentration (SIC) algorithm, and adjusting the SIC threshold to retrieve a consistent SIE trend based on the AMSR for the Earth Observing System (AMSR-E, one of the AMSR that operated from May 2002 to October 2011). Analysis results showed that the root-mean-square error between AMSR-E SICs and those of moderate resolution imaging spectroradiometer (MODIS) was 15%. In this study, SIE was defined as the sum of the areas where the AMSR-E SIC was >15%. When retrieving SIE, we adjusted the SIC threshold for each PMR to be consistent with the SIE calculated based on the 15% SIC threshold for AMSR-E. We then calculated a time-series of the SIE trends over approximately 45 years using the adjusted SIE data. Therefore, we revealed the dramatic decrease in global sea ice extent since 1978. This technique enables retrieval of more accurate long-term sea ice trends for more than half a century in the future. [ABSTRACT FROM AUTHOR]

This study delves into the synergy between remote sensing and satellite gravimetry, focusing on the utilization of Advanced Microwave Scanning Radiometer (AMSR-E) data for modeling delta Total Water Storage (ΔTWS) values derived from the GRACE mission. Various machine learning algorithms were employed to investigate the concordance between Gravity Recovery and Climate Experiment (GRACE) and AMSR-E observations. Despite the limited correlation in circumpolar permafrost areas, ΔTWS was successfully modeled with an accuracy of a Root Mean Square Error (RMSE) of 3.5 cm. The Amazon region exhibited a notable model error, attributed to significant ΔTWS amplitude; the overall model quality was affirmed by Normalized Root Mean Square Error (NRMSE) and Nash-Sutcliffe Efficiency (NSE) metrics. Importantly, the effectiveness of AMSR-E Soil Moisture (SM) data, encompassing C (frequency of 4–8 GHz) and X (frequency of 8–12 GHz) ranges (~0.04 m and ~0.03 m wavelength, respectively) in modeling ΔTWS, even in heavily forested equatorial regions, was demonstrated. [ABSTRACT FROM AUTHOR]

Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) LST data products are usually desired for many applications. Instead of developing and launching new high-resolution satellite sensors for LST observations, a more economical and practical way is to develop proper methodologies to derive high-resolution LSTs from available Low-Resolution (LR) datasets. This study explores different algorithms to downscale low-resolution LST data to a high resolution. The existing regression-based downscaling methods usually require simultaneous observations and ancillary data. The Super-Resolution Reconstruction (SRR) method developed for traditional image enhancement can be applicable to high-resolution LST generation. For the first time, we adapted the SRR method for LST data. We specifically built a unique database of LSTs for the example-based SRR method. After deriving the LST data from the coarse-resolution passive microwave observations, the AMSR-E at 25 km and/or AMSR-2 at 10 km, we developed an algorithm to downscale them to a 1 km spatial resolution with the SRR method. The SRR downscaling algorithm can be implemented to obtain high-resolution LSTs without auxiliary data or any concurrent observations. The high-resolution LSTs are validated and evaluated with the ground measurements from the Surface Radiation (SURFRAD) Budget Network. The results demonstrate that the downscaled microwave LSTs have a high correlation coefficient of over 0.92, a small bias of less than 0.5 K, but a large Root Mean Square Error (RMSE) of about 4 K, which is similar to the original microwave LST, so the errors in the downscaled LST could have been inherited from the original microwave LSTs. The validation results also indicate that the example-based method shows a better performance than the self-similarity-based algorithm. [ABSTRACT FROM AUTHOR]

The study presents a compatibility analysis of gravimetric observations with passive microwave observations. Monitoring the variability of soil water content is one of the essential issues in climate-related research. Total water storage changes (ΔTWS) observed by Gravity Recovery and Climate Experiment (GRACE), enables the creation of many applications in hydrological monitoring. Soil moisture (SM) is a critical variable in hydrological studies. Advanced Microwave Scanning Radiometer (AMSR-E) satellite products provided unique observations on this variable in near-daily time resolutions. The study used maximum covariance analysis (MCA) to extract principal components for ΔTWS and SM signals. The analysis was carried out for the global area, dividing the discussion into individual continents. The amplitudes of gravimetric and microwave signals were computed via the complex empirical orthogonal function (EOF) and the complex conjugate EOF* to determine the regions for detailed comparison. Similarities and differences in signal convergence results were compared with land cover data describing soil conditions, vegetation cover, urbanization status, and cultivated land. Convergence was determined using Pearson correlation coefficients and cross-correlation. In order to compare ΔTWS and SM in individual seasons, ΔTWS observations were normalized. Results show that naturally forested areas and large open spaces used for agriculture support the compatibility between GRACE and AMSRE observations and are characterized by a good Pearson correlation coefficient >0.8. Subpolar regions with permafrost present constraints for AMSR-E observations and have little convergence with GRACE observations. [ABSTRACT FROM AUTHOR]

In this article, we explore the soil water effect on vegetation growth in west-central Africa using remotely sensed near-surface soil moisture content (nSMC), terrestrial water storage anomaly (TWSA), and leaf area index (LAI). TWSA is the only data to reflect deeper soil water used by perennial plants with their deep root systems, which microwave sensors cannot observe. However, deep groundwater complicates investigating the water availability of plants using TWSA. We confirmed that the TWSA's slope during the dry season, which might be caused by vegetation transpiration, was steeper towards equatorial regions. Our results show that the relationship between LAI and the TWSA's inclination during the dry season was negatively correlated in areas where the LAI was below 2 and the precipitation was less than 40 mm/month. This finding might enable us to investigate the water availability of perennial plants by focusing on the TWSA's slope during the dry season. [ABSTRACT FROM AUTHOR]

Microwave radiometry can be used to measure vegetation water content through vegetation optical depth (VOD). VOD can vary due to changes in water stress alone, but also scales with aboveground biomass. Several studies have therefore interpreted VOD temporal anomalies as linearly proportional to biomass anomalies, neglecting the influence of water stress. Here, we explicitly test this assumption using a new annual‐resolution data set of biomass derived from optical, radar, and lidar remote sensing. Both L‐band and X‐band VOD datasets are tested. Although VOD and biomass variations are highly correlated in space, their temporal anomalies are almost uncorrelated. At regional scale, it is as common for VOD anomalies to be more correlated to root‐zone soil moisture anomalies (a proxy for water stress) than to biomass anomalies as for the opposite to occur. Care should therefore be taken when deriving biomass anomalies from VOD, especially in the absence of any large‐scale biomass disturbances. Plain Language Summary: Satellite measurements known as vegetation optical depth (VOD) measure how much water is in plants. Because plants with more mass can hold more water, changes in VOD are used to determine biomass changes, ignoring the fact that changes in how dry the plants are also affect VOD. We use an alternative data set for biomass to test this approach. At a given location, VOD and biomass do not vary in the same pattern from year to year and for many types of plants, VOD seems to follow soil moisture (which should co‐vary with the fraction of water in plants) better than it follows biomass itself. These results help clarify when VOD can and can't provide information about biomass. Key Points: We test the common literature assumption that biomass interannual variability (IAV) follows vegetation optical depth (VOD) IAVVOD‐biomass relationship is driven almost entirely by spatial correlation, rather than temporal similarityRegional‐scale VOD IAV is often more sensitive to soil moisture than biomass, due to influence of plant water stress on VOD [ABSTRACT FROM AUTHOR]

The reliable estimation of soil moisture on spatial and temporal scales is of fundamental importance in improving the impact assessment of drought and flood on plant productivity and enhancing our understanding of the link between the water and biogeochemical cycles. Currently, remotely sensed data can offer a chance to improve spatial variability, especially in environments with scarce ground‐based data. To obtain soil moisture information with high resolution and high precision in such mountainous areas as the Huaihe River Basin, we combined the advantages of MODerate‐resolution Imaging Spectroradiometer (MODIS) optical remote sensing sensor that has high spatial resolution and sensitive characteristics to surface covering type and Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR‐E) passive microwave sensor that has high temporal resolution and the characteristics of small interference by clouds. Five years of AMSR‐E and MODIS data (2006–2010) were fused based on the wavelet transform (WT) method. The soil moisture in the Huaihe River Basin was extracted using these fused remotely sensed data. The results showed that the soil moisture from AMSR‐E and MODIS fusion data could capture soil moisture dynamics well. The accuracies of soil moisture from AMSR‐E and MODIS fusion data based on WT were better than those from single remote sensing data. The soil moisture from MODIS and AMSR‐E fusion data was more sensitive to the season, especially in spring, summer, and autumn. The accuracy of soil moisture from MODIS and AMSR‐E fusion data in time and space varied in the Huaihe Basin. From the time series, high accuracies of soil moisture were observed in the spring of 2009 and in the spring and winter of 2010. From the spatial series, high accuracies of soil moisture were found in Zhumadian and Bozhou stations, and the worst accuracies were observed in Huaiyin and Shangqiu stations, which were primarily related to some factors such as local topography, vegetation coverage, and precipitation. These findings will provide interesting insights that can be useful for developing measures to prevent drought and flood disasters on a regional scale in China. [ABSTRACT FROM AUTHOR]

Microwave (MW) remote sensing has the potential to obtain all‐weather land surface temperature (LST) and serves as a complement to the thermal‐infrared (TIR) LST under cloudy sky conditions. However, the accuracy of MW LST is generally lower than that of TIR LST, making the retrieval of highly accurate all‐weather LST a challenging task. We propose an empirical algorithm for retrieving LST from the Advanced Microwave Scanning Radiometer (AMSR‐E) brightness temperature (BT) data. First, we constructed a comprehensive classification system of environmental variables (CCSEV), allowing for the influence of topography, land cover, solar radiation, and atmospheric condition on the spatiotemporal distribution of LST, then the LST was expressed as a function of the combination of different AMSR‐E channels for each CCSEV class. When performing the testing with the data from 2005, 2009 and 2011, the accuracy is 3.27 K, 2.65 K and 3.48 K in the daytime and 2.94 K, 2.63 K, 2.15 K at nighttime, respectively. The proposed algorithm was compared to an existing algorithm developed for China without considering the topography. The result shows that the accuracy of LST has improved by 2.81 K in the daytime and 2.14 K at nighttime in China, compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST. The verification at the Naqu sites in the Qinghai‐Tibet Plateau shows that the accuracy has improved by 1–2 K in the daytime and 0.7–1 K at nighttime. These results indicate that the developed algorithm is universal and accurate and benefits the retrieval of accurate all‐weather LST. Key Points: Topographic effect is considered for the first time in the microwave land surface temperature retrievalA classification system involving various factors on land surface temperature distribution is constructedAccuracy of the AMSR‐E LST in China retrieved from the proposed algorithm has been obviously improved compared to the previous algorithms [ABSTRACT FROM AUTHOR]

The frequent and persistent cloud cover in the Arctic limits the extent to which sea surface temperature (SST) can be retrieved from thermal infrared (IR) satellite sensors. Passive microwave (PMW) observations provide highly complementary information to IR, enabling measurements through non-precipitating clouds, although at a coarser spatial resolution. The differences in coverage, accuracy, footprint size, spatial resolution and error characteristics between IR and PMW SSTs require a systematic assessment of how to best combine IR and PMW SST retrievals. This is provided in this study on the basis of the ESA-CCI PMW SST climate data record (CDR) and an existing IR-based gap-free SST and sea ice surface temperature CDR covering the Arctic (> 58 ° N), where cloud cover is a serious limitation to IR sensors. An important step towards a combined IR and PMW SST CDR is to correct for systematic biases in the PMW and IR SST datasets relative to each other. The PMW SSTs show reduced biases against in situ SSTs compared to the IR SSTs, but for consistency with time periods when no Arctic PMW SSTs were available, the PMW SSTs have been adjusted to the IR SSTs in this study. This is done using a dynamic bias correction to generate a consistent combined IR and PMW Arctic SST CDR for the period 2002–2017. Including PMW SSTs reduces the standard deviations from 0.54 °C, 0.55 °C and 0.47 °C to 0.47 °C, 0.54 °C and 0.41 °C against drifters, moorings and Argo floats, respectively. The improved performance is seen in almost all regions (including those already covered by IR observations), with the largest improvement in IR data sparse regions. The average theoretical uncertainty reduces by 0.08 °C, which is in good agreement with the observed improvement in the standard deviation against drifters. The results are very promising and expected to improve even further in the future with the launch of the Copernicus Imaging Microwave Radiometer (CIMR), which will enable PMW SST retrievals with lower uncertainties and much closer to coasts and sea ice (where the largest uncertainties arise) than what is possible with previous and current PMW radiometers. • Optimal blending of infrared and microwave sea surface temperatures in the Arctic. • Improved spatial coverage when including microwave satellite observations. • Reduced standard deviations against in situ observations in almost all regions. [ABSTRACT FROM AUTHOR]

The near-surface soil freeze–thaw (FT) transition is an important factor affecting land-atmosphere exchanges, hydrology and carbon cycles. Thus, effectively monitoring the temporal–spatial changes of soil FT processes is crucial to climate change and environment research. Several approaches have been developed to detect the soil FT state from satellite observations. The discriminant function algorithm (DFA) uses temperature and emissivity information from Advanced Microwave Scanning Radiometer Enhanced (AMSR-E) passive microwave satellite observations. Although it is well validated, it was shown to be insufficiently robust for all land conditions. In this study, we use in-situ observed soil temperature and AMSR-E brightness temperature to parameterize the DFA for soil FT state detection. We use the in-situ soil temperature records at 5 cm selected from available dense networks in the Northern Hemisphere as a reference. Considering the distinction between ascending and descending orbits, two different sets of parameters were acquired for each frequency pair. The validation results indicate that the overall discriminant accuracy of the new function can reach 90%. We further compared the Advanced Microwave Scanning Radiometer 2 discriminant results using the new function to the Soil Moisture Active Passive freeze/thaw product, and a reasonable consistency between them was found. [ABSTRACT FROM AUTHOR]

Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source-driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink-driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems. • Estimating Gross Primary Production (GPP) based on Vegetation Optical Depth (VOD) • Relationship between VOD and GPP can be described through a differential equation. • Further, we use Generalized Additive Models for estimating GPP from VOD. [ABSTRACT FROM AUTHOR]

Global monitoring of soil moisture and snow is now available through various satellite observations from optical, microwave, and gravitational sensors. However, very few modeling frameworks exist that conjointly use the above sensors to produce mutually and physically consistent earth system records. To this goal, a prototype of multi-sensor land data assimilation system is developed by linking the Community Land Model version 4 (CLM4) and a series of forward models with the Data Assimilation Research Testbed (DART). The deterministic Ensemble Adjustment Kalman Filter (EAKF) within the DART is utilized to estimate global soil moisture and snow by assimilating brightness temperature, snow cover fraction, and daily total water storage observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Moderate Resolution Imaging Spectroradiometer (MODIS), and Gravity Recovery and Climate Experiment (GRACE), respectively. A 40-member of Community Atmosphere Model version 4 (CAM4) reanalysis is adopted to introduce ensemble spread in CLM4 land states and some methods are used to reduce the computational load. Data assimilation with different combinations of sensors is implemented for 2003–2009 to investigate individual contributions from different satellite observations. Evaluation results and cross-comparison of open-loop and data assimilation cases suggest that 1) assimilation of MODIS snow cover fraction slightly improves snow estimation in mid and high latitudes; 2) lower and higher frequencies of AMSR-E brightness temperature play complementary roles in improving global soil moisture and snow estimation; 3) assimilation of GRACE tends to degrade soil moisture estimation but poses potential in improving snow depth estimation in most high-latitude regions. Generally, the combination of MODIS, GRACE, and AMSR-E observations with regard to spatial locations holds promise to provide a robust global soil moisture and snow estimation through the multi-sensor land data assimilation system. [ABSTRACT FROM AUTHOR]

Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution. [ABSTRACT FROM AUTHOR]

At the global scale, the uptake of atmospheric carbon dioxide by terrestrial ecosystems through photosynthesis is commonly estimated through vegetation indices or biophysical properties derived from optical remote sensing data. Microwave observations of vegetated areas are sensitive to different components of the vegetation layer than observations in the optical domain and may therefore provide complementary information on the vegetation state, which may be used in the estimation of Gross Primary Production (GPP). However, the relation between GPP and Vegetation Optical Depth (VOD), a biophysical quantity derived from microwave observations, is not yet known. This study aims to explore the relationship between VOD and GPP. VOD data were taken from different frequencies (L-, C-, and X-band) and from both active and passive microwave sensors, including the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS) mission, the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E) and a merged VOD data set from various passive microwave sensors. VOD data were compared against FLUXCOM GPP and Solar-Induced chlorophyll Fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). FLUXCOM GPP estimates are based on the upscaling of flux tower GPP observations using optical satellite data, while SIF observations present a measure of photosynthetic activity and are often used as a proxy for GPP. For relating VOD to GPP, three variables were analyzed: original VOD time series, temporal changes in VOD (ΔVOD), and positive changes in VOD (ΔVOD ≥0 ). Results show widespread positive correlations between VOD and GPP with some negative correlations mainly occurring in dry and wet regions for active and passive VOD, respectively. Correlations between VOD and GPP were similar or higher than between VOD and SIF. When comparing the three variables for relating VOD to GPP, correlations with GPP were higher for the original VOD time series than for ΔVOD or ΔVOD ≥0 in case of sparsely to moderately vegetated areas and evergreen forests, while the opposite was true for deciduous forests. Results suggest that original VOD time series should be used jointly with changes in VOD for the estimation of GPP across biomes, which may further benefit from combining active and passive VOD data. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) and vegetation optical depth (VOD) are essential variables in the terrestrial ecosystem. The multi-frequency radiometers AMSR-E and AMSR2 provide >20 years of data records, enabling the development of long-term SM and VOD products. Most of the current retrieval algorithms either only focus on SM or VOD, and generally ignore the polarization or simplify the frequency dependence of vegetation effects for reducing the unknowns and facilitating the retrieval process, limiting the synergic applicability of VOD and SM products in the soil-plant-atmosphere continuum. In this study, a new global SM and frequency- and polarization-dependent VOD product from 2002 to 2021 was developed using the multi-channel collaborative algorithm (MCCA), based on the inter-calibrated AMSR-E/2 multi-frequency passive microwave measurements. The MCCA algorithm comprehensively considers the physical relationship between multiple microwave channels and could retrieve frequency- and polarization-dependent VOD while considering the accuracy of the SM retrievals. In the overall comparison with other SM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) over 25 dense SM networks, MCCA achieved the best scores in terms of root mean square error (RMSE = 0.074 m3/m3), unbiased root mean square error (ubRMSE = 0.073 m3/m3) and bias (0.007 m3/m3), and presented slightly lower value of Pearson's correlation coefficient (R = 0.709) than LPRM-X (R = 0.735). For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs, especially for the H-polarized VODs, exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions of the globe. In particular, MCCA-derived VODs can physically present reasonable variations across the microwave spectrum (values of VOD increase with microwave frequency), which is superior to the LPRM and VODCA products. It is expected that the MCCA algorithm can be extended to the observations of the ongoing AMSR2 or other similar satellite missions with multi-frequency capability, such as FY-3B/C/D/F/G or the upcoming AMSR3 and CMIR missions. • A physically consistent SM product over two decades is developed using the MCCA. • MCCA-derived SM shows the best accuracy among the six AMSR SM products. • MCCA-derived VODs present reasonable variations across the microwave spectrum. • H-polarized VOD is more sensitive to vegetation in terms of biomass and greenness than V-polarized VOD. [ABSTRACT FROM AUTHOR]

Broad scale monitoring of inland waters is essential to research on carbon and water cycles, and for application in the monitoring of disasters including floods and droughts on various spatial and temporal scales. Satellite remote sensing using spatiotemporal data fusion (STF) has recently attracted attention as a way of simultaneously describing spatial heterogeneity and tracking the temporal variability of inland waters. However, existing STF approaches have limitations in describing abrupt temporal changes, integrating “dissimilar” datasets (i.e., fusions between microwave and optical data), and compiling long-term, frequent STF datasets. To overcome these limitations, in this study we developed and evaluated a lookup table (LUT)-based STF, termed database unmixing (DBUX), using multiple types of satellite data (AMSR series, MODIS, and Landsat), and applied it to semi-arid seasonal wetlands in Namibia. The results show that DBUX is: 1) flexible in integrating optical data (MODIS or Landsat) with microwave (AMSR series) and seasonal (day of year) information; 2) able to generate long-term, frequent Landsat-like datasets; and 3) more reliable than an existing approach (spatial and temporal adaptive reflectance fusion model; STARFM) for tracking dynamic temporal variations in seasonal wetlands. Water maps retrieved from the resulting STF dataset for the wetlands had a 30-m spatial resolution and a temporal frequency of 1 or 2 days, and the dataset covered from 2002 to 2015. The time series water maps accurately described both seasonal and interannual changes in the wetlands, and could act as a basis for understanding the hydrological features of the region. Further studies are required to enable application of DBUX in other regions, and for other landscapes with different satellite sensor combinations. [ABSTRACT FROM AUTHOR]

Across Eastern Africa, croplands cover 45 million ha. The regional economy is heavily dependent on small holder traditional rain-fed peasant agriculture (up to 90%), which is vulnerable to extreme weather events such as drought and floods that leads to food insecurity. Agricultural production in the region is moisture limited. Weather station data are scarce and access is limited, while optical satellite data are obscured by heavy clouds limiting their value to study cropland dynamics. Here, we characterized cropland dynamics in Eastern Africa for 2003-2015 using precipitation data from Tropical Rainfall Measuring Mission (TRMM) and a passive microwave dataset of land surface variables that blends data from the Advanced Microwave Scanning Radiometer (AMSR) on the Earth Observing System (AMSR-E) from 2002 to 2011 with data from AMSR2 from 2012 to 2015 with a Chinese microwave radiometer to fill the gap. These time series were analyzed in terms of either cumulative precipitable water vapor-days (CVDs) or cumulative actual evapotranspiration-days (CETaDs), rather than as days of the year. Time series of the land surface variables displayed unimodal seasonality at study sites in Ethiopia and South Sudan, in contrast to bimodality at sites in Tanzania. Interannual moisture variability was at its highest at the beginning of the growing season affecting planting times of crops, while it was lowest at the time of peak moisture. Actual evapotranspiration (ETa) from the simple surface energy balance (SSEB) model was sensitive to track both unimodal and bimodal rainfall patterns. ETa as a function of CETaD was better fitted by a quadratic model (r² > 0.8) than precipitable water vapor was by CVDs (r² > 0.6). Moisture time to peak (MTP) for the land surface variables showed strong, logical correspondence among variables (r² > 0.73). Land surface parameters responded to El Niño-Southern Oscillation and the Indian Ocean Dipole forcings. Area under the curve of the diel difference in vegetation optical depth showed correspondence to crop production and yield data collected by local offices, but not to the data reported at the national scale. A long-term seasonal Mann-Kendall rainfall trend showed a significant decrease for Ethiopia, while the decrement was not significant for Tanzania. While there is significant potential for passive microwave data to augment cropland status and food security monitoring efforts in the region, more research is needed before these data can be used in an operational environment. [ABSTRACT FROM AUTHOR]

Melting snow provides an essential source of water in many regions of the world and can also contribute to devastating, wide-scale flooding. Global datasets of recorded passive microwave emissions provide non-destructive, daily information on snow processes including the presence of liquid water in the snow, which can be an indicator of snowmelt. The objective of this research is to test the sensitivity of the emission signal as it relates to the spatial distribution of liquid water content in the snowpack. This signal response was evaluated over an area approximately the size of a microwave pixel to assess whether a relationship exists between the aerial extent of wet snow and the magnitude of the T B response. A sensitivity analysis was performed using a high-resolution, physically based snow-emission model to simulate microwave emissions. The signal response to wet snow was evaluated given a range of spatially distributed snowpack conditions. Daily snow states were simulated for a 9-year period using a high-resolution (50 m) energy balance snow model over a 34 × 34 km domain. These data were fed into a microwave emission model to simulate brightness temperatures. A near-linear relationship was found between the T B signal response over a spatially heterogeneous snowpack and the percent area with liquid water content (LWC) present. The results were confirmed by evaluating actual wet snow events over a 9-year period. The model output was also compared to AMSR-E passive microwave satellite data and discharge data at a basin outlet within the study area. The results are used to help understand the impact of spatially distributed snowmelt as detected by passive microwave data. [ABSTRACT FROM AUTHOR]

Digitizing the land surface temperature (Ts) and surface soil moisture (mv) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrievingTsandmvusing the dual-polarizedTbof Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band (6.9 GHz) based on the simplified radiative transfer equation. Validation using in situTsandmvin southern China showed the average root mean square errors (RMSE) ofTsandmvretrievals reach 2.42 K (R2= 0.61,n= 351) and 0.025 g cm−3(R2= 0.68,n= 663), respectively. The results were also validated using global in situTs(n= 2362) andmv(n= 1657) of International Soil Moisture Network. The corresponding RMSE are 3.44 k (R2= 0.86) and 0.039 g cm−3(R2= 0.83), respectively. The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging SpectroradiometerTs(R2= 0.57; RMSE = 2.91 k) and ECV_SMmv(R2= 0.51; RMSE = 0.045 g cm−3), respectively. Overall, this paper indicates an effective way to jointly modelingTsandmvusing passive microwave remote sensing. [ABSTRACT FROM AUTHOR]

Microwave remote sensing techniques are used, among others, for temporally and spatially highly-resolved observations of land-surface properties, e.g., for the management of agricultural productivity and water resource, as well as to improve the performances of numerical weather prediction and climate simulations with soil moisture data. In this context, the effective dielectric constant of the soil is a key variable to quantify the land surface properties. We propose a new approach for the effective dielectric constant of the multiphase soil that is based on an arithmetic average of the dielectric constants of the land-surface components with damping. The results show, on average, better agreement with experimental data than previous approaches. Furthermore, the proposed new model overcomes the theoretical limitation of previous models in the incorporation of non-physical parameters to simulate measured data experimentally with satisfactory accuracy. For microwave remote sensing such as SMAP (Soil Moisture Active Passive), SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), the physical-based model in our study showed a 23-35% RMSE (root-mean-square error) reduction compared to the most prevalent refractive mixing model in the prediction of the dielectric constant for the real and imaginary part, respectively. Furthermore, in radiowave bands used in portable soil sensors such as TDR (time-domain reflectometer) and GPR (ground-penetrating radar) the new dielectric mixing model reduced RMSE by up to 53% in the prediction of the dielectric constant. We found that the permittivity over the saturation point (porosity of dry soil) has a very different and varying pattern compared to that measured in the unsaturated condition. However, in our study, this pattern was mathematically derived from the same mixing rule applied for the unsaturated condition. It is expected that the new dielectric mixing model might help to improve the accuracy of flood monitoring by satellite. [ABSTRACT FROM AUTHOR]

Knowledge of soil moisture, however varied in space and time, is critical for planning land use in water-limited drylands. Although satellite observations provide soil moisture instantaneously at large scale, few studies have addressed the application of satellite soil moisture retrieval in dryland areas with changing land use. In this study, we propose a method to evaluate the spatiotemporal variance of satellite retrieval based on the evaluation of soil moisture retrievals from the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E). The operation period of this instrument (June 2002 to October 2011) covers the re-vegetation of the semi-arid Loess Plateau. Our study found that AMSR-E retrievals posted by the Japanese Aerospace Exploration Agency (JAXA) capture the spatiotemporal variance of soil moisture in the Loess Plateau, as well as the in-situ measured soil moisture decrease following the massive re-vegetation– despite it differing from the in-situ measurement in spatial scale. Soil moisture decrease in response to the massive re-vegetation occurred in a transition zone of grass-forest ecosystems with annual precipitation between 450 and 550 mm. Our study suggests that current AMSR-E JAXA retrievals are an essential alternative to field measurement and model simulation, because they can identify the location and spatial extent of areas where soil moisture is sensitive to climate change and managed re-vegetation. To avoid the overconsumption of soil water and limited plant growth in transition zone, managers of dryland areas should pay special attention to species selection. [ABSTRACT FROM AUTHOR]

Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave remote sensing, very few studies have estimated the seasonal GrIS snow depth using this technique. In this study, to estimate seasonal snowpack on GrIS, we investigated the microwave property and optimum physical parameters. We used our microwave radiative transfer model to create a lookup table and a simple satellite retrieval algorithm to estimate seasonal snow depth on GrIS in spring, based on the microwave satellite brightness temperature from AMSR-E and AMSR2. Our research suggests there is potential for estimating snow depth based solely on GrIS passive microwave remote sensing data. We validated these estimates against in situ snow depths at several sites and compared them with the snow spatial distributions over the entire GrIS of several major products (ERA-interim, MAR ver. 5.3.1 and GLDAS-CLM) that evaluate snow depth. [ABSTRACT FROM AUTHOR]

Droughts, typically caused by the deficiencies of precipitation and soil moisture, affect water resources and agriculture. As soil moisture is of key importance in understanding the interaction between the atmosphere and Earth’s surface, it can be used to monitor droughts. In this study, a High resolution Soil Moisture Drought Index (HSMDI) was proposed and evaluated for meteorological, agricultural, and hydrological droughts. HSMDI was developed using the 1 km downscaled soil moisture data produced from the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) from 2003 to 2011 (March to November) over the Korean peninsula. Seven products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellite sensors were used to downscale AMSR-E soil moisture based on random forest machine learning. The downscaled 1 km soil moisture was correlated well with both in situ and AMSR-E soil moisture with the mean coefficient of determination ( R 2 ) of 0.29 and 0.59, respectively. The Standardized Precipitation Index (SPI) with time scales from 1 to 12 months, crop yields (for sesame, highland radish, and highland napa cabbage) and streamflow data were used to validate HSMDI for various types of droughts. The results showed that HSMDI depicted meteorological drought well, especially during the dry season, with a similar pattern with the 3-month SPI. However, the performance fluctuated a bit during the wet season possibly due to the limited availability of optical sensor data and heterogeneous land covers around the stations. HSMDI also showed high correlation with crop yield data, in particular the highland radish and napa cabbage cultivated in non-irrigated regions with a mean R 2 of 0.77. However, HSMDI did not monitor streamflow well for hydrological drought presenting a various range of correlations with streamflow data (from 0.03 to 0.83). [ABSTRACT FROM AUTHOR]

For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records. [ABSTRACT FROM AUTHOR]

Uncertainties in model parameters can easily result in systematic differences between model states and observations, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this research, a soil moisture assimilation scheme is developed to jointly assimilate AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System) brightness temperature (TB) and MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products, which also corrects model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter (DEnKS). Common Land Model (CoLM) and a Radiative Transfer Model (RTM) are adopted as model and observation operator, respectively. The assimilation experiment was conducted in Naqu on the Tibet Plateau from 31 May to 27 September 2011. The updated soil temperature at surface obtained by assimilating MODIS LST serving as inputs of RTM is to reduce the differences between the simulated and observed TB, then AMSR-E TB is assimilated to update soil moisture and model parameters. Compared with in situ measurements, the accuracy of soil moisture estimation derived from the assimilation experiment has been tremendously improved at a variety of scales. The updated parameters effectively reduce the states bias of CoLM. The results demonstrate the potential of assimilating AMSR-E TB and MODIS LST to improve the estimation of soil moisture and related parameters. Furthermore, this study indicates that the developed scheme is an effective way to retrieve downscaled soil moisture when assimilating the coarse-scale microwave TB. [ABSTRACT FROM AUTHOR]

The Advanced Microwave Scanning Radiometer 2 (AMSR2), a follow-up microwave sensor to the AMSR for Earth Observing System (AMSR-E), was launched on the Global Change Observation Mission 1 – Water (GCOM-W1) satellite in May 2012. It is as yet unclear if instrumental improvements in AMSR2 over AMSR-E have led to better soil moisture (SM) estimates, especially since there is no overlapping period of data between the sensors. This study focuses on comparing the results of AMSR2 and AMSR-E SM over Australia, distinguishing four Köppen climate zones to determine if AMSR2 is better than AMSR-E. This is achieved by selecting two year-long comparative time periods from the operating periods of AMSR-E and AMSR2, based on their statistical similarities in modeled SM as a proxy, using Modern Era Retrospective-analysis for Research and Applications-Land (MERRA-L). The AMSR2 and AMSR-E C- and X-band SM derived from the Land Parameter Retrieval Model (LPRM) was evaluated. Both AMSR2 C- and X-band SM products were found to show similar temporal patterns and spatial agreement with AMSR-E C- and X-band SM, supported by unbiased root mean square difference (ubRMSD) and R-values with MERRA-L SM, respectively. Using lag-based instrumental variable analysis to estimate the random error component of SM retrievals, the noise-to-signal ratios in AMSR2 X-band SM were found to be slightly higher than their AMSR-E counterparts. The improvements in AMSR2, such as the superior radiometric sensitivity and spatial resolution, have therefore not led to statistically significant differences in performance for LPRM retrievals at 1/2° × 1/2° grid resolution, when compared with AMSR-E. However, similarities in the metrics for AMSR2 and AMSR-E SM suggest that AMSR2 provides a valuable continuation to AMSR-E. [ABSTRACT FROM AUTHOR]

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size. [ABSTRACT FROM AUTHOR]

Surface soil moisture is a critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purposes are sensors that are installed at depths of approximately 5 cm. There are however, sensor technologies and network designs that do not provide an estimate at this depth. If soil moisture estimates at deeper depths could be extrapolated to the near surface, in situ networks providing estimates at other depths would see their values enhanced. Soil moisture sensors from the U.S. Climate Reference Network (USCRN) were used to generate models of 5 cm soil moisture, with 10 cm soil moisture measurements and antecedent precipitation as inputs, via machine learning techniques. Validation was conducted with the available, in situ , 5 cm resources. It was shown that a 5 cm estimate, which was extrapolated from a 10 cm sensor and antecedent local precipitation, produced a root-mean-squared-error (RMSE) of 0.0215 m 3 /m 3 . Next, these machine-learning-generated 5 cm estimates were also compared to AMSR-E estimates at these locations. These results were then compared with the performance of the actual in situ readings against the AMSR-E data. The machine learning estimates at 5 cm produced an RMSE of approximately 0.03 m 3 /m 3 when an optimized gain and offset were applied. This is necessary considering the performance of AMSR-E in locations characterized by high vegetation water contents, which are present across North Carolina. Lastly, the application of this extrapolation technique is applied to the ECONet in North Carolina, which provides a 10 cm depth measurement as its shallowest soil moisture estimate. A raw RMSE of 0.028 m 3 /m 3 was achieved, and with a linear gain and offset applied at each ECONet site, an RMSE of 0.013 m 3 /m 3 was possible. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHIs) have been long studied using both ground-based observations of air temperature and remotely sensed thermal infrared (TIR) data. While ground-based observations lack spatial detail even in the occasional “dense” urban network, skin temperature retrievals using TIR data have lower temporal coverage due to revisit frequency, limited swath width, and cloud cover. Algorithms have recently been developed to retrieve near-surface air temperatures using microwave radiometer data, which enables characterization of UHIs in metropolitan areas, major conurbations, and global megacities at regional to continental scales using temporally denser time series than those that have been available from TIR sensors. Here we examine how UHIs appear across the entire Western Hemisphere using surface air temperatures derived from the Advanced Microwave Scanning Radiometers (AMSRs), AMSR-E onboard the National Aeronautics and Space Administration’s (NASA’s) Aqua and AMSR2 onboard the Japan Aerospace eXploration Agency’s Global Change Observation Mission-Water1 (JAXA’s GCOM-W1) satellites. We compare these data with station observations from the Global Historical Climate Network (GHCN) for 27 major cities across North America (in 83 urban-rural groupings) to demonstrate the capability of microwave data in a UHI study. Two measures of thermal time, accumulated diurnal and nocturnal degree-days, are calculated from the remotely sensed surface air temperature time series to characterize the urban-rural thermal differences over multiple growing seasons. Daytime urban thermal accumulations from the microwave data were sometimes lower than in adjacent rural areas. In contrast, station observations showed consistently higher day and night thermal accumulations in cities. UHIs are more pronounced at night, with 55% (AMSRs) and 93% (GHCN) of urban-rural groupings showing higher accumulated nocturnal degree-days in cities. While urban-rural thermal gradients may vary according to different datasets or locations, day-night differences in thermal time metrics were consistently lower (>90% of urban-rural groupings) in urban areas than in rural areas for both datasets. We propose that the normalized difference accumulated thermal time index (NDATTI) is a more robust metric for comparative UHI studies than simple temperature differences because it can be calculated from either station or remotely sensed data and it attenuates latitudinal effects. [ABSTRACT FROM AUTHOR]

A new method to assess land surface fractional open water ( fw ) inundation dynamics was developed using high frequency (89 GHz) brightness temperatures ( T b ) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), with other ancillary inputs from AMSR-E and MODIS (Moderate Resolution Imaging Spectroradiometer). The resulting 89 GHz fw ( fw WBand ) retrievals provide 10-day temporal fidelity and enhanced (5 km) resolution relative to other passive microwave sensor based land observations. A look-up table was first established to provide reference microwave emissivities for water and land endmembers under a range of atmosphere and land surface conditions. The fw WBand retrievals were then obtained on a per pixel basis using difference ratio and double difference methods for respective barren and vegetated soil conditions. The resulting fw WBand summer (JJA) seasonal composites correspond favorably ( R ≥ 0.86, p < 0.001) with alternative, finer resolution (≤ 300 m) static open water maps derived from satellite radar and optical-infrared sensors over a pan-Arctic (≥ 45°N) domain. The fw WBand retrievals also capture significant seasonal drought and flooding events inferred from river discharge records within the major Arctic basins. The fw WBand retrievals are found to be reliable for discriminating open water in moderately dense vegetation areas, but with greater uncertainty over barren land. The ability of the fw retrievals to detect surface water under vegetation also decreases exponentially with increasing canopy optical thickness. The new fw WBand dataset provides an improved resolution satellite environmental data record documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Remotely sensed and modeled soil moisture products are widely used in hydro-meteorological, agricultural and other applications. However, both of them need to be validated by ground measurements before they can be used for different applications. Three remotely sensed soil moisture products and the simulated soil moisture using the Community Land Model version 4.0 (CLM4.0) were compared and verified with the in situ measurements from a mesoscale soil moisture monitoring network in the central Tibetan Plateau area. These four soil moisture data sets can overall reasonably capture the surface soil moisture dynamics but with considerable biases for some certain soil conditions, such as frozen. The Land Parameter Retrieval Model (LPRM) AMSR-E soil moisture systematically overestimates unfrozen soil moisture and fails to estimates frozen soil, with bias ( BIAS ) and root mean square error ( RMSE ) values of 0.11 m 3 m − 3 and 0.12 m 3 m − 3 in average, respectively. The Japan Aerospace Exploration Agency (JAXA) AMSR-E soil moisture shows better performance than the other two remotely sensed soil moisture, with smaller BIAS and RMSE values (0.017 and 0.096 m 3 m − 3 in average, respectively). But it still has a large overestimation bias for unfrozen soil. The AMSR2 data show a similar performance to the JAXA AMSR-E product, but with slight larger BIAS and RMSE (0.024 and 0.112 m 3 m − 3 in average, respectively), and a very large short-term variability during unfrozen periods. The CLM4.0 model was overall able to model multiple-layers' soil moistures well ( RMSE = 0.03 m 3 m − 3 ) but slightly underestimated surface soil moisture (0–10 cm) and overestimated deeper soil moisture (20–40 cm) under unfrozen conditions. These comparisons suggest that the LPRM algorithm fails to retrieve soil moisture value in the Tibetan Plateau area (TP) and the JAXA algorithm needs to be improved for unfrozen soil. For the AMSR2 product, more calibration work and to align it to the AMSR-E sensor are needed. This study also show the potential of using the land surface models (LSMs) for verify the remotely sensed soil moisture products especially for the area with lack of observations available, such as in the Tibetan Plateau area. [ABSTRACT FROM AUTHOR]

Soil moisture is one of the essential climate variables for the Global Climate Observing System (GCOS) that has been prioritized by the ESA's Climate Change Initiative to construct its homogeneous long-term climate record. This requires a consistent characterization of the error structures in the individual data sets, which vary due to changes in instrument configuration and calibration, and retrieval algorithm design. In this paper, the random error and systematic differences in nine passive and active microwave satellite soil moisture products over Australia (time coverage: 1978–present) are estimated in a same manner for SM components at subseasonal and seasonal-to-interannual timescales separately. The multi-scale error structures are found to be non-trivial and vary between the products, giving cause for conducting multi-scale merging with awareness of these differences. Noticeable similarities between the error structures of the satellite products derived from same retrieval algorithm and same measuring frequency however suggest transferability of error parameters between them. Using partial rank correlation analysis, the error maps are linked to statistics on vegetation index, digital elevation, soil moisture and soil temperature, and land cover fractions and mixing in order to explain the observed variability and the similarities between the products. [ABSTRACT FROM AUTHOR]

Snowfall comprises a significant percentage of the annual water budget in High Mountain Asia (HMA), but snow-water equivalent (SWE) is poorly constrained due to lack of in-situ measurements and complex terrain that limits the efficacy of modeling and observations. Over the past few decades, SWE has been estimated with passive microwave (PM) sensors with generally good results in wide, flat, terrain, and lower reliability in densely forested, complex, or high-elevation areas. In this study, we use raw swath data from five satellite sensors — the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) (1987–2015, F08, F11, F13, F17), Advanced Microwave Scanning Radiometer — Earth Observing System (AMSR-E, 2002–2011), AMSR2 (2012–2015), and the Global Precipitation Measurement (GPM, 2014–2015) — in order to understand the spatial and temporal structure of native sensor, topographic, and land cover biases in SWE estimates in HMA. We develop a thorough understanding of the uncertainties in our SWE estimates by examining the impacts of topographic parameters (aspect, relief, hillslope angle, and elevation), land cover, native sensor biases, and climate parameters (precipitation, temperature, and wind speed). HMA, with its high seasonality, large topographic gradients and low relief at high elevations provides an excellent context to examine a wide range of climatic, land-cover, and topographic settings to better constrain SWE uncertainties and potential sensor bias. Using a multi-parameter regression, we compare long-term SWE variability to forest fraction, maximal multi-year snow depth, topographic parameters, and long-term average wind speed across both individual sensor time series and a merged multi-sensor dataset. In regions where forest cover is extensive, it is the strongest control on SWE variability. In those regions where forest density is low (< 5%), maximal snow depth dominates the uncertainty signal. In our regression across HMA, we find that forest fraction is the strongest control on SWE variability (75.8%), followed by maximal multi-year snow depth (7.82%), 90th percentile 10-m wind speed of a 10-year December-January-February (DJF) time series (5.64%), 25th percentile DJF 10-m wind speed (5.44%), and hillslope angle (5.24%). Elevation, relief, and terrain aspect show very low influence on SWE variability (< 1%). We find that the GPM sensor provides the most robust regression results, and can be reliably used to estimate SWE in our study region. While forest cover and elevation have been integrated into many SWE algorithms, wind speed and long-term maximal snow depth have not. Our results show that wind redistribution of snow can have impacts on SWE, especially over large, flat, areas. Using our regression results, we have developed an understanding of sensor-specific SWE uncertainties and their spatial patterns. The uncertainty maps developed in this study provide a first-order approximation of SWE-estimate reliability for much of HMA, and imply that high-fidelity SWE estimates can be produced for many high-elevation areas. [ABSTRACT FROM AUTHOR]

Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010–Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m 3 /m 3 ), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m 3 /m 3 for AMSR-reg and 0.084 m 3 /m 3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations. [ABSTRACT FROM AUTHOR]

This paper presents the comparison of three global soil moisture products (ASCAT, AMSR and SMOS) versus a land surface model over a region representative of several Mediterranean landscapes located in the Northeast of the Iberian Peninsula. Our approach has been for agricultural and water management applications at the regional and local scale. Despite being a rather small area, we were able to observe different signal behaviours corresponding to major land cover classes in Mediterranean areas i.e.: dryland and irrigated crops, forests and natural vegetation (grass-shrubs). The area also allowed assessing the impact of topography. The first result of the study is that the results are very dependent on the normalizations used to make the data comparable, thus their impact must be carefully analysed. In this study, we applied two different normalisation methods (called ZV35 and ZV) and different moving average windows (1, 10 and 30 days) in order to enhance seasonal effects. Using no smoothing window, ASCAT is the soil moisture product that correlates best with the LSM over all cover classes, whatever the method. Using smoothing window, AMSR-E tends to outperform other soil moisture products with the ZV method. The ZV35 method is not able to identify a small heavily irrigated area. The reason for these different results is that ZV35, tends to eliminate the monthly scale soil moisture memory and therefore becomes more sensitive to precipitation and less sensitive to the monthly evolution of superficial soil moisture. The comparison shows in general good agreement for all soil moisture products with the LSM on the temporal series simulated over flat, non irrigated areas which are not close to the sea. SMOS has difficulties in areas close to the sea and in areas with steep relief and the current version of the L2 Operational Algorithm (V5.51) depicts few values in forested areas. ASCAT, in its turn, shows some limitations over agricultural and natural vegetation where it shows an increase of soil moisture from June to October probably due to increase of penetration depth in dry soil moisture conditions. AMSR-E LPRM shows a clear vegetation cycle over all the land cover classes. From all the remote sensing products, SMOS is the only one able to see irrigation and the only that does not show clear vegetation or roughness effects. In this study, we were able to assess the impact of higher resolution soil moisture products to map irrigated areas. [ABSTRACT FROM AUTHOR]

In snow depth studies, the smoothing effect of ground data interpolation and the low spatial resolution of remote sensing have a great impact on the estimation accuracy. In this paper, by using cloud-removed snow cover products from the fusion of MODIS (moderate resolution imaging spectro-radiometer) and AMSR-E (advanced microwave scanning radiometer - EOS) to construct virtual station, we make up the shortage of meteorological stations less and unevenness and correct the smoothing effect of Kriging interpolation. At the same time, a new scheme is proposed to improve the estimation accuracy of snow depth interpolation, which integrates data assimilation algorithm and Kriging method to fuse ground-based snow depth measurements, MODIS snow cover products. and snow depth derived from the AMSR-E microwave brightness temperature. This method was applied in the area of northern Xinjiang. Three independent stations at different elevations were chosen to evaluate fusion results. The results indicate that the proposed algorithm can effectively improve the accuracy of snow depth spatial distribution. [ABSTRACT FROM AUTHOR]

Terrestrial evaporation is an essential variable in the climate system that links the water, energy and carbon cycles over land. Despite this crucial importance, it remains one of the most uncertain components of the hydrological cycle, mainly due to known difficulties to model the constraints imposed by land water availability on terrestrial evaporation. The main objective of this study is to assimilate satellite soil moisture observations from the Soil Moisture and Ocean Salinity (SMOS) mission into an existing evaporation model. Our over-arching goal is to find an optimal use of satellite soil moisture that can help to improve our understanding of evaporation at continental scales. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used to simulate evaporation fields over continental Australia for the period September 2010–December 2013. SMOS soil moisture observations are assimilated using a Newtonian Nudging algorithm in a series of experiments. Model estimates of surface soil moisture and evaporation are validated against soil moisture probe and eddy-covariance measurements, respectively. Finally, an analogous experiment in which Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture is assimilated (instead of SMOS) allows to perform a relative assessment of the quality of both satellite soil moisture products. Results indicate that the modelled soil moisture from GLEAM can be improved through the assimilation of SMOS soil moisture: the average correlation coefficient between in situ measurements and the modelled soil moisture over the complete sample of stations increased from 0.68 to 0.71 and a statistical significant increase in the correlations is achieved for 17 out of the 25 individual stations. Our results also suggest a higher accuracy of the ascending SMOS data compared to the descending data, and overall higher quality of SMOS compared to AMSR-E retrievals over Australia. On the other hand, the effect of soil moisture data assimilation on the evaporation fields is very mild, and difficult to assess due to the limited availability of eddy-covariance data. Nonetheless, our continental-scale simulations indicate that the assimilation of soil moisture can have a substantial impact on the estimated dynamics of evaporation in water-limited regimes. Progressing towards our goal of using satellite soil moisture to increase understanding of global land evaporation, future research will focus on the global application of this methodology and the consideration of multiple evaporation models. [ABSTRACT FROM AUTHOR]

Summary The objective of this study is to evaluate the relative potential of spatially distributed surface and root zone soil moisture estimates in calibration of Soil and Water Assessment Tool (SWAT) toward improving its hydrologic predictability with reduced equifinality. The Upper Wabash and Cedar Creek, two agriculture-dominated watersheds in Indiana, USA are considered as test beds to implement this multi-objective SWAT calibration. The proposed calibration approach is performed using remotely sensed Advanced Microwave Scanning Radiometer-Earth Observing System surface soil moisture (∼1 cm top soil) estimates (NASA’s Aqua daily level-3 gridded land surface product-version 2) in sub-basin/HRU level together with observed streamflow data at the watershed’s outlet. Although application of remote sensing data in calibration improves surface soil moisture simulation, other hydrologic components such as streamflow, evapotranspiration (ET) and deeper layer moisture content in SWAT remain less affected. An extension of this approach to apply root zone soil moisture estimates from limited field sensor data showed considerable improvement in the simulation of root zone moisture content and streamflow with corresponding observed data. Difference in relative sensitivity of parameters and reduced extent of uncertainty are also evident from the proposed method, especially for parameters related to the subsurface hydrologic processes. Regardless, precise representation of vertical soil moisture stratification at different layers is difficult with current SWAT ET depletion mechanism. While the results from this study show that root zone soil moisture can play a major role in SWAT calibration, more studies including various soil moisture data products are necessary to validate the proposed approach. [ABSTRACT FROM AUTHOR]

According to the ASPeCt sea ice visual observation protocol, we extracted the sea ice edge from MODIS images and assessed the quality of AMSR-E SIC product using a corresponding MODIS Sea Ice Concentration(MODIS SIC). Results show that AMSR-E pixels located at the ice edges have a significantly different mean SIC value from the well-established 15% threshold. The correlation between AMSR-E and MODIS SIC is rather low with coefficients R2≤0.2 and the ASI algorithm underestimates sea ice edge concentrations in summer. Then transect analysis, applied on all the ice areas(including multi-year ice, one-year ice, new ice and open water), however, show that AMSR-E SIC and MODIS SIC have a good linear relationship with R2 of 0.82 in summer and 0.81 in winter. When AMSR-E SIC value falls between 20% and 30%, it has larger errors compared with the other range of SIC values. [ABSTRACT FROM AUTHOR]

Validating coarse-scale satellite soil moisture data still represents a big challenge, notably due to the large mismatch existing between the spatial resolution (> 10 km) of microwave radiometers and the representativeness scale (several m) of localized in situ measurements. This study aims to examine the potential of DisPATCh (Disaggregation based on Physical and Theoretical scale Change) for validating SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) level-3 soil moisture products. The ∽40–50 km resolution SMOS and AMSR-E data are disaggregated at 1 km resolution over the Murrumbidgee catchment in Southeastern Australia during a one year period in 2010–2011, and the satellite products are compared with the in situ measurements of 38 stations distributed within the study area. It is found that disaggregation improves the mean difference, correlation coefficient and slope of the linear regression between satellite and in situ data in 77%, 92% and 94% of cases, respectively. Nevertheless, the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. Consistently, better results are obtained in the semi-arid than in a temperate zone of the catchment. In the semi-arid Yanco region, disaggregation in summer increases the correlation coefficient from 0.63 to 0.78 and from 0.42 to 0.71 for SMOS and AMSR-E in morning overpasses and from 0.37 to 0.63 and from 0.47 to 0.73 for SMOS and AMSR-E in afternoon overpasses, respectively. DisPATCh has strong potential in low vegetated semi-arid areas where it can be used as a tool to evaluate coarse-scale remotely sensed soil moisture by explicitly representing the sub-pixel variability. [ABSTRACT FROM AUTHOR]

Multiple soil moisture products have been generated from data acquired by satellite. However, these satellite soil moisture products are not spatially or temporally complete, primarily due to track changes, radio-frequency interference, dense vegetation, and frozen soil. These deficiencies limit the application of soil moisture in land surface process simulation, climatic modeling, and global change research. To fill the gaps and generate spatially and temporally complete soil moisture data, a data assimilation algorithm is proposed in this study. A soil moisture model is used to simulate soil moisture over time, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the control variables of the soil moisture model from good-quality satellite soil moisture data covering one year, so that the temporal behavior of the modeled soil moisture reaches the best agreement with the good-quality satellite soil moisture data. Soil moisture time series were then reconstructed by the soil moisture model according to the optimal values of the control variables. To analyze its performance, the data assimilation algorithm was applied to a daily soil moisture product derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Microwave Radiometer Imager (MWRI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Preliminary analysis using soil moisture data simulated by the Global Land Data Assimilation System (GLDAS) Noah model and soil moisture measurements at a multi-scale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau (CTP-SMTMN) was performed to validate this method. The results show that the data assimilation algorithm can efficiently reconstruct spatially and temporally complete soil moisture time series. The reconstructed soil moisture data are consistent with the spatial precipitation distribution and have strong positive correlations with the values simulated by the GLDAS Noah model over large areas of the region. Compared to the soil moisture measurements at the medium and large networks, the reconstructed soil moisture data have almost the same accuracy as the soil moisture product derived from AMSR-E/MWRI/AMSR2 for ascending and descending orbits. [ABSTRACT FROM AUTHOR]

The main objectives of this study were to provide a proxy “validation” of the Soil Moisture and Ocean Salinity (SMOS) mission's vegetation optical depth product ( τ SMOS ) on a global scale, to give a first indication of the potential of τ SMOS to capture large-scale vegetation dynamics, and to contribute towards investigations into the possible use of optical vegetation indices (VI's) for the estimation of τ . The analyses were performed by comparing the spatial and temporal behaviour of τ SMOS relative to four MODIS-based VI's, with that of the vegetation optical depth from a similar sensor, AMSR-E ( τ AMSR-E ). 16-day and annual average values of the passive microwave optical depth ( τ ) for the year 2010 were obtained from SMOS (1.4 GHz) and AMSR-E (6.9 GHz) observations. The VI's chosen for this study were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and Normalized Difference Water Index (NDWI). The highest global-scale, annual correlation was found between τ SMOS and τ AMSR-E from ascending orbits (Spearman's R = 0.80). On global, annual scales, τ SMOS showed higher correlations with τ AMSR-E than with the VI's, while τ AMSR-E was more highly correlated with VI's than with τ SMOS . Timeseries of both τ and the VI's were made per landcover class, for the northern hemisphere, tropics and southern hemisphere. Although the large-scale spatial and spatio-temporal behaviour of τ SMOS and τ AMSR-E is generally similar, the results highlight some notable differences in observing vegetation with optical vs . passive microwave sensors, and certain crucial differences between the two passive microwave sensors themselves. Overall, the results found in this study give a good first confidence in the SMOS L3 τ product and its potential use in vegetation studies. These results provide an essential general reference for future (global-scale) vegetation monitoring with passive microwaves, for the future inclusion of τ SMOS in long-term, multi-sensor datasets, and for passive microwave algorithm development. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable in hydrological processes. Although the combined effects of multiple climatic factors in different land cover conditions are highly valuable for water resource management, a complete understanding of these effects remains unclear. This study used a cluster analysis approach to investigate the combined effects of precipitation and air temperature, rather than a single factor, in different land covers for an area over the Poyang Lake Basin in China from 2003 to 2009. Specifically, monthly soil moisture was classified into eight clusters according to the change in precipitation and air temperature; the clusters describe a range of climates from the extreme of wet–hot to that of dry–cold. For an individual climate factor, our results showed that the contribution of air temperature to soil moisture is greater than that of precipitation, and the effect of air temperature is more sensitive in different land covers. When considering the combined effects of precipitation and air temperature, soil moisture varies with land cover; however, the variation in a normal climate cluster is greater than in an extreme climate cluster. This indicated that land cover is the dominant factor in soil moisture variation in normal climatic conditions, whereas climate is the dominant factor in extreme conditions. As climate shifts from the wet–hot to the dry–cold cluster, soil moisture decreases for all land covers, with the minimum rate occurring in forest conditions. Meanwhile, soil moisture deficit and saturation are more likely to occur in grassland and forest areas, indicating that forest cover might mitigate drought. The results of this study provide an effective approach to investigate the combined effects of climate factors on soil moisture for various land covers in humid areas. This study also supports the management of water resources in changing climates. [ABSTRACT FROM AUTHOR]

Two machine learning (ML) models are investigated for retrieving sea surface temperature (SST) from passive microwave (PMW) satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and auxiliary data, such as ERA5 reanalysis data. The first model is the Extreme Gradient Boosting (XBG) model and the second is a multilayer perceptron neural network (NN). The performance of the two ML algorithms is compared to that of an existing state-of-the-art regression (RE) retrieval algorithm. The performance of the three algorithms is assessed using independent in situ SSTs from drifting buoys. Overall, the three models have similar biases; 0.01, 0.01 and −0.02 K for the XGB, NN and RE, respectively. The XGB model performs best with respect to standard deviation; 0.36 K. While the NN model performs slightly better than the RE model with respect to standard deviation, 0.50 and 0.55 K, respectively, the RE model is found to be more sensitive to changes in the in situ SST. Moreover, the XGB model is the least sensitive with an overall sensitivity of 0.78, compared to 0.90 for the RE model and 0.88 for the NN model. The good performance of the two ML algorithms compared to the state-of-the-art RE algorithm in this initial study demonstrates that there is a large potential in the use of ML algorithms for the retrieval of SST from PMW satellite observations. • Retrieval of passive microwave sea surface temperature using machine learning models. • Comparison of a neural network and a tree-based model to a regression model. • The tree-based model outperforms the other models for bias and standard deviation. • The regression model and neural network have significantly higher sensitivities. • The tree-based model has the lowest sea surface temperature sensitivity. [ABSTRACT FROM AUTHOR]

River discharge is one of the most critical renewable water resources. Accurately estimating river discharge with land surface models (LSMs) remains challenging due to the difficulty in estimating land water storages such as snow, soil moisture, and groundwater. While data assimilation (DA) ingesting optical, microwave, and gravity measurements from space can help constrain theses storage states, its impacts on runoff and eventually river discharge are not fully understood. Here, by taking advantage of recently published land DA results that jointly assimilate eight different combinations of observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), we quantify to what degree multi-sensor land DA improves the river discharge simulation skills over 40 global river basins, and investigate the complementary strengths of different satellite measurements on river discharge. To be more specific, river discharge is updated by feeding gridded runoff from the eight multi-sensor DA simulations into a vector-based river routing model named the Routing Application for Parallel computatIon of Discharge (RAPID). Our modeling results, including 7-year simulations at 177,458 river reaches globally, are used to study the seasonal to interannual variability of river discharge. It is found that assimilating GRACE has the greatest impact on global runoff patterns, leading to the most pronounced improvements in spatial river discharge in the middle and high latitudes with the R2 increased by 0.16. The seasonal variation of spatial discharge is most skillful during the boreal summer. However, our evaluation also shows model and DA still struggle to generate reasonable variability and averaged discharge over permafrost regions. By assessing how different satellites add value to discharge forecasts, this study paves the way for more advanced multi-sensor satellite data assimilation to predict the terrestrial hydrological cycle. • We quantify the impact of land data assimilation on discharge with river routing. • GRACE DA improves discharge patterns in middle and high latitudes during summer. • Multi-sensor DA provides complementary information for discharge estimations. [ABSTRACT FROM AUTHOR]

This study made comprehensive comparison of sea ice concentration derived from ship observations, optical and passive microwave images. Seven types of SIC derived from AMSR-E images and MODIS images were compared with RSO at 27 sample points between 2002 and 2007. The correlation between SIC derived from synchronous data observed at the same spatial scale had higher correlation coefficients than the others, whereas SICs observed at different times and scales displayed large inconsistencies. The correlation between SIC derived from data observed at the same spatial scale but at different times were affected by the spatial resolution. The RSO had considerably large differences with the PSO, the subjectivity in e visual estimation, weather conditions, quality of the image classification, and extraction method all attribute to their differences. Although the pseudo ship observation extraction method is helpful to generate large number of samples at the sea ice edges, error propagation must be controlled in the extraction process. [ABSTRACT FROM AUTHOR]

Soil moisture plays an integral role in multi-scale hydrologic modeling, agricultural decision analysis, climate change assessments, and drought prediction/prevention. The broad availability of soil moisture estimates has only occurred within the past decade through a combination of in situ networks and satellite-driven remote sensing. The U.S. Climate Reference Network (USCRN) has provided a nationwide in situ resource since 2009. The Advanced Microwave Scanning Radiometer (AMSR-E), launched in 2002, is one of the satellite products available for comparison, but there are a limited number of years where the data records overlap. This study compares the results of modeled historical soil moisture estimates derived using USCRN precipitation data to the remotely sensed estimates provided by the AMSR-E satellite between 2002 and 2011. First, this work assesses the calibrated model's similarity to in situ estimates. Next, the model estimates and in situ measurements are shown to perform comparably well against the AMSR-E satellite product, suggesting that it may be possible to utilize modeled estimates at times and locations where satellite estimates are unavailable and further extend the soil moisture record spatially and temporally. [ABSTRACT FROM AUTHOR]

The mapping and forecasting of droughts and floods is an important potential field of application of global soil moisture and water storage products from satellites and models. Especially when extremes in near-surface soil moisture propagate into extremes in total water storage, agricultural production and water supply can be severely impacted. This study relates soil moisture from the WaterGAP Global Hydrology Model (WGHM) and the satellite sensors Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) and Advanced Scatterometer (ASCAT) to total water storage variations from the satellite gravity mission GRACE. A particular focus is on destructive hydrological extreme events, as listed in the International Disaster Database EM-DAT. Data sets are analyzed via correlation, time shift, and principal component analyses. The study area is the La Plata Basin in South America. The results indicate that most of the soil moisture anomalies are linked to periods of El Niño and La Niña and associated natural disasters. For the La Plata drought of 2008/2009 and the El Niño flooding of 2009/2010, soil moisture serves as an indicator for the later deficit or surplus in total water storage. These hydrological anomalies were strongest in the southern, central, and eastern parts of the basin, but more than one hundred thousand people were also affected in the northwestern part. [ABSTRACT FROM AUTHOR]

The paper reports the recent progress in the radiative transfer model (RTM) development, which serves as the observation operator of a Land Data Assimilation System (LDAS), and its validation at two Planetary Boundary Layer (PBL) stations with different weather and land cover conditions: Wenjiang station of humid and cropped field and Gaize station of arid and bare soil field. In situ observed micrometeorological data were used as the driven data of LDAS, in which AMSR-E brightness temperatures (TB) were assimilated into a land surface model (LSM). Near surface soil moisture content output from LDAS, together with the one simulated by a LSM with default parameters, were compared to the in-situ soil moisture observation. The comparison results successfully validated the capability of LDAS with new RTM to simulate near surface soil moisture at various environments, supporting that LDAS can generally simulate soil moisture with a reasonable accuracy for both humid vegetated fields and arid bare soil fields while the LSM overestimates near surface soil moisture for humid vegetated fields and underestimates soil moisture for arid bare soil fields. [ABSTRACT FROM AUTHOR]