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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.

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 objectives of this study were to analyze satellite data on the ice conditions in the White Sea available for the period 1979-2021 so that to identify statistically significant trends in closeness of ice during that period, and to establish spatial and temporal characteristics of the sea ice regime in this area. It was found that according to the satellite data a statistically significant negative trend (-0.34±0.11% year-1 at a significance level of 0.05) of the ice closeness changes was observed over the above period for all areas of the sea, which corresponds to the general tendency of reducing the area of the Arctic ice cover due to the global warming. However, contrary to the statements of J.C. Comiso, D.K. Perovich, M.C. Serreze, J. Streve, D. Notz that since the beginning of the XX century the reduction of the ice area in the Arctic Ocean accelerates, no tendency in decrease of mean annual values of the ice closeness for the White Sea was revealed. In addition, the results of the regression analysis showed a slight decrease in the absolute value of the trend from - 0.74±0.38% year-1 in 1979-1996 to -0.47±0.25% year-1 in 1997-2020. It is also shown that the results of a comparative analysis of two trends in the year-to-year variability of ice closeness in the White Sea: the first one obtained from the microwave radiometer data provided by the National Snow and Ice Data Center, and the second one revealed from data of advanced microwave scanning radiometers (the University of Bremen), are in good agreement with each other. The difference in the slope coefficients of the linear approximation is only 0.038% year-1 for the period 2002-2020. This is the important argument in support of using microwave radiometer data to determine trends in variability of ice closeness in the White Sea.

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.

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]

As a key region of Northeast Passage, the polynya along the Siberian coast in the East Siberian and Chukchi Seas is important to local dynamic and thermodynamic processes, sea ice production and marine ecosystem. The detailed variations of polynya and the contributions of atmospheric and oceanic factors to the polynya have not been explored quantitatively. AMSR-E satellite data from January to April during the period 2003–2011 were used to study the impacts of wind stress and ocean heat transport on variations of polynya in the East Siberian Sea and Chukchi Sea. The study region was divided into six domains. Four sets of AMSR-E data with resolutions of 6.25 km and 12.5 km were compared based on two algorithms of sea ice concentration (referred to as 6.25 km-IC and 12.5 km-IC) and sea ice thickness (referred to as 6.25 km-h and 12.5 km-h). The monthly and yearly polynya areas in the four cases and six domains had remarkable differences. The two cases of 6.25 km-h and 12.5 km-h had larger areas of polynya than the other two cases of 6.25 km-IC and 12.5 km-IC. The difference in polynya area between the 6.25 km-h and 12.5 km-h cases was much smaller than the difference between the 6.25 km-IC and 12.5 km-IC cases. The study of atmospheric and oceanic mechanisms on polynya is influenced significantly by the sensitivity of polynya areas. In general, the impact of wind stress and ocean heat transport on the polynyas had noticeable monthly and interannual variations and was dependent on the locations of the polynyas. The alongshore and offshore wind had stronger correlations with the polynya area than ocean heat transport. Although the higher resolution (6.25 km) AMSR-E data are best for the study of atmospheric and oceanic impacts on polynya area, the coarse resolution (12.5 km) AMSR-E data based on sea ice thickness can also be used. Wind direction dominated the polynya area in the East Siberian Sea and wind speed dominated the polynya area in the Chukchi Sea. The variation in ocean heat transport was influenced mainly by variation in volume transport rather than variation in water temperature.

Vegetation optical depth (VOD) retrieved from microwave remote sensing techniques has been used as an important proxy for monitoring the vegetation dynamics at large scales. In a first study we developed a new X-VOD indice from inversion of the X-MEB model from the Microwave Scanning Radiometer 2 (AMSR2, 2012-present) observations. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E, 2002–2011) and AMSR2 share many characteristics, such as local crossing time, incidence angle, frequency at X-band, etc, which makes it possible to produce a consistent long-term X-VOD product over a long period spanning from 2002 to present. The objective of this study was to merge the AMSR-E and AMSR2 observations in order, in a second step, to produce a consistent long-term X-VOD product spanning over the combined periods of AMSR-E and AMSR2.The main challenges in retrieving a consistent X-VOD data set from AMSR-E and AMSR2 are that (i) there is a bias between observations from the two sensors; and (ii) the lack of overlapping observations between the two sensors makes it impossible to achieve a direct inter-calibration. Here, to overcome this problem, we used AMSR-E slow rotation data (AMSR-E L1S), which has similar characteristics as AMSR-E and provided observations concurrently with AMSR2, as a bridge to calibrate the AMSR-E brightness temperature (TB) observations with AMSR2 TB.As our main objective in this TB calibration study was to produce a consistent long-term AMSR-E/AMSR2 X-VOD product, we evaluated the retrieved X-VOD product based on different inter-calibration, either global-based or pixel-based, methods. Both AMSR-E and AMSR2 X-VOD were evaluated against the Aboveground Biomass (AGB), Leaf Area Index (LAI) and the Normalized Difference Vegetation Index (NDVI). The results suggest that global-based inter-calibration methods using homogeneous and temporally stable land covers (Evergreen Broadleaf Forests and Snow and Ice) as reference calibration data sets provided the best results. For instance, the spatial relationships between X-VOD and AGB/LAI/NDVI are highly consistent over the AMSR-E and AMSR2 periods after the calibration work. This study laid a solid foundation for monitoring the dynamics of X-VOD, as a proxy of AGB, over the combined periods of AMSR-E and AMSR2 sensors (almost 20 years).

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]

Marine boundary layer clouds tend to organize into closed or open mesoscale cellular convection (MCC). Here, two‐dimensional wavelet analysis is applied for the first time to passive microwave retrievals of cloud water path (CWP), water vapor path (WVP), and rain rate (RR) from Advanced Microwave Scanning Radiometer for Earth Observing System in 2008 over the Northeast and Southeast Pacific, and the Southeast Atlantic subtropical stratocumulus to cumulus transition regions. The (co‐)variability between CWP, WVP, and RR in 160 × 160 km2 analysis boxes is partitioned between four mesoscale wavelength octaves (20, 40, 80, and 160 km). The cell scale is identified as the wavelength of the peak CWP variance. Together with a machine‐learning classification of cell type, this allows the statistical characteristics of open and closed MCC of various scales, and its relation to WVP, RR, and potential environmental controlling factors to be analyzed across a very large set of cases. The results show that the cell wavelength is most commonly 40–80 km. Cell‐scale CWP perturbations are good predictors of the WVP and RR perturbations. For cells larger than 20 km, there is no obvious dependence of cell scale on the environmental controlling factors tested, suggesting that the cell scale may depend more on its historical evolution than the current environmental conditions. Key Points: Two‐dimensional wavelet analysis is applied for the first time to a full year of passive microwave retrievals from Advanced Microwave Scanning Radiometer for Earth Observing SystemCell‐scale cloud water path perturbations are good predictors of the water vapor path and rain rate perturbations, similar between open and closed cellsSea surface temperature, estimated inversion strength, and 10‐m wind speed do not play a major role in determining the scale of the mesoscale cellular convection [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.

This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A. The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression. The snow depth in case of seasonal ice was calculated by using parameters of the cross-calibration of data from the MWRI Tb. The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97. The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI. Data from the MWRI Tb were cross-calibrated to the AMSR-E baseline. Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration, the standard deviations decreased significantly in the range of 1.32 K to 2.57 K, and the correlation coefficients were as high as 99%. An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated. Furthermore, the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz, 23.8 GHz, and 36.5 GHz (V polarization), and at 89 GHz (H/V polarization), and were applied to the snow depths retrieval in the Arctic. The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products. Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths, with the standard deviations ranging from 4.19 cm to 4.80 cm. [ABSTRACT FROM AUTHOR]

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

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]

Abstract 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.

Since 2002, global snow water equivalent (SWE) estimates have been generated using Advanced Microwave Scanning Radiometer (AMSR-E)/Aqua data. Accurate estimates of SWE are important to improve monitoring and managing of water resources in specific regions. SWE and snow map product accuracy are functions of topography and of land cover type because landscape characteristics have a strong influence on redistribution and physical properties of snow cover, and influence the microwave properties of the surface. Here we evaluate the AMSR-E SWE and derived snow map products in the Mackenzie River Basin (MRB), Canada, which is characterized by complex topography and varying land cover types from tundra to boreal forest. We compare in situ snow depth observations and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover maps from January 2003 to December 2007 with passive microwave remotely sensed SWE from AMSR-E and derived snow cover maps. In the MRB the mean absolute error ranges from 12 mm in the early winter season to 50 mm in the late winter season and overestimations of snow cover maps based on a 1 mm threshold of AMSR-E SWE varies from 4% to 8%. The optimal threshold for AMSR-E SWE to classify the pixels as snow ranges from 6 mm to 9 mm. The overall accuracy of new snow cover maps from AMSR-E varies from 91% to 94% in different sub-basins in the MRB.

Radio-frequency interference (RFI) detection for low-frequency microwave measurements is an important step before these data are applied to geophysical parameter retrieval or data assimilation. There are several robust techniques to identify the RFI signals, such as the mean/standard deviation method and the normalized principal component analysis method. However, verification of these existing detection methods remains an open issue in the absence of a reliable validation data-set of the ‘true’ RFI signals. In this paper, a cross-validation scheme using two independent RFI detection methods is proposed to derive the thresholds for identifying the RFI-contaminated data for the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). It is shown that the new scheme is effective in the quantitative classification of the RFI signals in the AMSR-E C- and X-band channels over the continents. Strong RFI signals are found to be populated over cities of the United States at AMSR-E C-band, while RFIs at X-band are mainly observed over Europe and Japan.

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature.

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]

In this study, the spatial and temporal variabilities of terrestrial water storage anomaly (TWSA) and snow water equivalent anomaly (SWEA) information obtained from the Gravity Recovery and Climate Experiment (GRACE) twin satellites data were analysed in conjunction with multisource snow products over several basins in the Canadian landmass. Snow water equivalent (SWE) data were extracted from three different sources: Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E), and Canadian Meteorological Centre (CMC). The objective of the study was to understand whether SWE variations have a significant contribution to terrestrial water storage anomalies in the Canadian landmass. The period was considered from December 2002 to March 2011. Significant relationships were observed between TWSA and SWEA for most of the 15 basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products compared with satellite‐based SWE data. Stronger relationships were found in snow‐dominated basins (Rs > = 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in the north of Quebec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behaviour was observed in the Western Hudson Bay basin. In both regions, it was found that the contribution of non‐SWE compartments including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the Water‐Global Assessment and Prognosis Global Hydrology Model (WGHM) simulations and then subtracted from GRACE observations. The GRACE‐derived SWEA correlation results showed improved relationships with three SWEA products. The improvement is particularly important in the sub‐basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE‐derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). Overall, the results indicated the important role of SWE on terrestrial water storage variations. [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 retrieving Ts and mv using the dual-polarized Tb of Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band (6.9 GHz) based on the simplified radiative transfer equation. Validation using in situ Ts and mv in southern China showed the average root mean square errors (RMSE) of Ts and mv retrievals 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 situ Ts (n = 2362) and mv (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 Spectroradiometer Ts (R2 = 0.57; RMSE = 2.91 k) and ECV_SM mv (R2 = 0.51; RMSE = 0.045 g cm−3), respectively. Overall, this paper indicates an effective way to jointly modeling Ts and mv using passive microwave remote sensing.

Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. With lack of ground observations in Pakistan, microwave sensors are viable technologies for retrieving soil moisture from space. Thus, this study compares soil moisture data from Soil Moisture and Ocean Salinity (SMOS) with Advanced Microwave Scanning Radiometer (AMSR-E) for 2010 and 2011, which were years where massive flooding occurred. We find suitable correlations (r = 0.47) for Level-2 soil moisture values for both satellites and report temporal variability during the pre-monsoon, monsoon, post-monsoon and winter seasons. This study motivates using satellite retrievals as a start towards more comprehensive studies in Pakistan.

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]

The Advanced Microwave Scanning Radiometer for the Earth Observing System and Advanced Microwave Scanning Radiometer 2 sensors (AMSR) have provided multifrequency microwave measurements of the global terrestrial water cycle since 2002. A new AMSR surface wetness index (ASWI) was developed by analyzing the near‐surface atmospheric vapor pressure deficit (VPD), surface volumetric soil moisture (VSM), and land surface fractional open water (FW) time series from an established AMSR Land Parameter Data Record (LPDR). The ASWI allows for multicomponent and independent satellite assessments of near‐surface drought conditions by exploiting the weighted anomalies of VPD, VSM, and FW. Comparisons between ASWI and more traditional drought metrics, including the Palmer moisture anomaly index (PDSI‐Z) and the U.S. Drought Monitor, showed generally consistent classifications of drought severity for three major droughts over the Contiguous United States since 2002. The AWSI showed moderate (0.3 ≤ R ≤ 0.7 for 56% of area) to strong (R > 0.7 for 29% of area) correlations with the PDSI‐Z during the summer months (June–August) from 2002 to 2017. ASWI and PDSI‐Z differences were attributed to AMSR retrieval uncertainties and the different aspects of drought represented by the indices. Comparisons between ASWI and the Gravity Recovery and Climate Experiment drought severity index (GRACE‐DSI) showed strong correspondence (R = 0.61) in regions where possible long‐term total water storage changes occurred. The sole reliance of the ASWI on satellite microwave remote sensing and continuing AMSR2 operations enables effective global monitoring of drought conditions while providing new information on the atmosphere, soil, and surface water components of drought. Plain Language Summary: Extreme hydrologic events affect the economy, environment, and society. The anomalies of the water cycle components are associated with drought events. Considering the capability of multifrequency microwave remote sensing in observing multiple water cycle components, this study developed a new surface wetness index by analyzing near‐surface atmospheric vapor pressure deficit, volumetric soil moisture, and land surface water inundation time series from an established satellite data record. The new index showed consistent assessment of major drought events relative to traditional drought monitors while providing enhanced information on the water cycle components of drought. A combined analysis of the index with satellite total water storage measurements provides complementary information on the different aspects of droughts, including relatively dynamic surface water changes and slower evolving groundwater conditions. The index enables independent and continuous satellite assessment of global drought conditions. Key Points: A new surface wetness index was developed from satellite‐observed vapor pressure deficit, soil moisture, and surface water coverThe index is consistent with traditional drought monitors but allows for analyzing the relative importance of water cycle componentsThe satellite‐based multicomponent index allows for independent monitoring of global drought conditions [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]

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]

The variation of soil moisture (SM) is a complex and synthetic process, which is impacted by numerous factors. The effects of these factors on soil moisture are dynamic. As a result, the relationship between soil moisture and explanatory variables varies with time and season. This kind of change should be considered in obtaining fine spatial resolution soil moisture products. We chose a study area with four distinct seasons in the temperate monsoon region. In this research, we established seasonal downscaling models to avoid the influence of seasonal differences. Precipitation, land surface temperature, evapotranspiration, vegetation index, land cover, elevation, slope, aspect and soil texture were taken as explanatory variables to produce fine spatial resolution SM. SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were downscaled with the help of machine learning algorithms. We compared three machine learning algorithms of random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) to determine the most suitable algorithm for this study. The results show that season-based downscaling is even better than continuous time series. In the analysis of seasonal differences, precipitation plays a dominant role, but its contribution rate is different in each season. Moreover, the influence of vegetation is more prominent in winter, while the influence of terrain is more important in the other three seasons. It could be noted that the accuracy of the RF model is the best among three machine learning algorithms, and the RF-downscaled products have superior matching performance to both AMSR (AMSR-E and AMSR2) SM products and in-situ measurements. The analysis indicates considering seasonal difference and the application of machine learning has high potential for spatial downscaling in remote sensing applications.

Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present DO and DD as determined from the time series of Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) melt pond fraction (MPF) estimates in an area with Canadian landfast ice. To address the lack of evaluation on a day-by-day basis for the AMSR-E MPF estimate, we first compared AMSR-E MPF with the daily Medium Resolution Imaging Spectrometer (MERIS) MPF. The AMSR-E MPF estimate correlates significantly with the MERIS MPF (r = 0.73−0.83). The estimate has a product quality similar to the MERIS MPF only when the albedo is around 0.5−0.7 and a positive bias of up to 10% in areas with an albedo of 0.7−0.9, including melting snow. The DO/DD estimates are determined by using a polynomial regression curve fitted on the time series of the AMSR-E MPF. The DOs/DDs from time series of the AMSR-E and MERIS MPFs are compared, revealing consistency in both DD and DO. The DO timing from 2006 to 2011 is correlated with melt onset timing. To the best of our knowledge, our study provides the first large-scale information on both DO timing and DD.

Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.

Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from satellite remote sensing or spatial interpolation of ground-based observations in relation to data from 22 meteorological stations in Amhara Region, Ethiopia, for 2003−2016. Famine Early Warning Systems Network (FEWS-Net) Land Data Assimilation System (FLDAS) interpolated temperature showed the lowest bias (mean error (ME) ≈ 1−3 °C), and error (mean absolute error (MAE) ≈ 1−3 °C), and the highest correlation with day-to-day variability of station temperature (COR ≈ 0.7−0.8). In contrast, temperature retrievals from the blended Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave and Moderate-resolution Imaging Spectroradiometer (MODIS) land-surface temperature data had higher bias and error. Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) rainfall showed the least bias and error (ME ≈ −0.2−0.2 mm, MAE ≈ 0.5−2 mm), and the best agreement (COR ≈ 0.8), with station rainfall data. In contrast FLDAS had the higher bias and error and the lowest agreement and Global Precipitation Mission/Tropical Rainfall Measurement Mission (GPM/TRMM) data were intermediate. This information can inform the selection of geospatial data products for use in climate and disease research and applications.

The daily AMSR-E/NASA (the Advanced Microwave Scanning Radiometer-Earth Observing System/the National Aeronautics and Space Administration) and JAXA (the Japan Aerospace Exploration Agency) soil moisture (SM) products from 2002 to 2011 at 25 km resolution were developed and distributed by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) and JAXA archives, respectively. This study analyzed and evaluated the temporal changes and accuracy of the AMSR-E/NASA SM product and compared it with the AMSR-E/JAXA SM product. The accuracy of both AMSR-E/NASA and JAXA SM was low, with RMSE (root mean square error) > 0.1 cm3 cm−3 against the in-situ SM measurements, especially the AMSR-E/NASA SM. Compared with the AMSR-E/JAXA SM, the dynamic range of AMSR-E/NASA SM is very narrow in many regions and does not reflect the intra- and inter-annual variability of soil moisture. We evaluated both data products by building a linear relationship between the SM and the Microwave Polarization Difference Index (MPDI) to simplify the AMSR-E/NASA SM retrieval algorithm on the basis of the observed relationship between samples extracted from the MPDI and SM data. We obtained the coefficients of this linear relationship (i.e., A0 and A1) using in-situ measurements of SM and brightness temperature (TB) data simulated with the same radiative transfer model applied to develop the AMSR-E/NASA SM algorithm. Finally, the linear relationships between the SM and MPDI were used to retrieve the SM monthly from AMSR-E TB data, and the estimated SM was validated using the in-situ SM measurements in the Naqu area on the Tibetan Plateau of China. We obtained a steeper slope, i.e., A1 = 8, with the in-situ SM measurements against A1 = 1, when using the NASA SM retrievals. The low A1 value is a measure of the low sensitivity of the NASA SM retrievals to MPDI and its narrow dynamic range. These results were confirmed by analyzing a data set collected in Poland. In the case of the Tibetan Plateau, the higher value A1 = 8 gave more accurate monthly AMSR-E SM retrievals with RMSE = 0.065 cm3 cm−3. The dynamic range of the improved retrievals was more consistent with the in-situ SM measurements than with both the AMSR-E/NASA and JAXA SM products in the Naqu area of the Tibetan Plateau in 2011.

Land surface phenologies and seasonalities in the US Prairie Pothole Region (PPR) were characterized using land surface variables derived from the coarse spatial resolution (25 km) Advanced Microwave Scanning Radiometer (AMSR) blended data for 2003 to 2016 linked with the optically based USDA NASS Crop Data Layer (CDL) at a much finer spatial resolution. Two transects of AMSR pixels—one in east-central North Dakota and the other in eastern South Dakota—were selected for analysis. The AMSR data were grouped earlier (2003−2005, 2007) and later (2013−2016) to emphasize temporal change and to avoid data discontinuity in 2011−2012 and a major drought in 2006. The nonparametric Mann-Kendall trend test on the CDL data revealed that area in grasslands and wetlands strongly decreased in both transects, while corn and soybean coverage strongly increased. In crop-dominated sites, the AMSR vegetation optical depth (VOD) time series caught the early spring growth, ploughing, and crop growth and senescence. In contrast, the VOD time series at grassland dominated sites exhibited a lower peak but extended growth period. Crop-dominated sites had significantly higher amplitude VODs in both periods and transects. Based on the paired two-sample t-test, neither the peak VOD amplitude nor the peak VOD timing measured in accumulated growing degree-days was significantly different between temporal groups in the North Dakota transect. In contrast, in South Dakota, both the peak VOD amplitude and its timing were significantly different with shifts to later peak timing during the 2013−2016 period. In addition, in South Dakota but not North Dakota, there were significantly earlier shifts in the timing of peak growing degree-days and peak precipitation water vapor. Both spatial and temporal changes in AMSR land surface variables are linked to shifts in land cover along the South Dakota transect as revealed in the CDL data. More research is required to understand the dynamics evident in the passive microwave time series.

Snow depth on sea ice is a major constituent of the marine cryosphere. It is a key parameter for the derivation of sea-ice thickness from satellite altimetry. One way to retrieve the basin-scale snow depth on sea ice is by satellite microwave radiometry. There is evidence from measurements and inter-comparison studies that current retrievals likely under-estimate the snow depth over deformed, rough sea ice. We follow up on an earlier study, where satellite passive microwave data were combined with information on the sea-ice topography from the satellite laser altimeter on board the Ice, Cloud and land Elevation Satellite (ICESat) in a hybrid approach. Such topography information is spatiotemporally limited because of ICESat’s operation mode. In this paper, we aim to derive a proxy for this topography information from satellite microwave radiometry. For this purpose, we co-locate parameters describing the sea-ice deformation taken from visual ship-based observations and the surface elevation standard deviation derived from ICESat laser altimetry with the microwave brightness temperatures (TB) measured via the Advanced Microwave Scanning Radiometer aboard Earth Observation Satellite (AMSR-E) and aboard Global Change Observation Mission-Water 1 (GCOM-W1) (AMSR2). We find that the TB polarization ratio at 6.9 GHz and the TB gradient ratio between 10.7 GHz (horizontal polarization) and 6.9 GHz (vertical polarization), might be suited as such a proxy. Using this proxy, we modify the above-mentioned hybrid approach and compute the snow depths on sea ice from the AMSR-E and AMSR2 data. We compare our snow depths with those of the commonly used approach, the hybrid approach, with the ship-based observations for the years 2002 through 2015 and with the measurements made by drifting buoys for the period of 2014 through 2018. We find a convincing overall agreement with the hybrid approach and some improvement over the common approach. However, our approach is sensitive to the presence of thin ice—here, the retrieved snow depths are too large; and our approach performs sub-optimally over old ice—here, the retrieved snow depths are too small. More investigations and, in particular, more evaluations are required to optimize our approach so that the snow depths retrieved for the combined AMSR-E/AMSR2 period could serve as a data set for sea-ice thickness retrieval based on satellite altimetry.

Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information. Firstly, the most important parameters are selected using sensitivity analysis, and then these parameters are included in a subsequent model calibration. The results of our multi-objective calibration reveal a substantial contribution of remote sensing products to the lumped model calibration, even if their spatially-distributed information is lost during the spatial aggregation. Inclusion of new observations, such as groundwater levels from wells and remotely sensed soil moisture to the calibration improves the model’s physical behavior, while it keeps a reasonable water balance that is the key objective of every hydrologic model.