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Soil moisture is an important variable in agricultural and hydrological applications. Sentinel-1 (A/B) satellite provides high-resolution (∼10 m) synthetic aperture radar data, and its high revisit capability (6 days at the equator) with multi-track data provides great potential for global soil moisture monitoring. In this study, an algorithm was proposed for soil moisture retrieval using the Multi-Temporal Dual-Channel (MTDC) Sentinel-1 data. The MTDC algorithm first conducted an incidence angle normalization method based on time series data to normalize backscattering coefficients from multiple tracks. Subsequently, using the vegetation water content obtained through a vegetation index climatology, soil roughness and soil unfrozen water content could be estimated for the freezing period (unfrozen water content in the soil is assumed to be stable for January and December). The changes in total backscatter between successive observations were expressed as a function of the two-way vegetation transmissivity multiplied by the difference between soil backscatter, which was directly related to the changes in soil moisture. Considering the retrieved soil roughness as a constant for the entire year, and the average unfrozen water content as the initial soil moisture, the changes in and absolute values of soil moisture for each observation can be estimated by minimizing a cost function between the modeled and observed changes in total backscatter. Retrieval results were validated against the soil moisture network in a semi-arid watershed of Shandian River with an unbiased Root Mean Square Error (ubRMSE) of 0.06 cm3·cm−3, and a bias of −0.04 cm3·cm−3. With the initial soil moisture from in-situ measurements, a higher accuracy was achieved for the MTDC algorithm (ubRMSE = 0.06 cm3·cm−3; bias = −0.01 cm3·cm−3). Further, comparisons with Soil Moisture Active Passive (SMAP) enhanced products further verified the efficacy of the proposed algorithm (ubRMSE = 0.03 cm3·cm−3; bias = −0.004 cm3·cm−3), and demonstrated the feasibility of operational soil moisture products using the Sentinel-1 radar dataset. • A multi-temporal dual-channel algorithm is proposed for soil moisture retrieval. • The incidence angle normalization enables the use of Sentinel-1 multi-track data. • Soil roughness and soil unfrozen water content are initially retrieved for frozen period. • Sentinel-1 can map 1-km soil moisture effectively at a local to regional scale. [ABSTRACT FROM AUTHOR]

Total precipitable water (TPW) is a key parameter in the study of water cycle and climate change. High-resolution TPW is critical for understanding regional water cycle processes. Besides, high-resolution TPW is also an important input parameter for atmospheric correction which helps to eliminate atmospheric impact and improve surface parameter retrieval accuracy. However, obtaining high-resolution TPW in all-weather condition from a single satellite sensor still has many limitations and challenges. This paper proposed a new fusion method to obtain all-weather TPW with a high spatial resolution of 0.05° × 0.05° by taking advantage of various water vapor related variables derived from microwave and near-infrared (NIR) remote sensing. At the same time, the contribution of variables in the fusion algorithm was analyzed to find the best fusion scheme. There are two major improvements in the newly developed fusion method. First, a new water vapor sensitive parameter – ratio of brightness temperature polarization difference between frequencies 89 and 36.5 GHz (∆ Tb 89 / ∆ Tb 36.5) was first introduced in the fusion algorithm to provide more high spatial resolution TPW information in both clear and cloudy sky conditions, and the accuracy of the fused TPW was significantly improved when the parameter ∆ Tb 89 / ∆ Tb 36.5 was introduced. Second, a two-steps fusion method was proposed to produce a fused high spatial resolution TPW by fully taking advantage of high quality TPW information from AMSR2 TPW and ∆ Tb 89 / ∆ Tb 36.5 in cloudy condition in the first step, and high quality TPW from MODIS in clear sky condition in the second step. The TPW derived from global navigation satellite system (GNSS) stations of Crustal Movement Observation Network of China (CMONOC) was used to validate the fused TPW and original TPW from MYD05 and AMSR2 at spatial resolution of 0.05° × 0.05°. According to the validation, the correlation coefficient, root mean square error, and relative root mean square error between the fused TPW and the GNSS TPW are 0.96, 4.23 mm and 23.67% respectively. The root mean square errors were improved by 19.96% and 27.32%, respectively, compared to the original MYD05 TPW and AMSR2 TPW. • A new two-step TPW fusion method was proposed based on MW and NIR remote sensing. • The influence of ∆Tb 89 /∆Tb 36.5 on fused TPW was analyzed in all weather conditions. • The fused TPW had a significant improvement compared to the original TPW datasets. [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]

Satellite remote sensing of cloud property retrieval and shortwave downward radiation (SWDR) estimation is essential for global radiation budget and climate change studies. Sun glint areas remain a challenge for the existing cloud and SWDR algorithms based on the visible channel since surface specular reflection has a significant impact on satellite retrieval. In this study, a set of algorithms for cloud detection and cloud microphysical parameter estimation were developed using infrared multichannel data from the new generation geostationary satellite Himawari-8 based on the random forest method. The results indicated that the cloud retrieval algorithm exhibited better performance in the sun glint areas where the official Himawari-8 products (cloud detection and cloud optical thickness) were overestimated. We developed a new SWDR estimation algorithm combining the radiative transfer model and machine learning techniques by considering the cloud properties from the cloud retrieval algorithm. The results indicated that the SWDR and cloud radiative forcing derived by the new algorithm were more consistent with those of the well-known radiation products Cloud and the Earth's Radiant Energy System than those estimated using the official-based cloud product, with decreases in the root mean square error of approximately 22% and 41%, respectively. The new algorithms effectively addressed sun glint contamination by providing more data coverage and exhibiting stable performance on a spatiotemporal scale. • Developing flexible algorithms for clouds and SWDR inversion using infrared bands. • Addressing the uncertainty of clouds and SWDR retrieval in the sun glint area. • Leveraging retrieval by combining machine learning and radiation calculations. [ABSTRACT FROM AUTHOR]

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP-L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i.e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m3/m3, respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m3/m3, respectively) performed better than ERA5-Land (R = 0.46; ubRMSE = 0.03 m3/m3). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs. • SSM and RZSM products are evaluated over the permafrost region of QTP • Uncertainties of SSM and RZSM products are analyzed • SSM products generally overestimate the in-situ measurements • Large errors were found in RZSM for both satellite-based and reanalysis products [ABSTRACT FROM AUTHOR]

Most algorithms for land surface temperature (LST) retrieval depend on acquiring prior knowledge. To overcome this drawback, we propose a novel LST retrieval method based on model-data-knowledge-driven and deep learning, called the MDK-DL method. Based on the expert knowledge and radiation transfer model, we deduce LST retrieval mechanism and determine the best combination of the thermal infrared (TIR) bands of the sensor. Then, we use the radiation transfer model simulation and reliable satellite-ground data to establish a training and test database, and finally use the deep learning neural network for optimal computation. Three typical high-, medium- and low-spatial-resolution TIR remote sensing datasets (from Gaofen, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Fengyun) are used for theoretical simulation and application analysis. The simulation shows that the minimum mean absolute error (MAE) is less than 0.1 K (standard deviation: 0.04 K; correlation coefficient: 1.000) at a small viewing direction (<7.5°) and less than 0.8 K at a large viewing direction (<65°). The in situ validation shows that the minimum MAE obtained by the optimal band combination is approximately 1 K (root mean square error (RMSE) = 1.12 K; coefficient of determination (R2) = 0.902). The retrieval accuracy is improved by increasing the number of TIR bands in the atmospheric window, and adding accurate atmospheric water vapor information produces better results. In general, four TIR bands in the atmospheric window bands are sufficient to retrieve the LST with high accuracy. Likewise, three TIR bands plus atmospheric water vapor information are sufficient for the retrieval requirements. All analyses indicate that our method is feasible and reliably accurate and can also be used to help design the instrument band to retrieve the LST with high precision. [Display omitted] • A novel LST retrieval method is proposed based on model-data-knowledge-driven and DL. • Abduction is introduced from artificial intelligence to retrieve LST. • The DL-NN algorithm make good use of the relationship among geophysical parameters. • DL-NN can be used to solve and optimize complex LST retrieval calculations. [ABSTRACT FROM AUTHOR]