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Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture (SM) retrieval. However, the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface. Introducing redundant ancillary data may also result in over-reliance problems. Therefore, we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS (CYGNSS) observations and Soil Moisture Active and Passive (SMAP) product. Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP, we establish five models for each grid with different parameters to achieve global SM retrieval. Subsequently, an optimal model, determined by the performance indicator, is used for SM retrieval. The results show that the root mean square error S RMSE with the improved method is decreased by 9.1% using SMAP SM as reference with the S RMSE = 0.040 cm3/cm3 compared with using single reflectivity-temperature-vegetation method. Additionally, using the in-situ SM of International Soil Moisture Network as reference, the overall correlation coefficient R and S RMSE values with the improved method are 0.80 and 0.064 cm3/cm3, respectively. The average R of the chosen sites is increased by 22.7%, and the average S RMSE is decreased by 8.7%. The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data. [ABSTRACT FROM AUTHOR]

Abstract Spaceborne global navigation satellite system-reflectometry has become an effective technique for Soil Moisture (SM) retrieval. However, the accuracy of global SM retrieval using a single model is limited due to the complexity of land surface. Introducing redundant ancillary data may also result in over-reliance problems. Therefore, we propose a method for SM retrieval that considers geographical disparities using the data from Cyclone GNSS (CYGNSS) observations and Soil Moisture Active and Passive (SMAP) product. Based on the CYGNSS effective reflectivity and ancillary datasets of SMAP, we establish five models for each grid with different parameters to achieve global SM retrieval. Subsequently, an optimal model, determined by the performance indicator, is used for SM retrieval. The results show that the root mean square error $$S_{\mathrm{RMSE}}$$ S RMSE with the improved method is decreased by 9.1% using SMAP SM as reference with the $$S_{\mathrm{RMSE}}$$ S RMSE = 0.040 cm3/cm3 compared with using single reflectivity-temperature-vegetation method. Additionally, using the in-situ SM of International Soil Moisture Network as reference, the overall correlation coefficient $$R$$ R and $$S_{\mathrm{RMSE}}$$ S RMSE values with the improved method are 0.80 and 0.064 cm3/cm3, respectively. The average $$R$$ R of the chosen sites is increased by 22.7%, and the average $$S_{\mathrm{RMSE}}$$ S RMSE is decreased by 8.7%. The results indicate that the improved method can better retrieve SM in both global and local scales without redundant auxiliary data.

Drought is a disaster that seriously constrains economic development and endangers human life. This paper explores the potential of Global Navigation Satellite System Reflectometry (GNSS-R) for drought monitoring, using Cyclone Global Navigation Satellite System (CYGNSS) data to monitor drought in Jiangxi and Hunan Provinces, China, in 2022. This study applies the Random Undersampling Boosting (RUSBoost) algorithm to detect waterbodies and linear regression to retrieve soil moisture (SM). Result shows that drought in September was heaviest, with the area of Poyang Lake in Jiangxi and Dongting Lake in Hunan decreasing by 70.2% and 76.9%, respectively, compared to that in June. The variation in retrieved SM shows that the Poyang Lake Plain and Jitai Basin in Jiangxi and the Dongting Lake, Yuanjiang River, and Xiangjiang River basins in Hunan suffered from the most serious drought. The variation in retrievals shows high consistency with various reference datasets, including Soil Moisture Active Passive (SMAP) SM data and vegetation condition index (VCI). The correlation coefficient between retrieved SM and VCI is 0.93 in Jiangxi and 0.94 in Hunan. [ABSTRACT FROM AUTHOR]

ABSTRACTThe estimation of soil moisture (SM) utilizing the data from the Cyclone Global Navigation Satellite System (CYGNSS) has attracted significant interest in recent times. However, CYGNSS’ inherent capability of variable resolution has not been fully exploited, often resulting in a loss of detailed spatial information in the raw data. In this paper, a novel downscaling scheme tailored for CYGNSS data is introduced to yield a “self-adjusting adaptive resolution” SM product, which dynamically varies the resolution of SM estimates based on the available CYGNSS data resolution at different geographic locations. Initially, a direct quantitative relationship is established between the key CYGNSS parameters reflecting SM variations and the reference SM from the Soil Moisture Active Passive (SMAP) mission with a coarse resolution of 36 km. This model is then applied to CYGNSS observations with resolutions down to 3 km to generate high-resolution, self-adjusting SM estimates that better conserve the fine-scale information linked to the original CYGNSS data. Extensive experimental results with error ratio diagrams show that the advanced geographically weighted regression (GWR)-based SM estimation method outperforms other competing estimation models and better retains localized spatial relationships and patterns. This study underscores the potential of CYGNSS as a novel and robust independent data source capable of delivering fine-resolution SM estimations by harnessing its unique multiresolution observational capability.

The agricultural productivity and the optimized use of water resources rely on the soil moisture (SM) retrieval to achieve some of the sustainable development goals, such as ensuring food security and monitoring climate change. One of the main aspects to provide accurate SM retrieval results is the selection of the most effective models. This study is carried out to exhibit the impact of three different soil formulations [i.e., Linear, Oh, and improved integral equation model (I2EM)] within the water cloud model (WCM). The experiments are conducted based on the combined use of Sentinel-1 and Sentinel-2 images. The in-situ measurements used in this work are collected from five different fields in Huamantla, Central Mexico. The experiments focus on the complete growing season of corn taking into consideration the soil and the vegetation contribution. The best Bias and unbiased root mean squared difference (ubRMSD) values obtained by the Oh-WCM are equal to −0.437 and 0.295 dB, respectively at VV in PX1. The I2EM-WCM achieved Bias and ubRMSD values equal to −0.760 and 0.379 dB at VV, respectively. The linear-WCM also obtained low Bias and ubRMSD values equal to −0.297 and 0.322 dB, respectively. Therefore, the combination of the Oh model within the WCM is considered as the appropriate combination for the SM retrieval due to its high achieved accuracy. The sensitivity analysis of changes in $\sigma ^{0}_{pq,\text{tot}}$ due to changes in SM found that it is possible to capture changes higher than 0.06 m$^{3}$/m$^{3}$ in SM over the complete growing season of corn using C-band backscatter observations.

Land surface temperature (LST) is an important parameter for the study related to land–air coupled systems. Satellite-based passive microwave (PMW) sensor is a significant approach to retrieving LSTs, which can penetrate the atmosphere conditions. However, soil moisture is one of the variables affecting PMW brightness temperature, yet few existing methods have considered it in the procedure of retrieving LSTs. On this basis, we propose a retrieval algorithm for PMW-based LST that comprehensively considers soil moisture changes and land scenarios (RA-PLST-SM) to take the soil moisture into account. Additionally, two fusion strategies one of which fuses the landform and soil moisture, and the other further fusing with land cover are proposed to construct the land environment description. Besides, we also propose a fusion strategy to integrate the LSTs from the time-interval-based models of month, quarter, and year. The mean root-mean-squared error (RMSE) referring to MODIS LSTs shows that RA-PLST-SM LSTs gain 3.13 and 2.32 K at day/night time. Also, referring to advanced LST data product, RA-PLST-SM LSTs present a yearly mean STD of 4.12 and 2.48 K at day/night time. Furthermore, the metric results according to six in situ stations demonstrate that RA-PLST-SM LST is significantly enhancing the consistency of actual LSTs, obtaining a mean RMSE of 4.42 K/3.48 K at day/night time. Moreover, the LST fusion strategy proposed in this article can effectively improve the quality of results. The progress of this article can promote the research fields to acquire cloudy LSTs and even provide the technique reference for PMW-based LST retrieval.

Understanding soil moisture (SM) at high spatio-temporal resolution provides crucial insights across various societal disciplines due to its direct impact on environmental and natural disaster monitoring, weather forecasting, agricultural productivity, and water resource management. In recent decades, a variety of algorithms have been developed to improve the spatial resolution of SM maps from passive sensors (∼40 km); however, the resulting maps, often with resolutions around 1 km or even hundreds of meters, still lack the necessary resolution for detailed local analysis. This study addresses this gap by presenting a machine learning methodology aimed at estimating SM at 60-m spatial resolution. A feedforward neural network is employed to capture the relationships among 14 different predictors, including several spectral bands and indices from Sentinel-2, land surface temperature from moderate-resolution spectroradiometer, elevation and slope from shuttle radar topography mission, precipitation from the fifth generation of the European Center for Medium-Range Weather Forecasts Reanalysis for Land, and the sand fraction from SoilGrids250m, with European Space Agency (ESA) Climate Change Initiative (CCI) SM serving as the target variable. The model is trained and applied over the central part of the Iberian Peninsula (38.9 °N–42.5 °N and 3.5 °W–7.2 °W) from 2019 to 2021. At 60-m resolution, the SM maps effectively capture the spatial heterogeneity of the terrain. The temporal analysis demonstrates that high-resolution SM maps preserve virtually the same sensitivity as those from the ESA CCI, with a correlation of 0.66, a bias of 0.095 m3/m3, and an unbiased root-mean-square error of 0.044 m3/m3 on average.

Soil moisture (SM) is a critical parameter in understanding the Earth's hydrological cycle and managing water resources. Remote sensing instruments, such as Advanced SCATterometer (ASCAT), can provide valuable long-term SM. However, compatibility issues may arise when integrating ASCAT SM retrieval with another retrieval, such as soil moisture active passive (SMAP), a high-quality microwave radiometer-based SM retrieval. In this study, we propose a novel image-to-image translation approach based on the U-Net architecture to convert ASCAT SM data into the format of SMAP (ASCAT2SMAP). The resulting SM from the ASCAT2SMAP was evaluated using temporally separated SMAP data and independent in-situ SM measurement from the International Soil Moisture Network (ISMN). In the separately divided test periods, ASCAT2SMAP showed good agreement with SMAP with R of 928, ubRMSD of 0.043 $\text{m}^{3}/\text{m}^{3}$, and bias of 0.002 $\text{m}^{3}/\text{m}^{3}$. When evaluating ASCAT2SMAP with ISMN data, it showed a better agreement than ASCAT and more similar metrics with SMAP. Moreover, we found that the ASCAT2SMAP is more robust to a problem of subsurface scattering than the original ASCAT SM. When simulating V-polarized brightness temperature from ASCAT2SMAP SM, it showed good agreement with ubRMSD of 5.602 K and bias of −0.135 K. Our results are expected to provide a valuable perspective preceding to creation of harmonized SM datasets from different sensors, contributing to improved data integration and analysis in the field of geoscience and remote sensing.

Soil moisture (SM) is an important driver for forest ecosystems, creating a need for globally extensive SM information that can only be achieved with satellite-based sensors and/or process-based model. However, the reliability of remotely sensed or modeled SM data in forests is poorly understood due to a lack of suitable validation sites and interference with remote sensing caused by vegetation water content. Here, we examine three multiyear SM products: remotely sensed surface (0–5 cm) SM from combined soil moisture active passive (SMAP) and Sentinel-1 observations (SMAP/Sentinel); the SMAP Level-4 surface (0–5 cm) and root-zone (0–1 m) SM data assimilation product (SMAP-L4); and simulated surface (0–10 cm) and root-zone (0–1 m) SM from the North American land data assimilation system (NLDAS). These estimates were compared with in situ measurements from 39 National Ecological Observatory Network sites throughout the U.S. At 21 unforested sites, the performance of the three products was similar for surface SM, and all three were able to track temporal changes in surface SM. The performance of the three products declined at 18 forested sites; however, while the performance declined modestly for SMAP-L4 and NLDAS, SMAP/Sentinel performance declined so much that it was largely unable to track changes in surface SM. The SMAP-L4 and NLDAS products also reliably captured temporal changes in root-zone SM at both forested and unforested sites. Our findings indicate that both SMAP-L4 and NLDAS can be used to track surface and root-zone SM changes in forests (unbiased root-mean-square deviation: 0.03–0.06 m3 m−3).

Global Navigation System Reflectometry (GNSS-R) is a microwave remote sensing technology that enables Earth observation by receiving GNSS signals reflected from the Earth's surface. The Cyclone Global Navigation Satellite System (CYGNSS) constellation is a satellite system that uses GNSS-R technology with high temporal resolution and has been a popular data source in soil moisture retrieval in recent years. However, the constant movement of GNSS transmitters and GNSS-R satellites results in potentially chaotic and random observations of the Earth's surface, with many unevenly distributed gaps in the observed data. In this paper, a gap-filling method based on spatial autocorrelation is proposed to interpolate the gaps within these observation datasets, with SM being estimated post-interpolation. The sample set for the model comprises points surrounding the interpolation target, with modeling conducted considering factors of spatial weighting to estimate values at the interpolation target. Different autocorrelation-based gap-filling methods using CYGNSS data can achieve good estimation accuracy, and the data coverage after interpolation is on average 1.8 times greater than before interpolation. The gap-filling method using XGBoost achieves the best performance and offers the highest accuracy in SM estimation, with an average correlation coefficient of 0.8445, and an average RMSE of 0.0457 m3/m3. The gap-filling approach can significantly enhance data coverage and facilitate the filling of daily gaps in CYGNSS data with all maintaining high SM estimation accuracy. The estimation of daily missing values using CYGNSS data can fully exploit the embedded surface features in the data's fine resolution and can provide high-resolution SM retrieval.

Passive microwave remote sensing is a vital tool for acquiring valuable information regarding the Earth's surface, with significant applications in agriculture, water management, forestry, and various environmental disciplines. Precision agricultural (PA) practices necessitate the availability of field-scale, high-resolution remote sensing data products. This study focuses on the design and development of a cost-effective, portable L-band microwave radiometer capable of operating from an unmanned aircraft system platform to measure high-resolution surface brightness temperature ($T_{B}$). This radiometer consists of a dual-polarized (Horizontal polarized, H-pol and Vertical polarized, V-pol) antenna and a software-defined radio-based receiver system with a 30 MHz sampling rate. The post-processing methodology encompasses the conversion of raw in-phase and quadratic (I&Q) surface emissions to radiation $T_{B}$ through internal and external calibrations. Radiometric measurements were conducted over an experimental site covering both bare soil within an agricultural field and a large water body. The results yielded a high-resolution $T_{B}$ map that effectively delineated the boundaries between land and water, and identified land surface features. The radiometric temperature measurements of the sky and blackbody demonstrated a standard deviation of 0.95 K for H-pol and 0.57 K for V-pol in the case of the sky and 0.39 K for both H-pol and V-pol in the case of the blackbody observations. The utilization of I&Q samples acquired via the radiometer digital back-end facilitates the generation of different time–frequency (TF) analyses through short-time Fourier transform and power spectral density (PSD). The transformation of radiometer samples into TF representations aids in the identification and mitigation of radio frequency interference originating from the instrument itself and external sources.

In cold regions, the coexistence and interconversion of liquid water and ice in frozen soils have important implications for energy partitioning and surface runoff at the Earth's surface. Passive microwave remote sensing is crucial for the global monitoring of soil moisture (SM). However, current research on SM focuses mainly on unfrozen soil conditions. Limited studies have been conducted on variations in soil liquid water content throughout the freezing season. This article investigated the potential use of brightness temperature observations from the Soil Moisture Active Passive (SMAP) satellite for retrieving all-season liquid SM. The single-channel algorithm and the Zhang-Zhao dielectric model, which was specifically developed for freezing and thawing soils, achieved successful retrieval of liquid SM in both frozen and thawed soils, even when snow cover was present. The results indicate improved spatial coverage (during winter) and consistent spatial patterns in SM compared with the SMAP products. Validation at 17 SM networks suggests that the retrieved all-season liquid SM effectively captures the dynamic characteristics of each region with an average bias of 0.011 m3/m3, an average unbiased root mean square error of 0.056 m3/m3, and an average correlation coefficient of 0.76. The additional retrieval of unfrozen water content during the freezing season would enhance the monitoring and understanding of the hydrological cycle and energy balance in cold regions.

The vegetation and ecosystem in the source region of the Yangtze River and the Yellow River (SRYY) are fragile. Affected by climate change, extreme droughts are frequent and permafrost degradation is serious in this area. It is very important to quantify the drought–vegetation interaction in this area under the influence of climate–permafrost coupling. In this study, based on the saturated vapor pressure deficit (VPD) and soil moisture (SM) that characterize atmospheric and soil drought, as well as the Normalized Differential Vegetation Index (NDVI) and solar-induced fluorescence (SIF) that characterize vegetation greenness and function, the evolution of regional vegetation productivity and drought were systematically identified. On this basis, the technical advantages of the causal discovery algorithm Peter–Clark Momentary Conditional Independence (PCMCI) were applied to distinguish the response of vegetation to VPD and SM. Furthermore, this study delves into the response mechanisms of NDVI and SIF to atmospheric and soil drought, considering different vegetation types and permafrost degradation areas. The findings indicated that low SM and high VPD were the limiting factors for vegetation growth. The positive and negative causal effects of VPD on NDVI accounted for 47.88% and 52.12% of the total area, respectively. Shrubs were the most sensitive to SM, and the response speed of grassland to SM was faster than that of forest land. The impact of SM on vegetation in the SRYY was stronger than that of VPD, and the effect in the frozen soil degradation area was more obvious. The average causal effects of NDVI and SIF on SM in the frozen soil degradation area were 0.21 and 0.41, respectively, which were twice as high as those in the whole area, and SM dominated NDVI (SIF) changes in 62.87% (76.60%) of the frozen soil degradation area. The research results can provide important scientific basis and theoretical support for the scientific assessment and adaptation of permafrost, vegetation, and climate change in the source area and provide reference for ecological protection in permafrost regions. [ABSTRACT FROM AUTHOR]

Meteorological drought precedes the agricultural drought and studying the propagation time from meteorological to agricultural drought can substantially reduce agricultural losses. To find this propagation time between the meteorological and agricultural drought, this study analyzed the copula-based conditional probability between the Standardized Precipitation Index at 12 timescales (SPI-1 to12, meteorological drought) and Standardized Soil Moisture Index at 1 month timescale (SSI-1, agricultural drought), over the fifteen Agro-Climatic Zones (ACZs) of India. SSI is computed using a total column Soil Moisture (SM) derived from ESACCI SM using the Statistical Soil Moisture Profile (SSMP) model. The SSMP-based ESACCI SM is positively correlated (Correlation Coefficient of 0.871) with ERA5 Land SM. To compute the conditional probability, three copulas namely Frank, Clayton, and Gumbel copulas are fitted between SPI-1 to 12 and SSI-1. The Goodness of Fit analysis showed the dominance of the Gumbel copula among the Clayton, Frank, and Gumbel copulas. Gumbel copula completely dominated the SPI timescales of 1–3 and 11–12 whereas Clayton copula was relatively favored for SPI timescales of 4–10. The propagation time results revealed that the monsoon season (JAS) consistently exhibits the highest propagation time, averaging around 10 months followed by summer (AMJ, 7 months), spring (JFM, 4 months), and winter (OND, 2–3 months) seasons. The Mann-Kendall trend test revealed that the same season can have different trends in different ACZs and vice versa, underlining the localized and season-specific strategies for drought management, considering each ACZ's unique environmental, agricultural, and socio-economic conditions in each zone. Overall, this study showcases the capability of remote sensing and copula-based statistical models, in understanding meteorological to agricultural drought propagation. It also highlights the efficiency of statistical models in deriving total column SM from surface SM.

Soil moisture plays an important role in crop yield estimation, irrigation management, etc. Remote sensing technology has potential for large-scale and high spatial soil moisture mapping. However, offline remote sensing data processing is time-consuming and resource-intensive, and significantly hampers the efficiency and timeliness of soil moisture mapping. Due to the high-speed computing capabilities of remote sensing cloud platforms, a High Spatial Resolution Soil Moisture Estimation Framework (HSRSMEF) based on the Google Earth Engine (GEE) platform was developed in this study. The functions of the HSRSMEF include research area and input datasets customization, radar speckle noise filtering, optical-radar image spatio-temporal matching, soil moisture retrieving, soil moisture visualization and exporting. This paper tested the performance of HSRSMEF by combining Sentinel-1, Sentinel-2 images and in-situ soil moisture data in the central farmland area of Jilin Province, China. Reconstructed Normalized Difference Vegetation Index (NDVI) based on the Savitzky-Golay algorithm conforms to the crop growth cycle, and its correlation with the original NDVI is about 0.99 (P < 0.001). The soil moisture accuracy of the random forest model (R2 = 0.942, RMSE = 0.013 m3/m3) is better than that of the water cloud model (R2 = 0.334, RMSE = 0.091 m3/m3). HSRSMEF transfers time -consuming offline operations to cloud computing platforms, achieving rapid and simplified high spatial resolution soil moisture mapping. [ABSTRACT FROM AUTHOR]

Background and Objectives: Natural vegetation cover is a crucial component of ecosystem change models and conservation efforts. The current research focuses on examining the spatial-temporal correlation of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) with meteorological indicators such as precipitation (P) and the Multivariate Standardized Precipitation Index (MSPI); biological indicators such as evapotranspiration (ET) and soil moisture (SM); and soil indicators, specifically land surface temperature (LST), using satellite imagery from Google Earth Engine over the period of 2000-2021 in Hormozgan Province. Materials and Methods: To evaluate the correlation between vegetation indices and climatic and biological variables, Google Earth Engine imagery was utilized. NDVI and EVI indices, evapotranspiration, and land surface temperature were extracted from 16-day Terra satellite images, and soil moisture (0-10 cm) from Soil Maps (Open Land Map) within the time frame of 01.01.2000 to 01.01.2022, using programming functions on a monthly basis. Precipitation and the MSPI drought index were obtained from a 22-year statistical dataset of weather stations. Using the Kriging spatial analysis method in ArcGIS, a precipitation spatial map was created. The PCA method was employed to calculate the drought index in different locations using SPSS software. Anomalies of vegetation indices were extracted to better understand their temporal changes over two decades. Correlation and trend analysis of parameters were conducted using the Spearman and Kendall methods. Results: The Spearman correlation coefficient and the Kendall rank correlation coefficient between the NDVI and EVI indices were found to be significant at 0.84 and 0.67, respectively, at a 99% confidence level. The temporal distribution of vegetation indices showed that their annual and monthly variations corresponded to the amount of autumn cultivation. In the warm and dry climate of the province, most of the precipitation occurs from December to February. Consequently, the indices had their highest values during December, January, and February, coinciding with the agricultural planting and growth season. Results revealed that the spatial variation of vegetation indices was in line with regions with higher precipitation, riverbanks, agricultural lands, and orchards. A significant inverse relationship was observed between vegetation cover indices and land surface temperature, indicating an increase in vegetation cover with decreasing temperature, and vice versa. The suitable overlap of precipitation zones and the MSPI drought index with the spatial distribution of vegetation indices, land surface temperature, and soil moisture indicated the determining role of precipitation in dry and semiarid regions. Anomalies of indices indicated a weak positive correlation during the 1378-1389 period due to frequent and severe droughts. In the second decade (2011-2022), a strong and significant correlation was observed due to a decrease in drought intensity. According to the MSPI drought index, western, central, and some eastern parts of the province with the highest levels of drought and low precipitation had the lowest vegetation index values. Conclusion: The extensive area of the region has led to a greater concentration of vegetation indices in certain areas. The highest values of NDVI and EVI are found in riverbank areas and water sources, which coincide with the agricultural centers in the plains of Rudan, Minab, Shamir, Takht, and some parts of Hajjiabad, which are geographically located in the northeastern and northern regions. An increase in any of the vegetation indices indicates an increase in their extent and abundance. However, a decrease in any of these indices results from the heterogeneous relationship between vegetation and climatic, soil, and land use factors in the region. The dynamic trend of vegetation cover and its relationship with other factors should be studied in the long term to establish a pattern. This study was conducted to the best of our abilities and with the available dataset. Long-term studies can be conducted by extracting empirical variables at the regional level and considering the impact of population growth on the region. The findings of this study can contribute to future research related to vegetation cover and its relationship with other factors, as well as nature conservation and resource allocation. [ABSTRACT FROM AUTHOR]

Tremendous progress has been made in estimating soil moisture (SM) from satellite remote sensing data. Several global-scale coarse-resolution products have also been generated and released for various applications in the Earth system. However, high-resolution SM estimation is still in its infancy. Currently, two main methods are used for this purpose: downscaling approaches and direct retrieval from microwave and optical/thermal data. Several studies have attempted to comprehensively evaluate the performance of these approaches and have found that each method has its own strengths and weaknesses, with no single method outperforming the others. In this study, we aim to investigate the advantages of integrating optical, thermal, and microwave data to estimate SM by leveraging an intensive SM network and triple collocation (TC) method. First, we determined the best-performing coarse-resolution microwave SM product through the TC approach. Second, we generated 1-km SM using a downscaling approach based on land surface temperature and vegetation index, utilizing the best-performing SMAP L3 descending product. Third, we evaluated the high-resolution downscaled SM, Sentinel 1 SM, and SMAP/Sentinel 1 combined SM products using SM measurements from the REMEDHUS station network, ETOPO1 elevation, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation, and the European Space Agency (ESA) CCI land cover map. Finally, we investigated and demonstrated the advantages of merging these products through point-scale evaluation and large-scale spatial pattern comparison. [ABSTRACT FROM AUTHOR]

The paper aims to construct simple soil moisture(SM) retrieval model using Sentinel-1 synthetic aperture radar (SAR) data. The water cloud model (WCM) removed the contribution of vegetation to the radar backscattering coefficient, and the backscattering coefficient of soil was estimated. Based on the established SM retrieval model without soil roughness parameters, the SM in farmland and forest land was retrieved using radar VV-VH dual-polarization data. We considered the interference of uneven surfaces on the radar signal, added the radar local incidence angle parameter to improve the model, and constructed a semi-empirical SM retrieval model. The accuracy of the results showed Root Mean Square Error (RMSE) of 0.04 and the Pearson correlation coefficient (r) of 0.80. The SM retrieval model for removing soil roughness parameters can estimate soil moisture with reasonable accuracy. The influence of topographic factors (elevation, slope and aspect) on the retrieval results of the model was analyzed. It was found that the area with the steep slope and blocked radar signal is not conducive to estimate SM. The SM retrieval method constructed in this paper provides many advantages for some research and practical applications, and its application in other SAR data remains to be further studied. [ABSTRACT FROM AUTHOR]

Integrating satellite observations into hydrologic models has the potential to enhance soil moisture (SM) retrievals at high spatial and temporal resolutions. The hydrological model Soil and Water Assessment Tool (SWAT) can produce spatiotemporally continuous SM product with high resolution. This article proposed to physically couple the SWAT and Tau-omega radiative transfer model to retrieve SM with a spatial resolution of 30 m. First, the SWAT model integrated a physically based SM module used to simulate hourly SM, from which the brightness temperature (TB) at 30 m was obtained by forwardly simulating the Tau-omega model. Then, the simulated TB at fine resolution and Soil Moisture Active and Passive (SMAP) TB at coarse resolution were merged by Kalman filter into a disaggregated TB. Finally, SM at 30 m resolution was retrieved from the disaggregated TB. The proposed methods were evaluated over the Lanjiang River Basin. The results show that the retrieved SM is highly correlated with two official SMAP SM products and that it has a better accuracy than five SMAP SM products. Particularly, due to the high resolution, the retrieved product captured the heterogeneity of SM within and among fields, compared to the individual use of the hydrological model or satellite observation. Our proposed approach can aid the decision-making in agricultural management at the field scale.

Soil moisture (SM) is important in understanding Earth's hydrologic cycles. However, an overall performance of multisource SM products is still unclear due to a lack of comprehensive validation. Using SM simulated by hydrologic models as a reference to perform validation is a promising alternative since SM simulation is not restricted by its coverage or scale. In this study, an integrated hydrologic model (ParFlow-CLM) forced by hydrometeorological and agricultural water use reanalysis data is built in the middle reaches of the Heihe River Basin (HRB), a typical managed agricultural region in Northwestern China. Using the ParFlow-CLM simulated SM data as the validating reference, ten SM products, including four single-source RS SM, three merged SM, and three assimilated SM products are systematically assessed by a comprehensive evaluation framework composed of fifteen statistical performance indicators. For validation, the results show that ParFlow-CLM SM simulations agree with the time domain reflectometry probe and cosmic ray neutron probes observations. The merged SM and assimilated SM outperform the single-source RS SM products if compared with ParFlow-CLM simulated data. Results from the intercomparison demonstrate that hydraulic conductivity and leaf area index are the dominant factors in SM spatial variations based on the generalized additive model. The statistical linkage indicates that mean absolute deviation, uncertainty at 95% (U95), Nash–Sutcliffe's efficiency, and combined performance index serve as substitutes for quantifying the relative uncertainty of SM. This study paves a way for model-data intercomparison of SM products in the HRB as well as in other arid and semiarid basins.

As an essential climate variable, soil moisture (SM) exerts an indispensable influence on numerous disciplines. However, various degrees of data gaps exist in current microwave SM products. Therefore, this article proposed a spatiotemporal attention-based residual deep network (STARN) to reconstruct gaps of the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) over the Qinghai–Tibet Plateau (QTP) during unfrozen seasons (May to September) from 2001 to 2021. The developed model is an end-to-end residual network embedded with three attention modules to comprehensively consider the potential relationship between SM and surface variables. Evaluation results revealed that the proposed model could well reconstruct SM gaps with an overall median R and unbiased RMSE (ubRMSE) values of 0.52 and 0.054 m3/m3, while the overall median R and ubRMSE values for the ESA CCI SM were 0.41 and 0.058 m3/m3. Besides, comparison with five baseline methods (e.g., the artificial neural network, convolutional neural network, extreme gradient boosting, long-short term memory, and DCT-PLS model) indicated that the STARN model had certain advantages over the five baseline models with higher correlation and more reasonable distribution patterns. The R/ubRMSE values for the five models were 0.38/0.057, 0.34/0.058, 0.40/0.058, 0.41/0.056, and 0.41/0.058, respectively. The pretraining using the ERA5-Land SM data could further improve the accuracy of generated seamless SM data since the ERA5-Land and ESA CCI SM complemented each other to a certain extent on the QTP. In summary, by leveraging the spatiotemporal information and attention modules, the STARN model showed great potentials in SM gap filling.

Soil moisture (SM) is valuable basic data in climate, hydrological models, and agricultural applications. The rapid development of remote sensing technology can be used to monitor changes in SM at multiple spatial and temporal scales. In this article, we unfolded an SM retrieval method using ensemble learning combined with the Water Cloud Model (WCM) by Sentinel-1 and Sentinel-2 with multisource datasets. First, using the WCM, the influence of vegetation cover on the backscattering coefficient was removed, where we use three vegetation index (enhanced vegetation index (EVI), normalized difference vegetation index, and normalized difference water index) for analysis and comparison. Then, combined with other multisource datasets, an SM retrieval model was established based on the ensemble learning algorithm. Here, we choose two familiar ensemble learning algorithms for analysis and comparison, using Pearson correlation significance analysis, which are the random forest (RF) and the adaptive boosting (AdaBoost). The results revealed that the RF model performed is slightly superior to the AdaBoost model. The optimal performance mean absolute error, root-mean-square error (RMSE), and the unbiased RMSE of RF model are 2.289 vol%, 2.934 vol%, 2.934 vol%, respectively, which are slightly better than the AdaBoost model. EVI is suitable for WCM model to remove vegetation scattering effect. It shows that it is attainable to utilize the ensemble learning method to inversion of SM using radar data. The proposed framework maximizes the potential of WCM, RF model, and multisource datasets in deriving spatiotemporally continuous SM estimates, which should be valuable for SM inversion development.

The use of global navigation satellite system interferometric reflectometry (GNSS-IR) to measure vegetation growth status has become a rapidly growing technique in remote sensing. GNSS signals reflected by the soil surface affect the accuracy of vegetation growth status (vegetation cover density) measurement, and the influence of soil moisture (SM) varies. This study establishes a calibration model that can reduce the influence of SM and snow layer on reflectivity. We used a direct-reflected signal amplitude ratio and GNSS-IR altimeter based on the Lomb–Scargle Periodogram to calculate the reflectivity of vegetation and snow layer depth. GNSS data from plate boundary observation were used to verify the validity of our model. The results show that reflectivity correlates better with vegetation growth status after calibrating the influence of the SM and snow layer. Moreover, the correlation increased by nearly 0.14. This study analyzed the influence of the snow layer and found that it had a noticeable effect on vegetation growth status measurement when the snow depth was over 30 cm. Furthermore, a fusion method is proposed to improve the accuracy of vegetation growth status measurement by combining the reflectivity and normalized microwave reflection index (NMRI). The experimental results show that better performance can be obtained compared to the single observation of the reflectivity and NMRI, and the best correlation between the measured and in situ normalized difference vegetation index is over 0.91, and the root mean square error decreases to 0.1893.

The vegetation and ecosystem in the source region of the Yangtze River and the Yellow River (SRYY) are fragile. Affected by climate change, extreme droughts are frequent and permafrost degradation is serious in this area. It is very important to quantify the drought–vegetation interaction in this area under the influence of climate–permafrost coupling. In this study, based on the saturated vapor pressure deficit (VPD) and soil moisture (SM) that characterize atmospheric and soil drought, as well as the Normalized Differential Vegetation Index (NDVI) and solar-induced fluorescence (SIF) that characterize vegetation greenness and function, the evolution of regional vegetation productivity and drought were systematically identified. On this basis, the technical advantages of the causal discovery algorithm Peter–Clark Momentary Conditional Independence (PCMCI) were applied to distinguish the response of vegetation to VPD and SM. Furthermore, this study delves into the response mechanisms of NDVI and SIF to atmospheric and soil drought, considering different vegetation types and permafrost degradation areas. The findings indicated that low SM and high VPD were the limiting factors for vegetation growth. The positive and negative causal effects of VPD on NDVI accounted for 47.88% and 52.12% of the total area, respectively. Shrubs were the most sensitive to SM, and the response speed of grassland to SM was faster than that of forest land. The impact of SM on vegetation in the SRYY was stronger than that of VPD, and the effect in the frozen soil degradation area was more obvious. The average causal effects of NDVI and SIF on SM in the frozen soil degradation area were 0.21 and 0.41, respectively, which were twice as high as those in the whole area, and SM dominated NDVI (SIF) changes in 62.87% (76.60%) of the frozen soil degradation area. The research results can provide important scientific basis and theoretical support for the scientific assessment and adaptation of permafrost, vegetation, and climate change in the source area and provide reference for ecological protection in permafrost regions.

The effect of soil moisture (SM) on the onset of East Asian subtropical summer monsoon (EASSM) is investigated based on multiple sets of reanalysis data in the period of 1981–2010. It is found that the EASSM is characterized by persistent 2-m s−1 southerly winds for about 3 months in spring at 850 hPa over the subtropical region of East Asia. Considering this feature of the meridional winds, we define the EASSM onset date, and obtain that the climatological onset date is pentad 17.7, around 26 March. On the interannual timescale, the onset date of EASSM exhibits statistically significant correlation with the SM in southeastern China in the month preceding the onset, with wetter (drier) conditions being associated with later (earlier) onset. The physical process by which the preceding SM affects the EASSM onset is further explored by examining the surface energy balance as well as its impacts. Positive (negative) SM anomalies in southeastern China in the month before onset may induce negative (positive) surface temperature anomalies. The decreased (increased) surface temperature in southeastern China before the EASSM onset weakens (strengthens) the zonal sea-land thermal contrast in the surface and low-level atmosphere in the subtropical East Asia. The zonal sea-land thermal contrast in wetter (drier) years induces anomalous northerly (southerly) winds over southeastern China, which tends to delay (advance) the zonal thermal seasonal transition in spring and is conducive to a later (earlier) onset of EASSM. These results are helpful for understanding and prediction of the variability of EASSM and the EASSM onset. [ABSTRACT FROM AUTHOR]

The sensitivity of Cyclone Global Navigation Satellite System (CyGNSS) data to inland water bodies was well documented, however, its advantage over other sensors has seldom been reported. In this work, a semantic segmentation method is adopted for detecting inland water bodies using the CyGNSS data. The widely used LinkNet with the global attention mechanism (GAM) and atrous spatial pyramid pooling (ASPP), namely GA-LinkNet, is equipped to better extract water distributions. The performance comparison with an existing method and other deep networks proved the accuracy and effectiveness of this approach. Satisfactory agreement between the derived and referenced water masks was achieved, with the overall accuracy being 0.959 and 0.976, the mean intersection over union being 0.785 and 0.641, and the F1 scores being 0.879 and 0.781 for the Amazon and Congo regions, respectively. Furthermore, underestimation of water by the reference data was shown during evaluation, which proves the usefulness of the CyGNSS-derived water mask for improving the existing water mask products. [ABSTRACT FROM AUTHOR]

• COSMOS is an effective method for SM estimation in coastal salinized areas. • COSMOS is used to quantify the effect of soil salinity on multi-scale SM data. • SM sensors can be evaluated by COSMOS in saline soil and further calibrated. • L-band is more disturbed by soil salinity than C-band for satellite-based SM. Accurate Soil Moisture (SM) measurements are essential for agronomic practices and environmental applications in coastal saline areas. However, soil salinity can introduce significant errors in state-of-the-art SM measurements based on dielectric theory. The Cosmic-ray Soil Moisture Observing System (COSMOS) has the advantage of measuring SM independent of soil salinity, with a field-scale resolution between the point scale and the coarse scale. This paper utilizes COSMOS records to assess the effect of salinity on three types of SM sensors (HydraProbe, CS655, and 5TE) and five mainstream satellite-based SM products (AMSR-2, ACSAT, SMOS, SMAP, and CYGNSS). The experimental site is located in a typical coastal saline area of the Yellow River Delta, China. The findings and conclusions mainly include: 1) all three types of SM sensors are affected by the soil salinity, the HydraProbe outperforms CS655 and 5TE when compared to COSMOS SM, and the accuracy of HydraProbe and CS655 could be improved based on a proper calibration method, 2) L-band SM products are more disturbed by soil salinity than C-band, with mean ubRMSE=0.102 m3 m−3 for L-band vs. mean ubRMSE=0.068 m3 m−3 for C-band, and 3) COSMOS has the potential to bridge the knowledge gap in multi-scale dielectric-based SM from point scale to coarse scale. [ABSTRACT FROM AUTHOR]

Recently, soil moisture (SM) has been estimated using Cyclone Global Navigation Satellite System (CYGNSS) data. Machine learning (ML) algorithms for CYGNSS SM estimation can minimize unpredictable influences and help improve the accuracy of SM retrieval. However, ML-based CYGNSS SM estimation requires ancillary data from other sources, and thus, the uncertainty, internal errors, and even dependence on external parameters of this process may complicate and limit SM estimation. In this article, a simple land type (LT) digitization strategy that incorporates the idea of classification is proposed with feature optimization to achieve an effective and independent SM retrieval without any other auxiliary data. The input features are chosen from the CYGNSS data themselves, and the corresponding labels (digitized stable LTs) are used in the training stage of the SM estimation model. During the fine-tuning stage, several input features (such as the dielectric constant and incident angle) are compared and selected after optimization to achieve better results. Moreover, the CYGNSS data are gridded at 9 × 9 km to validate the enhanced soil moisture active passive mission SM products at a resolution of 9 km. Only three input variables are adopted for the SM learning model, which are directly derived from the CYGNSS data for independently estimating SM at a high spatial resolution. Powerful performance is achieved by extreme gradient boosting based on a LT digitalization strategy, with root-mean-square error (RMSE) and unbiased RMSE (ubRMSE) values of 0.063 cm3/cm3 and a correlation coefficient (R) of 0.71 for the entire dataset. The performances of different ML learning models for various LTs are presented. The mean ubRMSE and RMSE are 0.041 cm3/cm3 and 0.057 cm3/cm3, respectively. The results demonstrate the effectiveness of the proposed LT digitization strategy for retrieving SM from CYGNSS data with various ML methods and the capability of SM estimation using the CYGNSS product as a new independent source.

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error 3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio–temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Building climate data records of soil moisture (SM) requires computing long time series by merging retrievals from sensors on-board different satellites, which implies to perform a bias correction or rescaling on the original time series. Due to their long time span and high temporal frequency, model data could be used as a common reference for the rescaling. However, avoiding model dependence in observational climate data records is needed for some applications. In this article, the possibility of using as reference remote sensing data from one of the $L$-band sensors specifically designed to measure SM is discussed. Advanced Microwave Scanning Radiometer 2 SM time series were rescaled by matching their cumulative distribution functions (CDFs) to those of Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Global Land Data Assimilation System (GLDAS) NOAH model time series. The CDF computation was investigated as a function of the time series length, finding significant differences from four to nine years. Replacing temporal by spatial variance does not allow us to compute better CDFs from short time series. The rescaled time series show a high correlation ($R>0.8$) to the original ones and a low bias with respect to the reference ($< $0.03 m $^{3}\cdot$ m$^{-3}$). The time series rescaled using several SMOS or SMAP datasets were also evaluated against in situ measurements and show performances similar to or slightly better than those rescaled using the model GLDAS. The impact of random errors and gaps of the observational data into the rescaling was evaluated. These results show that it is actually possible to use $L$-band data as reference to rescale time series from other sensors to build long time series of SM.

Soil moisture (SM) information can be routinely obtained from high-quality microwave retrievals at a global scale—such as datasets generated by the Soil Moisture Active Passive (SMAP) mission. In this article, using mutual information (MI) theory, we investigate the efficacy of the downscaled SMAP/Sentinel-1 L2 3-km EASE-Grid SM product (SPL2) for the detection of agricultural drought over northwestern China. The SPL2 is generated by merging SMAP enhanced radiometer data with Sentinel-1 radar observations. To evaluate the efficiency of the SPL2 downscaled algorithm, the SMAP Enhanced L3 Radiometer 9-km EASE-Grid SM product (SPL3) is also utilized as a non-downscaled baseline. Over croplands, comparing normalized MI (NMI) values sampled between the NDVI time series and 3-km Sentinel-1 C-band backscatter coefficient (σ) from SPL2 with NMI values between NDVI and SPL3 radiometer brightness temperature (Tb; resampled to 3-km resolution), we find that the Sentinel-1 σ explains more (3-km) NDVI information than the SPL3 Tb, as the NMI between σvh (σvv) and NDVI is 15% (8%), larger than that between SPL3 Tb and NDVI (5%). However, compared to the SPL3 Tb baseline, the information from downscaled SPL2 Tb on NDVI is reduced by approximately 3%, and the SPL2 algorithm extracts only 7% (10%) of the total information available from both enhanced SPL3 Tb and Sentinel-1 σvh (σvv). Overall, the C-band σ signal provides valuable information for vegetation monitoring due to its frequency advantage. However, additional efforts should be focused on SPL2 merging algorithms to improve the value of the downscaled SPL2 product for agricultural applications.

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm3·cm−3. The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm3·cm−3. This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.

The evapotranspiration and recharge are the key fluxes that link the land's water and energy cycles. These fluxes have major impacts on agriculture, water supply system, water resources planning, climate, etc. Accurate mapping of these fluxes is limited due to lack of in-situ observation sites and errors in estimation from numerical models. The dynamics of land surface state observations, specifically land surface soil moisture (SM) and temperature (LST), have implicit information on the partitioning of evapotranspiration and recharge fluxes, which provides an opportunity for global mapping of these fluxes across a range of spatial and temporal scales. In this study, a variational data assimilation (VDA) framework is developed for coupled estimation of evapotranspiration and recharge fluxes by assimilating LST and SM observations into a coupled water and energy balance model. We modified LIDA framework (Abdolghafoorian and Farhadi, 2019) to calculate the effective soil hydraulic parameters required for estimating the recharge flux in addition to evaporative flux parameters. Two numerical experiments are conducted using synthetic dataset generated by Hydrus 1-D model to test the accuracy and feasibility of the proposed estimation framework. Second-order information is used to estimate the uncertainty of the estimated parameters and fluxes and guide toward a well-posed optimization problem. Results demonstrate the success of the VDA framework in estimating evaporative and recharge fluxes from implicit information contained in SM and LST data and feasibility test results show promise in extending the framework to large scale using remotely sensed observations.

Soil moisture (SM) is one of the key measures to understand the land-atmosphere interaction and permafrost dynamics in the Qinghai–Tibet Plateau (QTP). ERA5-Land is a new reanalysis product with high spatial resolution (9 km), which can provide long-term SM data with a large spatial coverage as well as at multilayer soil depths. However, preliminary comparisons with in-situ data show that the ERA5-Land SM product generally underestimates the seasonal variability and demonstrate a positive bias on the QTP. In this article, we proposed to utilize the mode decomposition method to correct such bias. Specifically, through using the variational mode decomposition, we decomposed the long time-series of ERA5-Land SM data into a series of intrinsic mode functions, and found that the SM seasonal variation can be well represented by the low-frequency modes, which were then selected to feed a regression model for the bias correction. The single-site bias correction results show that our method significantly improves the accuracy of ERA5-Land SM product with bias reduced by 0.22 m3/m3, 0.3 m3/m3, and 0.15 m3/m3 for alpine meadow, alpine steppe, and alpine desert sites, respectively. Together with the slightly reduced accuracy but still acceptable results for the cross-site bias correction, we successfully demonstrate the potential of the mode decomposition method for the bias correction of the ERA5-Land SM product at regional scale. Our method is of great use to study climate impact on regional ecohydrological processes and the permafrost changes in the QTP region.

This study proposes a low-cost and “proof-of-concept” methodology to obtain high spatial resolution soil moisture (SM) via processing reflected global positioning system (GPS) and a multispectral camera data acquired by small unmanned aircraft system (UAS) platforms. An SM estimation model is developed using a random forest (RF) machine-learning (ML) algorithm by combining features obtained from reflected GPS signals (collected by smartphones and commercial off-the-shelf receivers) in conjunction with ancillary vegetation indices from the multispectral camera data. The proposed ML algorithm uses in situ SM measurements acquired via SM probes as labels. A preliminary field experiment was conducted on 210 by 110 m (2.31 ha) crop fields (corn and cotton) in 2020 (from January to November, including crop planting through senescence time period) at Mississippi State University (MSU)’s the heavily instrumented North Farm to acquire data needed for the ML model to train and test. Our results showed that both fields could be covered by GPS reflectometry measurements with about 13 min of flight time at a 15-m altitude, and SM can be mapped with 5 × 5 m spatial resolution (corresponding to the elongated first Fresnel zone). The model is trained with and validated against eight in situ SM station datasets via tenfold and leave-one-probe-out cross-validation techniques. Overall, root-mean-square errors (RMSE) of 0.013 m $^{3}$ m$^{-3}$ volumetric SM and R-value of 0.95 [-] are obtained for tenfold cross validation. The proposed model reached an RMSE of 0.033 m$^{3}$ m$^{-3}$ and an R-value of 0.5 [-] in leave-one-probe-out cross validation. While having limited data, the results indicate that high-resolution SM measurement can be achieved with a low-cost GPS reflectometry system onboard a small UAS platform for use in precision agriculture applications.

A surface soil moisture (SM) condition at high spatiotemportal resolutions is required by regional Earth system applications. Here, we mapped daily 1-km SM in the Babao River Basin in the northwest of China during the summers from 2013 to 2015 using a random forest (RF) method by merging SM information retrieved from in situ measurements, optical/thermal remote sensing, and topographical indices. Relative importance analysis was used to determine the optimal predictors for estimating high-resolution SM. A specific RF model (RFVI+sup) was constructed using the optimal predictors including remote sensing albedo, apparent thermal inertia (ATI), normalized difference vegetation index, normalized difference infrared index 5, soil adjusted vegetation index, and topographical indices (aspect and elevation). The RFVI+sup also accounted for missing observations of the thermal index (e.g., ATI) over the mountainous regions. In the comparison between the SM estimates using the new RFVI+sup model and other RF models, the spatial coverage of available estimates increased from 14% to 64% over the study region, the correlation coefficient values were improved to 0.75, the unbiased root-mean-squared difference values decreased to 0.032 m3/m3. Thus, the proposed RF method provided accurate SM estimates with high spatiotemporal resolution over the mountainous regions, by merging multiresource datasets from in situ measurements, remotely-sensed, and topographical indices.

Currently, land surface models (LSMs) are limited in representing realistic water and energy fluxes owing to the absence of reliable parameterization of irrigation. In this study, a novel method was employed to incorporate irrigation in the Community Land Model (CLM) Version 4.0. Two CLM experiments were set up, designated CLM-default run and CLM-irrigated run. The SM2RAIN algorithm was employed to reproduce the observed precipitation and irrigation using soil moisture (SM) information measured at the Fluxnet sites. The results showed that SM2RAIN reliably reproduced the observed precipitation on a daily timescale (R∼0.70 for all three sites) but significantly underestimated high-intensity precipitation (bias ∼0.5 mm day−1 for all sites). The bias-corrected SM2RAIN output showed improved representation of observed daily precipitation (R=0.89 and 0.86) and monthly irrigation (R=0.89 and 0.96) at US-Ne1 and US-Ne2, respectively. The SM2RAIN estimated irrigation was input to CLM as independent forcing data along with other atmospheric forcings. The simulated surface energy fluxes from CLM were compared with eddy covariance–based flux tower observations. The results showed that CLM simulated energy fluxes from the CLM-irrigated run improved the representation of turbulent heat fluxes (latent and sensible). Overall, mean bias decreased by 32% and 64% for sensible and latent heat fluxes, respectively. This indicates that SM2RAIN-estimated irrigation is reliable input data for LSMs that potentially improved model representations of surface energy fluxes, which are important for comprehending the complex interactions between land surface and atmosphere in irrigated areas. A novel method of estimating actual irrigation amount is presented for incorporating into land surface models (LSMs). The primary goal of the study was more accurate simulation of land surface states and fluxes by better representing agricultural land use. Moreover, this technique may allow numerical weather prediction (NWP) models to more precisely represent land–atmosphere feedback in managed areas, hence improving forecast skill. It was demonstrated in this study that using the novel irrigation method in LSMs can reliably represent surface water and energy fluxes. The findings of this study are very relevant to regional-to-global–scale water and energy cycle research, which has struggled to quantify the consequences of agricultural management practices like irrigation in the past. It is anticipated that improved representation of managed lands will make it possible to provide better weather and climate forecasting when the new irrigation plan is incorporated in NWP models. [ABSTRACT FROM AUTHOR]

In recent years, flash floods have become increasingly serious. Improving the runoff simulation and forecasting ability of hydrological models is urgent. Therefore, data assimilation (DA) methods have become an important tool. Many studies have shown that the assimilation of remotely sensed soil moisture (SM) data could help improve the simulation and forecasting capability of hydrological models. Still, very few studies have attempted to assimilate SM data from land surface process models into hydrological models to improve model simulation and forecasting accuracy. Therefore, in this study, we used the ensemble Kalman filter (EnKF) to assimilate the China Land Data Assimilation System (CLDAS) SM product into the MISDc model. We also corrected the CLDAS SM and assimilated the corrected SM data into the hydrological model. In addition, the effects of the 5th and 95th percentiles of flow were evaluated to see how SM DA affected low and high flows, respectively. Additionally, we tried to find an appropriate size for the number of ensemble members of the EnKF for this study. The results showed that the EnKF SM DA improved the runoff simulation ability of the hydrological model, especially for the high flows of the model; however, the simulation for the low flows deteriorated. In general, SM DA positively affected the ability of the MISDc model runoff simulation. [ABSTRACT FROM AUTHOR]

Microwave vegetation optical depth (VOD) and soil moisture (SM) can be simultaneously retrieved based on L-band radiometry with polarization information. VOD is indicative of the vegetation water content (VWC) because it captures the extinction of land surface emission. If the connectivity of VOD to VWC is robust, the pair of VWC-SM observations can be viable bases for understanding soil–plant–atmosphere water relations, providing new perspectives on ecosystem science. Simultaneous SM–VOD retrievals are feasible by inverting the $\tau -\omega $ model with two independent datasets in dual-channel algorithms. However, given correlated satellite vertical and horizontal brightness temperatures (TBs; TBv and ${{\mathrm {TB}}}_{h}$), an ill-posed inverse problem arises where TB errors result in high uncertainties of retrievals. In this study, we apply the degrees-of-information (DoI) metric and propose a signal-to-noise ratio (SNR) metric to assess the “retrievability” of VOD given the Soil Moisture Active Passive (SMAP) TBv–TBh linear dependence. The application of these metrics allows determining where the VOD retrievals are robust and reliable. This is a necessary step in supporting the applications of VOD in ecology and hydrology. Results show that regions with mainly nonwoody vegetation have the best potential for VOD retrievals, though regularization is necessary. We then assess VOD time variations from two regularization products that reduce the impact of underdetermined inversions: the L3 dual-channel algorithm (L3-DCA) and the multitemporal dual-channel algorithm (MTDCA), which constrain VOD time dynamics with and without using a priori VOD climatology, respectively. Though they both reduce noise, especially in the VOD retrievals, they result in differences in VOD seasonal amplitude and coupling to SM at high frequencies as we outline here. [ABSTRACT FROM AUTHOR]

This study aims to estimate the soil moisture (SM) in all-sky agriculture areas using fully polarimetric synthetic aperture radar (SAR) Gaofen-3 (GF-3) data. The radar vegetation index (RVI) is obtained using the radar SAR data, which overcomes the difficulty that the optical data cannot construct the vegetation index in cloud-covered areas. The RVI is introduced into the water cloud model (WCM) to remove the contribution of vegetation to the total radar backscatter σ 0 and obtain the soil backscattering coefficients σ soil 0 with HH and VV polarization. Subsequently, σ soil 0 and radar frequency data are introduced into the Chen model, and a semi-empirical model of SM estimation is established. The main findings of this study are as follows: (1) Compared with the σ 0 , the σ soil 0 obtained by the WCM has a stronger correlation with the SM. (2) In the cloud covered area, the accuracy of the estimated SM by synergistically using the WCM and the Chen model is ideal. An RMSE of 0.05 and a correlation coefficient (r) of 0.69 are achieved. In this study, the SM estimation method is not affected by clouds, and it shows many advantages for sustainable development, monitoring soil drought degree, and other related research. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important parameter in land surface processes and the global water cycle. Remote sensing technologies are widely used to produce global-scale SM products (e.g., European Space Agency's Climate Change Initiative (ESA CCI)). However, the current spatial resolutions of such products are low (e.g., >3 km). In recent years, using auxiliary data to downscale the spatial resolutions of SM products has been a hot research topic in the remote sensing research area. A new method, which spatially downscalesan SM product to generate a daily SM dataset at a 16 m spatial resolution based on a spatiotemporal fusion model (STFM) and modified perpendicular drought index (MPDI), was proposed in this paper. (1) First, a daily surface reflectance dataset with a 16 m spatial resolution was produced based on an STFM. (2) Then, a spatial scale conversion factor (SSCF) dataset was obtained by an MPDI dataset, which was calculated based on the dataset fused in the first step. (3) Third, a downscaled daily SM product with a 16 m spatial resolution was generated by combining the SSCF dataset and the original SM product. Five cities in southern Hebei Province were selected as study areas. Two 16 m GF6 images and nine 500 m MOD09GA images were used as auxiliary data to downscale a timeseries 25 km CCI SM dataset for nine dates from May to June 2019. A total of 151 in situ SM observations collected on 1 May, 21 May, 1 June, and 11 June were used for verification. The results indicated that the downscaled SM data with a 16 m spatial resolution had higher correlation coefficients and lower RMSE values compared with the original CCI SM data. The correlation coefficients between the downscaled SM data and in situ data ranged from 0.45 to 0.67 versus 0.33 to 0.54 for the original CCI SM data; the RMSE values ranged from 0.023 to 0.031 cm3/cm3 versus 0.027 to 0.032 cm3/cm3 for the original CCI SM data. The findings described in this paper can ensure effective farmland management and other practical production applications. [ABSTRACT FROM AUTHOR]

Wheat is one of the most important crops worldwide, and thus, the use of remote sensing data for wheat monitoring has attracted much interest. Synthetic aperture radar (SAR) observations show that at C-band and VV polarization, wheat canopy attenuates the surface scattering component from the underlying soil during a significant part of its growth cycle. This behavior needs to be accounted for or corrected before soil moisture (SM) retrieval is attempted. The objective of this article is to develop a new method for wheat attenuation correction (WATCOR) applicable to Sentinel-1 VV time series and based solely on the information contained in the time series itself. The hypothesis of WATCOR is that without attenuation, VV backscatter would follow a stable long-term trend during the agricultural season, with short-term variations caused by SM dynamics. The method relies on time-series smoothing and changing point detection, and its implementation follows a series of simple steps. The performance of the method was compared by evaluating the correlation between backscatter and SM content in six wheat fields with available SM data. The water cloud model (WCM) was also applied as a benchmark. The results showed that WATCOR successfully removed the attenuation in the time series and achieved the highest correlation with SM, thus improving markedly the correlation of the original backscatter. WATCOR can be easily implemented, as it does not require parameterization or any external data, only an approximate indication of the period where attenuation is likely to occur. [ABSTRACT FROM AUTHOR]

In this paper, a soil moisture (SM) retrieval model from spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) observations that incorporates soil surface temperature (SST) for the first time is evaluated. Here, based on the grid scale, Cyclone GNSS (CYGNSS) reflectivity, SST and vegetation optical depth (VOD) are employed to estimate SM by a trilinear regression, while the other influence factors such as soil roughness and texture are regard as static. The results are compared with globally Soil Moisture Active Passive (SMAP) SM and in-situ measurements from International Soil Moisture Network (ISMN) over the year of 2018 respectively, showing a good consistency (R = 0.929 and RMSE = 0.043 cm3cm−3 against SMAP SM; R = 0.927 and RMSE = 0.042 cm3cm−3 against in-situ SM). Although the sensitivity of reflectivity to SST is found to be much smaller than that to SM from the simulation, the incorporation of SST is demonstrated to be effective in SM estimation for its coupling relationship with SM. In the comparison with SMAP SM, the improvements of RMSE by incorporating SST are varying degrees globally, and significant in many arid areas with an improvement of over 40%. In the in-situ validation, the overall RMSE decreases from 0.047 to 0.042 cm3cm−3 with an improvement of 10.6%. This work demonstrates the necessity and improvement for incorporating SST into SM retrieval for GNSS-R. Moreover, the findings provide a potential method to obtain global SST dataset from CYGNSS observations.

Climate data records of soil moisture (SM) are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. However, many of these studies rely on models for which the errors are not yet fully understood over a region. This may have a considerable impact on the economic growth of the country if the model's future predictions are used for studying long-term trends. Here we examined the spatial distribution of past, present, and future predictions of SM studied using the Coupled Model Intercomparison Project Phase5 (CMIP5) simulations for the historical period (1850–2005) and future climate projections (2006–2099) based on Representative Concentration Pathways (RCP-RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Furthermore, the performance of modeled SM with the satellite AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) was studied. The modeled SM variations of 38 Global Climate Models (GCMs) show discreteness but still we observed that CESM1-CM5, CSIRO-MK3-6-0, BCC-CSM1-1, and also BCC-CSM1-1-M, NorESM1-M models performed better spatially as well as temporally in all future scenarios. However, from the spatial perspective, a large deviation was observed in the interior peninsula during the monsoon season from model to model. In addition, the spatial distribution of trends was highly diversified from model to model, while the Taylor diagram presents a clear view of the model's performance with observations over the region. Skill score statistics also give the accuracy of model predictions in comparison with observations. The time series was estimated for the future trend of the SM along with the past few decades, whereas the preindustrial and industrial period changes were involved. Significant positive anomaly trends are noticed in the whole time series of SM during the future projection period of 2021–2099 using CMIP5 SM model datasets. [ABSTRACT FROM AUTHOR]

Remotely sensed soil moisture (SM) with high accuracy and high spatial–temporal resolution is crucial to meteorological, agricultural, hydrological, and environmental applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that uses the L-band signal transmitted by the GNSS satellites to develop daily SM data products. In this study, a physics-based algorithm is proposed to couple CYGNSS surface reflectivity (SR) and Soil Moisture Active Passive (SMAP) brightness temperature estimates for accurate SM retrieval. The algorithm is based on the radiative transfer model and the SMAP data to derive a combined parameter of the vegetation optical depth ($\tau$) and the surface roughness parameter ($h$). The CYGNSS L1 Version 2.1 data of the years 2017–2018 and 2019–2020 are used for calibration and validation, respectively. The SM estimates agree and correlate well with the SMAP SM and in situ SM data on a global scale ($R =0.679$ , RMSE $=0.051\,\,\text{m}^{3}\text{m}^{-3}$ , and MAE $=0.045\,\,\text{m}^{3}\text{m}^{-\mathrm {3}}$ against SMAP SM; $R = 0.729$ against in situ SM). The proposed algorithm makes contributions from two aspects. First, the proposed algorithm provides a physics-based algorithm using SMAP brightness temperature to calibrate the attenuation due to vegetation and surface roughness on the CYGNSS-derived SR. Unlike attenuation models that have been explored previously in the context of CYGNSS, this algorithm executes the calibration without relying on observations of $h$ or vegetation biophysical parameters as inputs but with the SMAP brightness temperature as the only observations. Second, the proposed algorithm provides a new way for the combined usage of CYGNSS and SMAP to improve the temporal and spatial coverages of global SM with temporal coverage increased by 38.2% and spatial coverage increased by 31.6%. [ABSTRACT FROM AUTHOR]

Fast soil moisture content (SMC) mapping is necessary to support water resource management and to understand crop growth, quality, and yield. Therefore, earth observation (EO) plays a key role due to its ability of almost real-time monitoring of large areas at a low cost. This study aimed to explore the possibility of taking advantage of freely available Sentinel-1 (S1) and Sentinel-2 (S2) EO data for the simultaneous prediction of SMC with cycle-consistent adversarial network (CycleGAN) for time-series gap filling. The proposed methodology, first, learns latent low-dimensional representation of the satellite images, then learns a simple machine learning (ML) model on top of these representations. To evaluate the methodology, a series of vineyards, located in South Australia ’s Eden valley are chosen. Specifically, we presented an efficient framework for extracting latent features from S1 and S2 imagery. We showed how one could use S1 to S2 feature translation based on CycleGAN using S1 and S2 time series when there are missing images acquired over an area of interest. The resulting data in our study is then used to fill gaps in time-series data. We used the resulting latent representations to predict SMC with various ML tools. In the experiments, CycleGAN and the autoencoders were trained with data randomly chosen around the site of interest, so we could augment the existing dataset. The best performance was demonstrated with random forest (RF) algorithm, whereas linear regression model demonstrated significant overfitting. The experiments demonstrate that the proposed methodology outperforms the compared state-of-the-art methods if there are missing optical and synthetic-aperture radar (SAR) images. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for downscaling are generally based on pixel-to-pixel strategy, which ignores the information among pixels. Hence, a new downscaling method based on a convolutional neural network (CNN) is proposed to solve the problem. Furthermore, a weight layer is designed for the input, and residual SM is treated as the output of the CNN to improve the accuracy. This method is applied to downscale Soil Moisture Active Passive (SMAP) SM products (i.e., 36-km $\mathbf {L3{\_}SM{\_}P}$ and 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$) from January 1, 2018 to December 30, 2018. Compared with 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$, the 9-km downscaling result is satisfactory with obtained correlation coefficient ($R$), root mean square error (RMSE), and unbiased RMSE (ubRMSE) values of 95.81%, 2.77%, and 2.67%, respectively. Moreover, SMAP SM products (36 and 9 km) and downscaling SM (3 and 1 km) are validated by the in situ data, which are collected by the 109 stations of the Oklahoma Mesonet SM monitoring network. Mean $R$, RMSE, and ubRMSE values are 67.92%, 7.94%, and 4.87% for 36-km $\mathbf {L3{\_}SM{\_}P}$; 67.78%, 8.35%, and 4.95% for 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$; 67.28%, 8.34%, and 4.97% for 3-km downscaling SM; 65.90%, 8.40%, and 5.18% for 1-km downscaling SM, respectively. The 3-km downscaling SM generated by this method can improve the coarse resolution of 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ while preserving its accuracy. However, error will remarkably increase in the 1-km downscaling SM. Therefore, the proposed method provides a new strategy for SM downscaling and obtains satisfactory results in practice. Additional studies can be conducted in the future.

Spatio-temporally continuous and high-quality soil moisture (SM) is very important for assessing changes in the water cycle and climate, especially over the Tibetan plateau (TP). Data fusion is an important method to improve the quality of SM product. However, limited observation overlaps between different satellite SM products, caused by inherent gaps, make it difficult to fuse them to create a continuous and high-quality product. In this study, an SM spatio-temporal continuity and quality simultaneously improving algorithm is proposed. The first step of the approach is obtaining spatio-temporally continuous reference data, including land surface temperature (LST), normalized difference vegetation index (NDVI), Albedo, and digital elevation model (DEM). The second step is training the general regression neural network (GRNN) model with all available essential climate variables (ECV) and Fengyun (FY) SM. The last step is predicting the spatio-temporally continuous and high-quality SM using the trained GRNN derived by the spatio-temporal continuity reference data. An implementation of the algorithm on the TP showed that, compared with the original ECV and FY SM, both the continuity and quality of the fused SM product were largely improved in terms of coverage (72.5%), correlation (R = 0.809), root mean square error (0.081 cm3 cm-3) and bias (0.050 cm3 cm-3). The algorithm showed a good performance in obtaining spatio-temporal variation fusion weights over the TP. This spatio-temporally continuous and high-quality SM of the TP will help advance our understanding of global and regional changes in water cycle and climate.