Surface Soil Moisture Estimates Research Articles

Spatial-temporal constraints for surface soil moisture mapping using Sentinel-1 and Sentinel-2 data over agricultural regions

Surface soil moisture (SSM) information could have important applications in agricultural and regional water management. Remote sensing, particularly synthetic aperture radar (SAR), is an important technology for the estimation of spatial–temporal SSM over larger areas. Using Sentinel-1 and Sentinel-2 data, this research developed a general spatial–temporal constrained machine-learning-based method for surface soil moisture mapping over agricultural regions. Central to this method is the construction of spatial and temporal constraints and their implementation in machine-learning models. We first defined the spatial and temporal constraints for SSM estimation by investigating the spatial division of cultivated crop types and the temporal division of cumulative precipitation. Second, under the presumption that the SSM and associated variables are smoothly changing, we extracted the temporal difference variables from the multi-temporal remote sensing data. Finally, we incorporated two constraints as categorical features and temporal differences into a CatBoost-based model to improve surface soil moisture mapping. We verified the proposed model in a Spain study area with multi-temporal remote sensing observations. The experimental results after incorporating the spatial–temporal constraints demonstrate the efficacy of the proposed model for mapping surface soil moisture over agricultural regions, with significantly improved R2 = 0.7328, RMSE = 0.0451 vol, and MAE = 0.0351 vol This study also concluded that using multiple polarization in the machine-learning-based method could reliably and accurately estimate surface soil moisture.

Evaluating pulse-reserve characteristics of Soil-Plant continuum in India using remote sensing

The pulse-reserve paradigm associated with the plant's increased water uptake following surface soil moisture pulses is not well understood in India. India has a very high spatio-temporal climate and ecological variability. Such ecological and climatological diversity makes the country-scale analysis of the pulse-reserve paradigm challenging. Here, we present the first analysis of this phenomenon using 9 years of space based passive microwave estimates of surface soil moisture and vegetation water content from both X-band and L-band. We observed from both datasets that the pulse-reserve paradigm is widespread in India across seasons and biomes. The seasonal climate in India drives the precipitation characteristics, thus uphold the pulse-reserve paradigm. The estimated Soil Moisture Threshold (SMT), at which the vegetation water content drops, has substantial spatio-temporal variations, unlike other regions worldwide. The comparisons between X-band and L-band products show significant differences in the magnitudes of soil moisture and vegetation optical depths due to inherent properties. However, most of the findings related to the pulse-reserve paradigm, specifically the sensitivity of processes on different factors, remain similar. We found a strong dependence of SMT on seasonal climate, vegetation greenness, and pulse soil moisture. The association of SMT with soil characteristics, such as clay fraction, is weak in India, contrary to global traits. Our analysis provides new insights into the plant water uptake mechanisms in India to improve the parameterization schemes for better representation of the soil–plant continuum structural and functional behavior.

An improved change detection method for high-resolution soil moisture mapping in permafrost regions

ABSTRACT Soil moisture plays a crucial role in understanding the hydrological cycle and the ecological environment. This research presents an improved change detection method that leverages time series data from Sentinel-1 radar and Sentinel-2 optical sensors (2019–2021) to estimate surface soil moisture. The response of backscatter to soil moisture in bare soil was expressed in a logarithmic form, and the influence function of the normalized difference vegetation index (NDVI) on the backscatter difference was established for various vegetation-covered surfaces. Therefore, the impact of vegetation on backscatter is effectively mitigated, and the resulting change in backscatter relative to bare soil conditions can be obtained. An empirical function is subsequently formulated to ascertain the reference values of soil moisture in each pixel. The retrieval of soil moisture is demonstrated in the Wudaoliang permafrost region of the Qinghai-Tibet Plateau and validated against ground measurements. The retrieval results of the improved change detection method exhibit correlation coefficients ranging from 0.672 to 0.941, with root mean squared errors (RMSE) ranging from 0.031 m 3 / m 3 to 0.073 m 3 / m 3 . Compared to the Soil Moisture Active Passive (SMAP) 9-km product, our new method demonstrates higher correlation (0.898 vs. 0.867) and lower RMSE (0.037 m 3 / m 3 vs. 0.044 m 3 / m 3 ). The soil moisture retrieved from Sentinel shows a strong correlation with the SMAP 9-km soil moisture in the time series, thereby providing a better representation of the region’s soil moisture heterogeneity. Our method demonstrates the feasibility of combining Sentinel-1 and 2 for high-resolution (100 m) soil moisture mapping in permafrost regions.

Enhancing Global Surface Soil Moisture Estimation from ESA CCI and SMAP Product with Conditional Variational Auto-Encoder

Enhancing Global Surface Soil Moisture Estimation from ESA CCI and SMAP Product with Conditional Variational Auto-Encoder

Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments

Abstract Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments. Significance Statement The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations.

Surface soil moisture estimation in bare agricultural soil using modified Dubois model for Sentinel-1 C-band SAR data

Surface soil moisture has vital role in water energy balance, climate change and agriculture mainly for crop water requirements and irrigation scheduling. Microwave remote sensing with its unique characteristics of high penetration and sensitivity towards dielectric constant, has enabled the researchers to explore various techniques for soil moisture estimation. With the launch of Sentinel-1 (A&B) Synthetic Aperture Radar (SAR) satellites, the hindrance in accessing high spatial and temporal resolution data is eliminated. The current study focuses on surface soil moisture estimation for bare agricultural fields in the semi-arid region. Field soil moisture up to 5 cm depth using HydraGo Probe sensor and surface roughness synchronizing with satellite pass dates were collected from total 102 locations spanning four dates. Volumetric and sensor-based soil moisture are well correlated with R2 = 0.85. The Modified Dubois Model (MDM) was applied to obtain the relative permittivity of the soil for the backscattering coefficient (σ◦) for VV polarization, which is used as one of the inputs in universal Topp’s model for soil moisture calculation. Model derived soil moisture is well correlated with ground-based soil moisture for the entire range of the soil moisture (0.02-0.18 m3m-3) with R2 = 0.85 and RMSE=0.005. The entire soil moisture was categorized in three soil moisture ranges to evaluate the sensitivity. The highest correlation was observed for 0.06-0.1 m3m-3 with R2 = 0.73 and RMSE=0.003 followed by 0.015-0.6 m3m-3 with R2 = 0.81 and RMSE=0.001 and 0.11-0.18 m3m-3 with R2 = 0.48 and RMSE=0.019 which is significantly low. Performance accuracy of MDM is encouraging for bare soil moisture estimation for even the lower range of surface soil moisture.

Improved downscaling of microwave-based surface soil moisture over a typical subtropical monsoon region

Microwave remote sensing has great potential for capturing surface soil moisture (SSM) but suffers from its coarse spatial resolution. In this study, we combined the disaggregation based on physical and theoretical change (DISPATCH) and random forest (RF) approaches to downscale microwave-based SSM in the Yangtze River Delta region, China. Results showed that downscaled SSM by DISPATCH approach was superior to that by RF approach with Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), and the mean bias (Bias) of 0.778, 0.022 m3 m-3 and 0.061 m3 m-3, respectively. However, the DISPATCH approach was inapplicable in pixels with weak link between SSM and evapotranspiration, while the RF approach did not have such restriction. Therefore, the DISPATCH approach was supplemented with the RF approach to downscale the SSM for the entire study area. In addition, this downscaling process was conducted for the four different seasons and for the entire study period to test whether considering seasonality was necessary in the downscaling. The downscaled SSM was the best in autumn with R = 0.843, ubRMSE = 0.022 m3 m-3, and Bias = 0.048 m3 m-3, followed by summer and spring, and poorest in winter. However, the seasonally downscaled SSM performed better than that downscaled for the entire period. Moreover, over the entire study period, land surface temperature, land cover heterogeneity, soil texture, and water area percentage exerted significant influences on errors. However, contribution order of these environmental factors on errors varied with seasons. This study would be helpful to widen DISPATCH applicability in the semi-humid/humid region and maximize the potential of various data in generating high accurate and spatiotemporally continuous SSM estimates.

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Abstract. Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.

Global estimates of daily evapotranspiration using SMAP surface and root-zone soil moisture

The interplay between soil moisture and evapotranspiration modulates the water available to sustain soil evaporation and influences canopy stomatal conductance controls on vegetation transpiration. Modeling this behavior remains challenging. Indeed, satellite remote sensing based Penman-Monteith (PM) ET models tend not to directly consider soil moisture constraints on evaporation and transpiration due to a lack of consistent soil moisture data. To address this issue, we modified a PM model to include satellite enhanced surface and root zone soil moisture from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission. The resulting model was used to produce global 9-km daily ET estimates, including contributing fluxes from soil evaporation, transpiration and evaporation of canopy-intercepted precipitation. The global PM ET estimates were assessed using in situ sap flow and AmeriFlux measurements, upscaled FLUXCOM latent heat flux, as well as against other independent global ET data products (GLEAM, GLDAS, SSEBop, and LandFlux-EVAL). The modelled transpiration showed similar seasonal variation and positive correlation to in situ sap flow measurements available from several forest sites (R2 = 0.85; p < 0.01). When compared against AmeriFlux data, the PM ET estimates showed favorable agreement with annual ET measurements extracted from 34 diverse sites (R2 = 0.58; p < 0.01; RMSE = 227 mm yr−1). The PM 8-day ET results also reflected a similar hemispheric seasonality as the global FLUXCOM record (R2 = 0.94–0.98; p < 0.01), while comparisons against other global ET products showed moderate mean differences of between 49 and 107 mm yr−1 (11–25%) over the global domain. Our PM ET estimates varied up to 52% in response to SMAP surface soil moisture dynamics, displaying stronger surface (0–5 cm depth) than root zone (0–100 cm) soil moisture sensitivity. While PM ET sensitivity to soil moisture was greater in arid climate regions, it was also significant in humid climate zones, with analysis indicating that the inclusion of soil moisture predominantly acts as a sustaining influence on ET, especially in moisture limited drylands. PM ET sensitivity to temperature was stronger in humid forest regions relative to other climate and land cover regimes. Overall, the model results clarify the influence of soil moisture heterogeneity on the global ET pattern as informed by satellite-based estimates of surface and root zone soil moisture. Potential enhancements to the spatial and vertical resolution of soil moisture inputs are expected to enable further ET improvements through more realistic model representation of soil and plant available water.

Estimation of Surface and Subsurface Soil Moisture Using Microwave Remote Sensing: A Typical Analysis

Accurate measurement and monitoring of surface and subsurface soil moisture is essential for understanding hydrological processes, crop growth modeling, crop water requirement, and climate studies. Accurate measurement of the soil moisture content (SMC) in the root zone is essential for precise irrigation authority and plant water stress evaluation. However, the existing passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), that operate at L-band, can only estimate the top 5 cm of soil moisture. Microwave remote sensing has proven to be a valuable tool for non-invasive soil moisture estimation. This research aims to investigate and develop a methodology for estimating surface and subsurface soil moisture using microwave data from Sentinel-1. The study was conducted to establish the relationship between surface &amp; the backscatter coefficient derived using the Sentinel-1 SAR microwave remote sensing satellite imagery, and relationship between surface and subsurface soil moisture at different depths, in the Godhra region. Two seasons namely summer (Zaid) and monsoon (Kharif) were taken into consideration to build up the relationship between surface soil moisture and co-polarization backscatter coefficient ( For the summer (Zaid) and monsoon (Kharif) seasons, the co-polarization backscatter coefficient ( and surface soil moisture (0-5, cm) were found to have a correlation in terms of R2 as 0.91 and 0.90, respectively. The study explores the relationship between microwave signals and surface soil moisture content (0-5, cm) and then the relationship between surface soil moisture and soil moisture at various depths were also modeled thereby contributing to improved soil moisture estimation techniques and applications. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, and 41-60 cm depths were found to be 0.60, 0.51, and 0.46, respectively, in the summer (Zaid) season. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, 41-60 cm, 61-80 cm, and 81-100 cm depths were found to be 0.83, 0.61, 0.51, 0.26, and 0.13, respectively. According to the study, it is observed that the relationship between co-polarization backscatter coefficient ( and soil moisture weakens as the depth of soil moisture increases. Overall, the regression models developed between the co-polarization backscatter coefficient ( and surface soil moisture showed very good results, whereas the regression models developed between the surface soil moisture and soil moisture at various depths showed reasonably acceptable results up to the depth of 60 cm. The findings in the present study suggest that Sentinel-1A C-band SAR data can be used to estimate surface soil moisture. It is also shown in this study that the surface soil moisture can be correlated with the subsurface soil moisture up to the depth of 60 cm, satisfactorily using regression equations.

Surface soil moisture estimate from Sentinel-1 and Sentinel-2 data in agricultural fields in areas of high vulnerability to climate variations: the Marche region (Italy) case study

AbstractSurface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model’s accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4).

Satellite-based land surface temperature and soil moisture observations accurately predict soil respiration in temperate deciduous and coniferous forests

Soil respiration, defined as the total flux of carbon dioxide (CO2) from the soil to the atmosphere, is a key ecosystem process that affects the regional and global carbon (C) cycles and is highly sensitive to temperature and soil moisture. It is challenging to quantify soil respiration at the ecosystem level from commonly used in-situ soil chamber measurements because of large spatial variability. Methods that provide temporally and spatially continuous estimates of soil respiration at various scales are vital to understand the impact of climate change on soil C stock. In this study, we evaluate three commonly used empirical models and a Random Forest machine learning algorithm applied to satellite derived estimates of land surface temperature (LST) and soil moisture to estimate soil respiration in temperate deciduous and coniferous forests in Canada. The models were calibrated using in-situ soil temperature and moisture and validated against in-situ measurements of soil CO2 fluxes (gCm−2day−1) from automatic soil chambers. We separately evaluate the performance of nighttime and daytime satellite-based LST and soil moisture observations in modeling soil respiration. The soil respiration models were also evaluated at daily and monthly time scales against in-situ measurements. Results indicate that models based on satellite LST, and soil moisture can explain more than 70% of the variability in observed soil respiration. Nighttime LST at a monthly time scale resulted in consistently higher accuracy than daytime LST in estimating soil respiration. Satellite observations resulted in comparable accuracy in estimating soil respiration as in-situ measurements. Satellite LST and soil moisture observations are indispensable data sources to estimate soil respiration at ecosystem level and its upscaling to regional and global scales.

Soil moisture estimation from Sentinel-1 interferometric observations over arid regions

We present an experimental methodology based on interferometric synthetic aperture radar (InSAR) time series analysis that can provide surface (top 5 cm) soil moisture (SSM) estimations over sandy arid soils. A co-registered Single Look Complex (SLC) SAR stack as well as meteorological information are required as input of the proposed methodological workflow. The proposed methodology consists of five steps. In the first step, meteorological data are exploited to identify the snow/ice free SAR acquisition related to lowest SSM level. In the second step, interferometric (coherence and phase closure) observables over a spatial grid are calculated. In the third step, for each grid cell the ordering of SAR acquisitions according to the SSM is performed. The sorting is performed by starting from the SAR acquisition related to lowest SSM level and by exploiting coherence and backscattering information. In the fourth step, for each grid cell the coherence values due to SSM variations are calculated by exploiting the ordering of SAR acquisitions according to the SSM. In the fifth step, SSM is estimated by inverting an analytical interferometric model using coherence values due to SSM variations and phase closure information. The proposed method estimates SSM over arid regions by ordering SAR acquisitions based on SSM, by calculating coherence values due to SSM variations and by inverting an interferometric model.A case study over an arid region in California/Arizona is presented. The proposed workflow was applied in Sentinel-1 (C-band) VV-polarized InSAR observations. The estimated SSM results were assessed with independent SSM observations from a station of the International Soil Moisture Network (ISMN) (RMSE: 0.029 m3/m3 R: 0.78) and with ERA5-Land reanalysis model data (RMSE: 0.049 m3/m3 R: 0.62). In summary, the proposed methodology was able to provide accurate SSM estimations at high spatial resolution (∼250 m). A discussion of the benefits and the limitations of the proposed methodology highlighted the potential of interferometric observables for SSM estimation over arid regions.

Appraising the crop health response to water stress from enhanced crop and soil water estimates using SAR data and machine learning approaches

ABSTRACT Precise information on soil moisture (SM) and crop water dynamics is essential for hydrological and agricultural applications. The SM measurements from SAR data are influenced by varying degree of soil-water binding and thus affect the reliable assessment of vegetation dynamics. Hence, in this present study, the six SAR-based SM descriptors capable of representing bound and free water in the soil and the corresponding in-situ SM measurements were used to train the four state-of-the-art machine learning regression (MLR) approaches, namely, random forest regression (RFR), gradient boosting regression Tree (GBRT), support vector regression (SVR), and gaussian processes regression (GPR) to retrieve enhanced surface soil moisture (SSM enh ) estimates. The SSM enh results were used in the Water Cloud Model (WCM) to retrieve the PWC (plant water content). Lastly, the crop health schema (CHS) was proposed considering SSM enh and PWC, to examine the health dynamics of the cotton and sorghum crops. The results demonstrated that the GBRT model (R2 = 0.91, RMSE = 0.004 m3m − 3, MAE = 0.021) outperformed the other ML models in retrieving SM. The PWC (R2 = 0.82, RMSE = 0.014, MAE = 0.019) from WCM and CHS schema (F1 score = 0.86, Kappa = 0.79) have shown good statistical agreement with the field observations. The present study demonstrated the scope of SSM enh product in investigating vegetation health response to water stress from single-date dual-pol SAR imagery.

Estimation of Surface Soil Moisture from Agricultural Lands Using Multi-Spectral Optical Satellite Data: A Study of Bhagwanpur-I CD Block, East Medinipur, West Bengal, India

The spatiotemporal fluctuation of Surface Soil Moisture (SSM) is important for prediction of weather, modeling of hydrological cycle, water management, agricultural managing and making strategy. Optical remote sensing has demonstrated significant promise for precise surface soil moisture estimate. The study aimed to estimate the moisture content in the upper layer of agricultural fields using Landsat 8 OLI, Sentinel-2A multi-spectral satellite data as well as TVDI [Temperature Vegetation Dryness Index] data. The spatial resolution of the Landsat OLI and Sentinel-2A images was 30m and 10m, respectively. The spectral Thermal Infrared (TIR 10.9µm ), TIR (10.9µm) and along with the Short-Wave-Infrared (SWIR 2.2µm) band of the Landsat 8 and the Shore-Wave-Infrared (SWIR 2.2 µm) band of sentinel 2B satellite imagery was utilized to estimate how moist was topsoil of the agricultural lands. The soil moisture estimates using remote sensing based model were acquired and compared with in-situ soil moisture. In a depth of 10cm below the surface, the field-based soil moisture was measured. Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and TVDI were measured to estimate moisture content. The reflectance values of the TVDI show over the study area generally low, with the values ranging from -0.07 to 1.37. The statistical tests of TVDI values and gravimetric soil moisture values presented a positive correlation with RMSE= 0.17. The results of the study can give insight of better hydrological modeling, management of agriculture and policy making. However, further research is required to validate the methodology over a larger geographical area and to evaluate the preciseness of the assessed SSM at various depths of the soil.

Ensemble surface soil moisture estimates at farm-scale combining satellite-based optical-thermal-microwave remote sensing observations

Remote sensing presents great potential for soil moisture mapping, although a lack of high-resolution, all-season suitable data and model-based complications hinders continuous mapping at the farm scale. The current study attempts to access optical (Sentinel-2) and microwave (Sentinel-1) based Water Cloud Model (WCM), optical and/or thermal-based Thermal-Optical TRApezoid Model (TOTRAM), and Optical TRApezoid Model (OPTRAM) utilizing Landsat-8 data, and an ensemble of these three models synergistically using microwave, optical and thermal remote sensing data for fourteen dates spanning two post-monsoon periods over a semi-arid irrigated agricultural farm. The ensemble model developed with Gradient Boosting Machine (GBM) offered a superior SSM estimate for the entire crop growth season. The ensemble model had the lowest RMSE (0.053 m3 m−3), followed by OPTRAM (SWIR2) (0.06 m3 m−3), while TOTRAM recorded the highest RMSE (0.092 m3 m−3), even though the model performances varied throughout crop growth cycles. For SSM mapping in the early phases of crop growth, WCM or TOTRAM were found superior, while during the latter growth stages, the performance of OPTRAM was better. In contrast to TOTRAM, which needs local calibration as the land surface temperature is sensitive to atmospheric conditions, the study emphasizes the shortcomings of WCM in terms of calibration requirements for changing vegetation structure utilizing in situ data. OPTRAM is suitable for generating SSM maps in semi-arid environments with a predominance of clear skies throughout the post-monsoon season. It is straightforward, low data, and resource intensive with universal surface reflectance-soil moisture association. The ensemble technique can be recommended for farm-level irrigation mapping over a post-monsoon period.

Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow

Because runoff production is more efficient over wetter soils, and because soil moisture has an intrinsic memory, soil moisture information can potentially contribute to the accuracy of streamflow predictions at seasonal leads. In this work, we use surface (0–5 cm) soil moisture retrievals obtained with the National Aeronautics and Space Administration’s Soil Moisture Active Passive satellite instrument in conjunction with streamflow measurements taken within 236 intermediate-scale (2000–10,000 km2) unregulated river basins in the conterminous United States to show that late-fall satellite-based surface soil moisture estimates are indeed strongly connected to subsequent springtime streamflow. We thus show that the satellite-based soil moisture retrievals, all by themselves, have the potential to produce skillful seasonal streamflow predictions several months in advance. In poorly instrumented regions, they could perform better than reanalysis soil moisture products in this regard.

Field-scale evaluation of remote sensing soil moisture retrievals using a multi-satellite approach

ABSTRACT Nine remote sensing-based surface soil moisture (SSM) estimation models using images from Landsat 8, Sentinel-2 and Sentinel-1 satellites were compared. To evaluate these models, we measured SSM at 179 locations in a 50-ha sunflower field . The result showed that the Water Cloud-based model, a semi-empirical regression model, which used the synergy of Landsat 8 and Sentinel-1 data, was the best model, with an R 2 of 0.73 and RMSE of 0.053 m3/m3. In sum, with the integration of images from multiple satellites, soil moisture maps with suitable spatial resolutions were retrieved that may be used for irrigation planning.

Estimation of surface soil moisture by combining a structural equation model and an artificial neural network (SEM-ANN)

Soil moisture is an important variable of the environment that directly affects hydrological, ecological, and climatic processes. However, owing to the influence of soil type, soil structure, topography, vegetation, and human activities, the distribution of soil water content is spatially heterogeneous. It is difficult to accurately monitor the distribution of soil moisture over large areas. To investigate the direct or indirect influence of various factors on soil moisture and obtain accurate soil moisture inversion results, we used structural equation models (SEMs) to determine the structural relationships between these factors and the degree of their influence on soil moisture. These models were subsequently transformed into the topology of artificial neural networks (ANN). Finally, a structural equation model coupled with an artificial neural network was constructed (SEM-ANN) for soil moisture inversion. The results showed the following: (1) The most important predictor of the spatial variability of soil moisture in the April was the temperature–vegetation dryness index, while land surface temperature was the most important predictor in the August; (2) After the ANN model was improved, the inversion accuracy of surface soil moisture by SEM-ANN model was improved, and the R2 of verification set was increased by 0.01 and 0.02 in April and August, respectively, and the relative analysis error was reduced by 0.5 % and 1.13 %. (3) There were no significant differences in soil moisture distribution trends between the April and August.

Enhancing GNSS-R Soil Moisture Accuracy with Vegetation and Roughness Correction

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm3cm−3, resulting in a mean improvement of 4.4%. Validation of the retrieval algorithm through in situ measurements confirms its stable.