Surface Soil Moisture Retrieval Research Articles

Comparative analysis of SAOCOM and Sentinel-1 data for surface soil moisture retrieval using a change detection method in a semiarid region (Douro River’s basin, Spain)

The growing interest in low-frequency SAR for soil parameter retrieval has led to the development of new active L-band satellites, that will provide novel surface soil moisture products and retrieval possibilities; however, due to data unavailability so far, limited applications have investigated the use of change detection models using L-band satellite SAR data. Since July 2020, high revisit time, high-resolution acquisitions by the Satélite Argentino de Observación COn Microondas (SAOCOM) Argentinian-Italian constellation have become accessible over Europe. Therefore, this research presents an investigation of the potential of multi-temporal L-band SAOCOM-1 for monitoring soil moisture variations underneath low and sparse agricultural vegetation. Moreover, it proposes a procedure for the mitigation of roughness contribution, by exploiting the entropy parameter derived from the dual-polarimetric decomposition. L-band sensitivity to soil moisture has been jointly evaluated in respect of Sentinel-1 C-band data by (1) comparing the temporal profiles of the backscattering coefficient, γ0, at VV and VH polarization, with the support of decomposition parameters (entropy and ᾱ), NDVI and precipitation data; (2) regression analysis with in situ soil moisture measurements, obtained by the REMEDHUS network in the Douro River basin (Spain); 3) evaluating the soil moisture retrievals obtained at C- and L- band using a change detection method. Finally, the effectiveness of the roughness normalization procedure for SAOCOM data has been validated using in situ data. L-band co-polarized γ0 has proved to be the best configuration for soil moisture inversion, being relatively insensitive to vegetation, as demonstrated by decomposition results and trend interpretation. Overall, regressions detected an R2 22% higher at L-band than C-band, with values up to 0.74 for VV (R̄2=0.32) and up to 0.47 for the VH band (R̄2=0.14). Co-polarized data obtained R2 on average 62.1% and 74.7% higher for SAOCOM and Sentinel-1. The retrieval models show an ubRMSD of 7.1% for SAOCOM data and 8.3% for Sentinel-1. The application of the proposed roughness normalization procedure to SAOCOM led to an ubRMSD of 6.7% improving the retrieved soil moisture trend by 7.9%. This exploratory analysis demonstrated SAOCOM data potential for soil moisture mapping and would serve as a foundation for more advanced retrieval procedures.

Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model

This study explores the potential of integrating satellite retrievals of surface soil moisture (SSM) and vegetation conditions into the Noah-MP land surface model. In total, five data assimilation (DA) experiments were carried out. One of the experiments only assimilates SSM retrievals from the Soil Moisture Active Passive mission, two experiments only assimilate retrievals of vegetation conditions: either optical retrievals of leaf area index (LAI) from the Copernicus Global Land Service, or X-band microwave-based retrievals of vegetation optical depth (VOD) from the Advanced Microwave Scanning Radiometer 2. Additionally, two joint DA experiments are performed, each incorporating SSM and one of the vegetation products. The DA experiments are compared with a model-only run, and all experiments are evaluated using independent ground reference data of soil moisture, evapotranspiration, net ecosystem exchange and gross primary production (GPP). Assimilating only SSM improves estimates of the soil moisture profile (median SSM anomaly correlation improves with 0.02 compared to a model-only run), whereas assimilating LAI predominantly improves GPP estimates (reduction in median RMSD of 0.024 gC m−2 day−1 compared to a model-only run). The joint assimilation of SSM and vegetation conditions captures both of these improvements in a single, physically consistent analysis product. The DA increments show that this combined setup allows one satellite product to compensate for potential degradations introduced into the system by the other product. Furthermore, the joint SSM and VOD DA experiment has the smallest ensemble spread in its estimates (21% reduction in SSM spread compared to a model-only run). Overall, our results underline the potential of multi-sensor and multivariate DA, in which information from different sources is combined to improve the estimates of several land surface states and fluxes simultaneously.

Surface soil moisture retrieval based on transfer learning using SAR data on a local scale

ABSTRACT Retrieving surface soil moisture on a local scale using Synthetic Aperture Radar (SAR) data and Deep Learning (DL) models necessitates a substantial volume of data, which may not be available in all scenarios. In this study, the application of transfer learning was introduced as a novel approach to address the scarcity of training samples for DL models in the context of soil moisture retrieval. The proposed DL model was initially trained using International Soil Moisture Network (ISMN) data, followed by a fine-tuned process on a local scale using field trip data from an agricultural area in Karaj, Iran. The proposed DL model was compared against Random Forest Regressor (RFR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 indicators. All models underwent hyperparameter-tunning, and their performance was evaluated using 8-fold cross-validation (CV) and various combinations of inputs. The proposed DL model outperformed other models on a local scale achieving an RMSE, MAE, and R2 of 2.42 vol%, 1.66 vol%, and 0.90, respectively. The MLP model also exhibited good performance with an RMSE, MAE, and R2 of 2.84 vol%, 2.04 vol%, and 0.88 compared to the RFR with 2.83 vol%, 2.20 vol%, and 0.86, respectively. Additionally, the SVR yielded an RMSE, MAE, and R2 of 3.71 vol%, 3.05 vol%, and 0.78. However, the RMSE, MAE, and R2 of the MLP and the proposed DL model without using transfer learning deteriorated by around 18%, 32%, and 34%, respectively.

Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture

Despite the key role of irrigation in the Earth system, we lack fundamental information regarding the distribution of irrigated fields, irrigation timing and the amount of water utilized. In the past years, the SM_Delta and SM_Inversion approaches have been independently developed to provide estimates of irrigation timing and water amounts based on satellite soil moisture data. The SM_Delta approach retrieves irrigation from variations in soil moisture between an individual pixel and the surrounding rainfed area, while the SM_Inversion approach estimates the total amount of water entering the soil, then irrigation is derived by subtracting precipitation. In this study, we perform a comprehensive assessment of irrigation estimates from the SM_Delta and SM_Inversion algorithms based on Sentinel-1 surface soil moisture retrievals at 1 km resolution. Our analysis focuses on the Ebro basin, an irrigated region in Spain covering 83000 km2, during the period 2017–2019. We assess the ability of the two methods to discriminate irrigated and rainfed pixels, then we quantify the agreement of irrigation timing and water volumes with reference irrigation data. An inter-comparison between estimates from the SM_Delta and SM_Inversion methods is carried out considering both temporal and spatial features, i.e., monthly irrigation peaks and spatial irrigation patterns. Finally, we explore two potential applications of satellite-derived irrigation estimates: attributing irrigation water volumes to specific irrigation systems and to individual crops. We observe that both methods erroneously retrieve irrigation over rainfed pixels, and are therefore not suitable to map irrigated and rainfed fields. However, when auxiliary information on irrigated fields is available, we find a satisfactory agreement between district-scale reference data and satellite-retrieved irrigation, using both the SM_Delta and SM_Inversion approaches (Pearson R equal to 0.67 and 0.71, bias equal to −4.99 and −4.75 mm/15 days, respectively). When aggregated in space or time, the irrigation estimates exhibit coherent temporal dynamics and spatial patterns. For instance, estimates from both SM_Delta and SM_Inversion capture the delayed irrigation that occurred in 2018 due to wetter than usual conditions in spring. However, at the pixel-scale, limited consistency exists between irrigation estimates from the two methods due to different assumptions and parameterizations, e.g., use of constant vs pixel-specific soil water capacity (in the SM_Delta and SM_Inversion, respectively). Overall, the study demonstrates the reliability of irrigation estimates derived from the SM_Delta and SM_Inversion approaches, especially when shifting from small spatial and short temporal scales (pixel level, sub-weekly) to larger and longer scales (district level, seasonal). Hence, satellite-based irrigation estimates could inform water resources managers and basin authorities, as well as serve the modelling community by providing reliable information on the timing and the amounts of water employed at the basin level.

High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau

ABSTRACT As a key climate variable, soil moisture plays a crucial role in drought detection, flood warning, and crop yield prediction. In recent years, the demand for high-spatial-resolution soil moisture has increased, particularly in environmental management. In this study, Copernicus Sentinel-1 synthetic aperture radar data, Sentinel-2 multi-spectral data, and other auxiliary data (land cover types, soil texture, etc.) were used to retrieve surface soil moisture (10 m) in the cloud environment (Google Earth Engine + Google Colab + Google Drive) over the Tibetan Plateau, and an entirely data-driven machine learning-based model called Deep Forest was adopted. We discussed the application of the Deep Forest model and compared it with other machine learning models. Overall, on the basis of 10-fold cross-validation, the modified Deep Forest model performed the best, with estimate accuracy of 0.834 and 0.038 m3·m−3 in terms of coefficient of determination ( R 2 ) and unbiased Root Mean Square Error (ubRMSE), respectively. It also demonstrated the best performance in site-based validation ( R 2 of 0.606 and ubRMSE of 0.092 m3·m−3). In addition, the framework for the data acquisition, data preprocessing, model training, and soil moisture mapping in this study was completed in the cloud environment, which facilitated the entire retrieval process. This work provides new ideas beyond the retrieval model for other related studies.

Mitigating the impact of dense vegetation on the Sentinel-1 surface soil moisture retrievals over Europe

ABSTRACT The C-band Synthetic Aperture Radar (SAR) on board of the Sentinel-1 satellites have a strong potential to retrieve Surface Soil Moisture (SSM). Using a change detection model to Sentinel-1 backscatter, an SSM product at a kilometre scale resolution over Europe could be established in the Copernicus Global Land Service (CGLS). Over areas with dense vegetation and high biomass. The geometry and water content influence the seasonality of the backscatter dynamics and hamper the SSM retrieval quality from Sentinel-1. This study demonstrates the effect of woody vegetation on SSM retrievals and proposes a masking method at the native resolution of Sentinel-1’s Interferometric Wide (IW) swath mode. At a continental 20 m grid, four dense vegetation masks are implemented over Europe in the resampling of the backscatter to a kilometre scale. The resulting backscatter is then used as input for the TUWien (TUW) change detection model and compared to both in-situ and modelled SSM. This paper highlights the potential of high-resolution vegetation datasets to mask for non-soil moisture-sensitive pixels at a sub-kilometre resolution. Results show that both correlation and seasonality of the retrieved SSM are improved by masking the dense vegetation at a 20 m resolution.

Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

Soil moisture is important for understanding climate, water resources, water storage, and land use management. This study used Sentinel-2 (S-2) satellite optical data to retrieve surface soil moisture at a 10 m scale on grassland sites with low hydraulic conductivity soil in a climate dominated by heavy rainfall. Soil moisture was estimated after modifying the Optical Trapezoidal Model to account for mixed land cover in such conditions. The method uses data from a short-wave infra-red band, which is sensitive to soil moisture, and four vegetation indices from optical bands, which are sensitive to overlying vegetation. Scatter plots of these data from multiple, infrequent satellite passes are used to define the range of surface moisture conditions. The saturated and dry edges are clearly non-linear, regardless of the choice of vegetation index. Land cover masks are used to generate scatter plots from data only over grassland sites. The Enhanced Vegetation Index demonstrated advantages over other vegetation indices for surface moisture estimation over the entire range of grassland conditions. In poorly drained soils, the time lag between satellite surface moisture retrievals and in situ sensor soil moisture at depth must be part of the validation process. This was achieved by combining an approximate solution to the Richards’ Equation, along with measurements of saturated and residual moisture from soil samples, to optimise the correlations between measurements from satellites and sensors at a 15 cm depth. Time lags of 2–4 days resulted in a reduction of the root mean square errors between volumetric soil moisture predicted from S-2 data and that measured by in situ sensors, from ~0.1 m3/m3 to <0.06 m3/m3. The surface moisture results for two grassland sites were analysed using statistical concepts based upon the temporal stability of soil water content, an ideal framework for the intermittent Sentinel-2 data in conditions of persistent cloud cover. The analysis could discriminate between different natural drainages and surface soil textures in grassland areas and could identify sub-surface artificial drainage channels. The techniques are transferable for land-use and agricultural management in diverse environmental conditions without the need for extensive and expensive in situ sensor networks.

Simultaneous Retrieval of Land Surface Temperature and Soil Moisture Using Multichannel Passive Microwave Data

Simultaneous Retrieval of Land Surface Temperature and Soil Moisture Using Multichannel Passive Microwave Data

Systematic Modeling Errors Undermine the Application of Land Data Assimilation Systems for Hydrological and Weather Forecasting

Abstract Due to recent advances in the development of land data assimilation systems (LDAS) and the availability of high-quality, satellite-based surface soil moisture (SSM) retrieval products, we now have unambiguous evidence that the assimilation of SSM retrievals, or their proxy, can improve the precision (i.e., correlation versus truth) of surface state estimates provided by a land surface model (LSM). However, this clarity does not yet extend to the estimation of LSM surface water fluxes that are key to hydrologic and numerical weather forecasting applications. Here, we hypothesize that a key obstacle to extrapolating realized improvements in water state precision into comparable improvements in water flux accuracy (i.e., mean absolute error) is the presence of water state–water flux coupling strength biases existing in LSMs. To test this hypothesis, we conduct a series of synthetic fraternal twin data assimilation experiments where realistic levels of state–flux coupling strength bias—involving both evapotranspiration and runoff—are systematically introduced into an assimilation LSM. Results show that the accuracy of the resulting water flux analysis is sharply reduced by the presence of such bias, even in cases where the precision of soil moisture state estimates (e.g., SSM) is improved. The rescaling of SSM observations prior to their assimilation (i.e., the most common approach for addressing systematic differences between LSMs and assimilated observations) is not always a robust strategy for addressing these errors and can, in certain circumstances, degrade water flux accuracy. Overall, results underscore the critical need to assess, and correct for, LSM water state–water flux coupling strength biases during the operation of an LDAS. Significance Statement Land data assimilation is the process by which land surface model estimates of water states (e.g., soil moisture) and water fluxes (e.g., runoff and evapotranspiration) are improved via the incorporation of observations. Over the past decade, substantial improvements have been made in the precision of land surface model states via the assimilation of satellite-based soil moisture information. However, to date, these improvements have not yet been extended into water flux estimates like runoff and evapotranspiration. This is a critical shortcoming since advances in important weather and hydrologic forecasting applications are dependent on the improved estimation of such fluxes. We demonstrate that this shortcoming is linked to the inability of existing land surface models to accurately describe the impact of variations in water states on water fluxes and propose strategies for overcoming this issue in future land data assimilation systems.

Calibration of Dobson model for improving soil moisture retrievals from AMSR satellite imagery

Satellite-based retrieval of environmental variables has seen rapid advancements in recent years, although it remains challenged by various sources of uncertainty. In this study, we endeavor to enhance the accuracy of soil moisture estimation from AMSR imagery through parameter calibration. Our focus is on calibrating the Dobson dielectric mixing model, a component of the radiative transfer model that relies heavily on empirical relationships.
 The calibration process is based on black-box optimization techniques for the retrieval problem. We have adapted the CORS optimization algorithm to address the specific characteristics of our task. We also considered different target functions for calibration.
 To evaluate the efficacy of our framework, we conducted tests across a dataset comprising 118 ground stations in the United States. The outcomes reveal that optimizing parameter settings can provide limited improvement to the accuracy and, when configured accordingly, address specific issues such as bias correction. Calibration emerges as a potent tool for refining surface soil moisture retrievals, although its effectiveness tends to diminish in larger calibration areas.

Retrieval of Surface Soil Moisture over Wheat Fields during Growing Season Using C-Band Polarimetric SAR Data

Accurate estimation and regular monitoring of soil moisture is very important for many agricultural, hydrological, or climatological applications. Our objective was to evaluate potential contributions of polarimetry to soil moisture estimation during crop growing cycles using RADARSAT-2 C-band images. The research focused on wheat field data collected during Soil Moisture Active Passive Validation Experiment (SMAPVEX12) conducted in 2012 in Manitoba (Canada). A sensitivity analysis was performed to select the most relevant non-polarimetric and polarimetric variables extracted from RADARSAT-2, and statistical models were developed to estimate soil moisture. In fine, three models were developed and validated: a non-polarimetric model based on cross-polarized backscattering coefficient σHV0; a polarimetric mixed model using six polarimetric and non-polarimetric retained variables after the sensitivity analysis; and a simplified polarimetric mixed model considering only the phase difference (ϕHH−VV) and the co-polarized backscattering coefficient σHH0. The validation reveals significant positive contributions of polarimetry. It shows that the non-polarimetric model has a much larger error (RMSE = 0.098 m3/m3) and explains only 19% of observed soil moisture variation compared to the polarimetric mixed model, which has an error of 0.087 m3/m3, with an explained variance of 44%. The simplified model has the lowest error (0.074 m3/m3) and explains 53.5% of soil moisture variation.

Sensitivity of surface soil moisture retrieval to satellite-derived vegetation descriptors over wheat fields in the Kairouan plain

ABSTRACT Soil moisture estimation is a key component in hydrological processes and irrigation amounts' estimation. The synergetic use of optical and radar data has been proven to retrieve the surface soil moisture at a field scale using the Water Cloud Model (WCM). In this work, we evaluate the impact of staellite-derived vegetation descriptors to estimate the surface soil moisture. Therefore, we used the Sentinel-1 data to test the polarization ratio ( σ V H 0 / σ V V 0 ) and the normalized polarization ratio (IN) and the frequently used optical Normalized Difference vegetation Index (NDVI) as vegetation descriptors. Synchronous with Sentinel-1 acquisitions, in situ soil moisture were collected over wheat fields in the Kairouan plain in the center of Tunisia. To avoid the bare soil roughness effect and the radar signal saturation in dense vegetation context, we considered the data where the NDVI values vary between 0.25 and 0.7. The soil moisture inversion using the WCM and NDVI as a vegetation descriptor was characterized by an RMSE value of 5.6 vol.%. A relatively close performance was obtained using IN and ( σ V H 0 / σ V V 0 ) with RMSE under 7. 5 vol.%. The results revealed the consistency of the radar-derived data in describing the vegetation for the retrieval of soil moisture.

Retrieval and evaluation of surface soil moisture from CYGNSS using blended microwave soil moisture products

The Global Navigation Satellite System-Reflectometry (GNSS-R) can estimate land surface soil moisture (SSM) as a viable and promising approach. However, it has some large uncertainty in retrieving SSM. In this study, the SSM is retrieved from different Cyclone GNSS (CYGNSS) SSM retrieval models formed with different SSM reference data products, including two blended microwave SSM products from the European Space Agency's Climate Change Initiative (CCI) and the National Oceanic and Atmospheric Administration's Soil Moisture Operational Product System (SMOPS), and a single microwave sensor-derived Soil Moisture Active Passive (SMAP) Level-3 product. The performance of the developed retrieval models, characterized by spatial resolutions of 36 km × 36 km and 0.25° × 0.25°, is evaluated using K-fold cross-validation. Furthermore, the accuracy of these models is validated against ground measurements acquired from Chinese automated soil moisture observation network. In order to alleviate the impact of spatial mismatching between the predicted gridded SSM and the point-scale in-situ measurements, a cumulative distribution function (CDF) rescaling strategy is applied. The results indicated that all models are effective at capturing spatial variations in SSM, and the SMOPS-based model achieves the highest correlation coefficient (0.930) and the lowest root mean square error (RMSD, 0.028 cm3/cm3), followed by the CCI-based model (0.906 and 0.042 cm3/cm3). The SMAP-based model performs poorly in the comparison. The suboptimal performance of models evaluated with Chinese automated soil moisture measurements is largely attributed to the insufficient calibration of the original reference data in the region.

Soil moisture at 30 m from multiple satellite datasets fused by random forest

As a key variable used to characterize the climate process between the land surface and atmosphere, the surface soil moisture (SM) plays an irreplaceable role in the fields of hydrology, meteorology and agriculture. However, the relatively coarse spatial resolution of SM products currently limit the application in water resource management at the field scale. In this study, we proposed a high-spatial-resolution SM retrieval framework based on the random forest algorithm (RF-SM) to integrate in situ SM dataset from in the International Soil Moisture Network (ISMN), Landsat 8 optical and thermal observations, soil properties from SoilGrids V2.0, meteorological variables from the fifth generation of the European ReAnalysis (ERA5) dataset and four coarse-scale SM products including the Soil Moisture Active/Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), European Space Agency Climate Change Initiative (ESA CCI) and U.S. National Climate Assessment Land Data Assimilation System (NCA-LDAS). Compared to three other machine learning (ML) algorithms [extremely randomized trees (Extra-Trees), gradient boosting regression tree (GBRT) and extreme gradient boosting (XGBoost)], the random forest (RF) algorithm exhibited the best performance against a subset of 100 validation sites with a Kling–Gupta efficiency (KGE) of 0.69 and root-mean-square-error (RMSE) of 0.063 m3/m3. In terms of different land cover types and typical sites, RF-SM also showed a better accuracy than any of the individual SM product. Finally, the retrieval framework was applied to map the 30-m resolution SM spatial distributions in five substudy areas in the U.S. The results suggest that it is feasible to retrieve accurate SM at a 30-m spatial resolution from multiple satellite datasets based on the RF algorithm, which has important practical significance for agricultural drought monitoring at the field scale.

A Bayesian machine learning method to explain the error characteristics of global-scale soil moisture products

Estimating accurate surface soil moisture (SM) dynamics from space, and knowing the error characteristics of these estimates, is of great importance for the application of satellite-based SM data throughout many Earth Science/Environmental Engineering disciplines. Here, we introduce the Bayesian inference approach to analyze the error characteristics of widely used passive and active microwave satellite-derived SM data sets, at different overpass times, acquired from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) missions. In particular, we apply Bayesian hierarchical modeling (BHM) and triple collocation analysis (TCA) to investigate the relative importance of different environmental factors and human activities on the accuracy of satellite-based data.To start, we compare the BHM-based sensitivity analysis method to the classic multiple regression models using a frequentist approach, which includes complete pooling and no-pooling models that have been widely used for sensitivity analysis in the field of remote sensing and demonstrate the BHM's adaptability and great potential for providing insight into sensitivity analysis that can be used by various remote sensing research communities.Next, we conduct an uncertainty analysis on BHM's model parameters using a full range of uncertainties to assess the association of various environmental factors with the accuracy of satellite-derived SM data. We focus on investigating human-induced error sources such as disturbed surface soil layers caused by irrigation activities on microwave satellite systems, naturally introduced error sources such as vegetation and soil organic matter, and errors related to the disregard of SM retrieval algorithmic assumptions - such as the thermal equilibrium passive microwave systems. Based on the BHM-based sensitivity analysis, we find that assessments of SM data quality with a single variable should be avoided, since numerous other factors simultaneously influence their quality. As such, this provides a useful framework for applying Bayesian theory to the investigation of the error characteristics of satellite-based SM data and other time-varying geophysical variables.

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).

Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection

Global Navigation Satellite System interferometric reflectometry (GNSS-IR), as a new remote sensing detection technology, can retrieve surface soil moisture (SM) by separating the modulation terms from the effective signal-to-noise ratio (SNR) data. However, traditional low-order polynomials are prone to over-fitting when separating modulation terms. Moreover, the existing research mainly relies on prior information to select satellites for SM retrieval. Accordingly, this study proposes a method based on empirical modal decomposition (EMD) and cross-correlation satellite selection (CCSS) for SM retrieval. This method intended to adaptively separate the modulation terms of SNR through the combination of EMD and an intrinsic mode functions (IMF) discriminant method, then construct a CCSS method to select available satellites, and finally establish a multisatellite robust estimation regression (MRER) model to retrieve SM. The results indicated that with EMD, the different feature components implied in the SNR data of different satellites could be adaptively decomposed, and the trend and modulation terms of the SNR could more accurately be acquired by the IMF discriminant method. The available satellites could be efficiently selected through CCSS, and the SNR quality of different satellites could also be classified at different accuracy levels. Furthermore, MRER could fuse the multisatellite phases well, which enhanced the accuracy of SM retrieval and further verified the feasibility and effectiveness of combining EMD and CCSS. When rm=0.600 and rn=0.700, the correlation coefficient (r) of the multisatellite combination reached 0.918, an improvement of at least 40% relative to the correlation coefficient of a single satellite. Therefore, this method can improve the adaptive ability of SNR decomposition, and the selection of satellites has high flexibility, which is helpful for the application and popularization of the GNSS-IR technology.

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.

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.

Surface soil moisture retrieval through combining LST-VI feature space with soil porosity

ABSTRACT Surface soil moisture (SSM) is an important variable for controlling the energy exchange between earth’s surface and atmosphere. Accurate estimation of the distribution of SSM and its dynamic changes is increasingly essential in the study of hydrology, ecology, agriculture, geological hazard monitoring, and global climate change. This paper presents a new model for SSM retrieval (named, KSSM) through assimilating soil porosity with a two-dimensional feature space composed of land surface temperature and vegetation index (LST-VI). The KSSM model takes into account the physical properties of soil porosity, so it is suitable for SSM retrieval of various soil types in different regions. The KSSM model was validated using the measured data, and a comparison analysis with other data was conducted. Through the sensitivity analysis, it is concluded that the error of SSM retrieved by the KSSM model is less than 0.04 m3 m−3 and the KSSM model is robust. High accuracy of volumetric SSM estimation, error within 10% compared with the measured data, can be achieved by the KSSM model without the need for an in-situ SSM.