Soil Moisture Inversion Research Articles

Soil Moisture Monitoring Based on Deformable Convolution Unit Net Algorithm Combined with Water Area Changes

In response to the issue that existing soil moisture monitoring methods are significantly affected by surface roughness and the complex environment around water bodies, leading to a need for improvement in the accuracy of soil moisture inversion, a soil moisture detection algorithm based on a DCU-Net (Deformable Conv Unit-Net) water body extraction model is proposed, using the Ningxia region as the study area. The algorithm introduces the DCU (Deformable Conv Unit) module, which addresses the problem of extracting small water bodies at large scales with low resolution; reduces the probability of misjudgment during water body extraction caused by shadows from mountains, buildings, and other objects; and enhances the robustness and adaptability of the water body extraction algorithm. The method first creates a water body extraction dataset based on multi-year remote sensing images from Ningxia Province and trains the proposed DCU-Net model; then, it selects remote sensing images from certain areas for water body extraction; finally, it conducts regression analysis between the water body areas of Ningxia Province at different times and the corresponding measured soil moisture data to establish the intrinsic relationship between water body areas and soil moisture in the study area, achieving real-time regional soil moisture monitoring. The water body extraction performance of DCU-Net is verified based on extraction accuracy, with U-Net selected as the baseline network. The experimental results show that DCU-Net leads to improvements of 2.98%, 1.37%, 0.36%, and 1.49% in terms of IoU, Precision, Recall, and F1, respectively. The algorithm is more sensitive to water body feature information, can more accurately identify water bodies, and extracts water body contours more accurately. Additionally, a soil moisture inversion method based on a cubic polynomial is constructed. These results indicate that DCU-Net can precisely extract water body contours and accurately invert regional soil moisture, thereby providing support for the monitoring of large-scale soil moisture.

Evaluation of Satellite-Based Global Navigation Satellite System Reflectometry (GNSS-R) Soil Moisture Products in Complex Terrain: A Case Study of the Yunnan–Guizhou Plateau

Complex terrain is one of the main factors affecting the process of retrieving surface soil moisture using GNSS-R technology. This study evaluates the impact of complex terrain on surface soil moisture inversion using Cyclone Global Navigation Satellite System (CYGNSS) L3 SSM products, with Soil Moisture Active Passive (SMAP) SSM products as the true value. The errors in CYGNSS SSM are primarily attributed to med–high elevation and large relief. Compared with the Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products, CYGNSS exhibits superior performance in terms of AD and RMSE (median AD = −0.10 m3/m3, RMSE = 0.14 m3/m3). The ubRMSE of CYGNSS (median ubRMSE = 0.094 m3/m3) outperforms SMOS, but is slightly worse than AMSR2, with the differences mainly observed in med–high elevation and large-relief regions. The three satellites complement each other in detecting complex terrain. CYGNSS errors (AD, RMSE) are higher in the rainy season than in the dry season, with greater discrepancies observed in large-relief, high-elevation regions compared to flatter, lower-elevation areas. This study provides the first comprehensive analysis of CYGNSS in such a complex region, offering valuable insights for improving the application of GNSS-R inversion technology.

Estimating and Downscaling ESA-CCI Soil Moisture Using Multi-Source Remote Sensing Images and Stacking-Based Ensemble Learning Algorithms in the Shandian River Basin, China

Soil Moisture (SM) plays a crucial role in agricultural production, ecology, and sustainable development. The prevailing resolution of microwave-based SM products is notably coarse, typically spanning from 10 to 50 km, which might prove inadequate for specific applications. In this research, various single-model machine learning algorithms have been employed to study SM downscaling, each with its own limitations. In contrast to existing methodologies, our research introduces a pioneering algorithm that amalgamates diverse individual models into an integrated Stacking framework for the purpose of downscaling SM data within the Shandian River Basin. This basin spans the southern region of Inner Mongolia and the northern area of Hebei province. In this paper, factors exerting a profound influence on SM were comprehensively integrated. Ultimately, the surface variables involved in the downscaling process were determined to be Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Surface Reflectance (SR), Evapotranspiration (ET), Digital Elevation Model (DEM), slope, aspect, and European Space Agency-Climate Change Initiative (ESA-CCI) product. The goal is to generate a 1 km SM downscaling dataset for a 16-day period. Two distinct models are constructed for the SM downscaling process. In one case, the downscaling is followed by the inversion of SM, while in the other case, the inversion is performed after the downscaling analysis. We also employ the Categorical Features Gradient Boosting (CatBoost) algorithm, a single model, for analytical evaluation in identical circumstances. According to the results, the accuracy of the 1 km SM obtained using the inversion-followed-by-downscaling model is higher. Furthermore, it is observed that the stacking algorithm, which integrates multiple models, outperforms the single-model CatBoost algorithm in terms of accuracy. This suggests that the stacking algorithm can overcome the limitations of a single model and improve prediction accuracy. We compared the predicted SM and ESA-CCI SM; it is evident that the predicted results exhibit a strong correlation with ESA-CCI SM, with a maximum Pearson correlation coefficient (PCC) value of 0.979 and a minimum value of 0.629. The Mean Absolute Error (MAE) values range from 0.002 to 0.005 m3/m3, and the Root Mean Square Error (RMSE) ranges from 0.003 to 0.006 m3/m3. Overall, the results demonstrate that the stacking algorithm based on multi-model integration provides more accurate and consistent retrieval and downscaling of SM.

A Time-Constrained and Spatially Explicit AI Model for Soil Moisture Inversion Using CYGNSS Data

A Time-Constrained and Spatially Explicit AI Model for Soil Moisture Inversion Using CYGNSS Data

Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Estimation and analysis of vegetation parameters for the water cloud model

AbstractThe Water Cloud Model (WCM) plays a crucial role in active microwave soil moisture inversion applications. Empirical parameters are important factors affecting the accuracy of WCM simulation, but the current evaluation of empirical parameters only considers the forward simulation process, and insufficient consideration is given to the model inversion problem. This study proposes a new estimation method for vegetation parameters in the WCM by combining the soil backscattering model and the objective function. The effectiveness of the method is then verified using measured data. Simultaneously, this study also analyzes the factors influencing the evaluation of vegetation parameters in the WCM, resulting in the following conclusions. First, blindly utilizing vegetation parameters recommended by previous model studies is not advisable. To ensure the accuracy of the simulation, it is necessary to adjust the vegetation parameters appropriately. Second, to ensure the ability of the WCM solving both forward and inverse problems, it is advisable to consider both soil backscatter and surface backscatter simulations in the construction of the cost function. Third, soil backscatter simulations have an impact on the solution of vegetation parameters, and more accurate soil scattering models provide a better representation of the modeled vegetation. This study presents a dependable method for resolving the vegetation parameters of the WCM, thereby offering a valuable reference for the application of the model in surface parameter inversion research.

Ensemble Machine-Learning-Based Framework for Estimating Surface Soil Moisture Using Sentinel-1/2 Data: A Case Study of an Arid Oasis in China

Soil moisture (SM) is a critical parameter in Earth’s water cycle, significantly impacting hydrological, agricultural, and meteorological research fields. The challenge of estimating surface soil moisture from synthetic aperture radar (SAR) data is compounded by the influence of vegetation coverage. This study focuses on the Weigan River and Kuche River Delta Oasis in Xinjiang, employing high-resolution Sentinel-1 and Sentinel-2 images in conjunction with a modified Water Cloud Model (WCM) and the grayscale co-occurrence matrix (GLCM) for feature parameter extraction. A soil moisture inversion method based on stacked ensemble learning is proposed, which integrates random forest, CatBoost, and LightGBM. The findings underscore the feasibility of using multi-source remote sensing data for oasis moisture inversion in arid regions. However, soil moisture content estimates tend to be overestimated above 10% and underestimated below 5%. The CatBoost model achieved the highest accuracy (R2 = 0.827, RMSE = 0.014 g/g) using the top 16 feature parameter groups. Additionally, the R2 values for Stacking1 and Stacking2 models saw increases of 0.008 and 0.016, respectively. Thus, integrating multi-source remote sensing data with Stacking models offers valuable support and reference for large-scale estimation of surface soil moisture content in arid oasis areas.

Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province

Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province

Quality control and improvement of GNSS-IR soil moisture robust inversion model

Quality control and improvement of GNSS-IR soil moisture robust inversion model

Research on GNSS-IR soil moisture retrieval based on random forest algorithm

Abstract Soil moisture (SM) retrieval is of great significance in climate, agriculture, ecology, hydrology, and natural disaster monitoring, and it is one of the essential hydrometeorological parameters studied in the world at present. With the continuous development of the GNSS, a technique called GNSS-IR became widely used in ground SM inversion. Therefore, based on the frequency, amplitude and phase of signal-to-noise ratio residuals (δSNR), this study takes P037 and P043 stations set by UNAVCO in the United States as examples and develops the research of SM inversion from Random Forest Regression (RFR) prediction. The experimental results show that the retrieval accuracy of SM under different practical schemes can be in descending order: L1 + L2 dual frequency combination > L2 single frequency > L1 single frequency. It is confirmed that the experimental scheme based on the L1+L2 dual-frequency combination is beneficial to the inversion of SM. In the L1+L2 dual-frequency combination, the prediction set accuracy of the P037 station is as follows: R is 0.796, RMSE is 0.032 cm3cm-3, ME is 0.002 cm3cm-3. The prediction accuracy of the P043 station is as follows: R is 0.858, RMSE is 0.039 cm3cm-3, ME is -0.009 cm3cm-3. Among them, the RMSE of the L1+L2 dual-frequency combination of the two stations has an improvement effect of 13%-37% compared with their single-frequency, which has a noticeable improvement effect. The difference between the SM retrieved by GNSS-IR and the reference value of PBO-H2O is concentrated around 0, further showing the accuracy of SM retrieved by GNSS-IR technology. To sum up, this study considers that SM retrieval based on the RFR model has good reliability and accuracy, which makes GNSS-IR technology an efficient means for SM retrieval. With the continuous improvement of the GNSS system and technology, the application of GNSS-IR technology in SM will become broader.

Inversion of Farmland Soil Moisture Based on Multi-Band Synthetic Aperture Radar Data and Optical Data

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM.

Using the AIEM and Radarsat-2 SAR to Retrieve Bare Surface Soil Moisture

Taking the Jiefangzha irrigation area of the Inner Mongolia Autonomous Region as the research area, the response relationships between the backscattering coefficient and radar frequency, radar incidence angle, root-mean-square height, correlation length, and soil water content under different conditions were simulated using advanced integral equations. The backscattering characteristics of exposed surfaces in cold and dry irrigation areas were discussed, and the reasons for the different effects were analyzed. Based on this, surface roughness models and statistical regression moisture inversion models were constructed through co-polarized backscatter coefficients and combined surface roughness. The correlation between the inverted surface roughness values and the measured values was R2 = 0.7569. The correlation between the soil moisture simulation values and the measured values was R2 = 0.8501, with an RMSE of 0.04. The findings showed a strong correlation between the values from the regression simulation and the measured data, indicating that the model can be applied to soil moisture inversion and has a good inversion accuracy. Compared with previous studies in the same area, the inversion model proposed in this paper has a higher accuracy and is more suitable for the inversion of soil moisture in the Jiefangzha irrigation area. These findings can support research on the water cycle and water environment assessment in the region.

Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.

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.

Multi-Model Comprehensive Inversion of Surface Soil Moisture from Landsat Images Based on Machine Learning Algorithms

Soil moisture plays an important role in maintaining ecosystem stability and sustainable development, especially for the upper reaches of the Yellow River region. Therefore, accurately and conveniently monitoring soil moisture has become the focus of scholars. This study combines three machine learning algorithms: random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN)—with the traditional monitoring of soil moisture using remote sensing indices to construct a more accurate soil moisture inversion model. To enhance the accuracy of the soil moisture inversion model, 27 environmental variables were screened and grouped, including vegetation index, salinity index, and surface temperature, to determine the optimal combination of variables. The results show that screening the optimal independent variables in the Xijitan landslide distribution area lowered the root mean square error (RMSE) of the RF model by 16.95%. Of the constructed models, the combined model shows the best applicability, with the highest R2 of 0.916 and the lowest RMSE of 0.877% with the test dataset; the further research shows that the BPNN model achieved higher overall accuracy than the other two individual models, with the test set R2 being 0.809 and the RMSE 0.875%. The results of this study can provide a theoretical reference for the effective use of Landsat satellite data to monitor the spatial and temporal distribution of and change in soil water content on the two sides of the upper Yellow River basin under vegetation cover.

UAV-Based Remote Sensing to Evaluate Daily Water Demand Characteristics of Maize: A Case Study from Yuci Lifang Organic Dry Farming Experimental Base in Jinzhong City, China

Soil moisture critically influences crop growth, especially in dryland environments. Precise agricultural management requires real-time monitoring of stratified soil moisture and assessment of crops’ daily water needs. We aim to provide low-cost, high-throughput information acquisition services for dryland regions with underdeveloped infrastructure and offer scientific support for sustainable water resource management. We used UAVs (Unmanned Aerial Vehicles) with multi-spectral sensors for routine maize monitoring, capturing leaf reflectance. Constructing vegetation indices, we quantified the relationship between leaf water content and surface soil moisture, using the Biswas model to predict deep soil moisture distribution. We used UVAs to monitor crop height and calculated the daily water demand for the entire growth period of corn using the Penman Montes equation. We found an R2 of 0.8603, RMSE of 2.455%, and MAE of 2.099% between NDVI and canopy leaf water content. A strong linear correlation (R2 = 0.7510) between canopy leaf water content and soil moisture was observed in the top 20 cm of soil. Deep soil moisture inversion from the top 20 cm soil moisture showed an R2 of 0.9984, with RE mostly under 10%, but exceeding 20% at 120 cm depth. We also constructed a maize height model aligning with a sigmoidal growth curve (R2 = 0.9724). Maize’s daily water demand varied from 0.7121 to 9.4263 mm, exhibiting a downward-opening parabolic trend. Integration of rainfall and soil water data allowed for dynamic irrigation adjustments, mitigating drought and water stress effects on crops. We highlighted UAV multi-spectral imaging’s effectiveness in monitoring crop water needs, facilitating quick daily water requirement estimations. Our work offers a scientific foundation for managing maize cultivation in drylands, enhancing water resource utilization.

Drone-Based Multispectral Remote Sensing Inversion for Typical Crop Soil Moisture under Dry Farming Conditions

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas.

Downscale Inversion of Soil Moisture during Vegetation Growth Period in Ebinur Lake Watershed

Soil moisture content is an important measure of soil health, and high-precision soil moisture trend analysis is essential for understanding regional ecological quality in the context of climate change, flood monitoring, and water cycle processes. However, in the arid regions of Central Asia, where data are severely lacking, obtaining high-spatial-resolution, continuous soil moisture data is difficult due to the scarcity of stations. Moreover, because soil moisture is easily affected by evaporation time, surface morphology, and anthropogenic factors, mature theoretical models or empirical or semiempirical models to measure soil moisture are also lacking. To investigate the distribution and trend of soil moisture in the Ebinur Lake water, in this study, microwave remote sensing and visible remote sensing data were selected as inputs, and the Global Land Data Assimilation System (GLDAS-2.2) data products were downscaled using the GTWR model, which increased the spatial scale from 27,830 m × 27,830 m to 30 m × 30 m. The phenomena involved in the soil moisture change cycle, spatial distribution, temporal variation, and internal randomness distance were analyzed in the study area through wavelet analysis, Theil–Sen trend analysis, the Mann–Kendall (MK) test, and a variogram. This study obtained high-resolution continuous soil moisture data in the arid and data-scarce region in Central Asia, thus broadening the field of multisource remote sensing analysis and providing a theoretical basis for the construction of precision agriculture in northwest China.

A temporal gap filling approach for GNSS-IR soil moisture inversion based on generalized extension

A temporal gap filling approach for GNSS-IR soil moisture inversion based on generalized extension

GNSS-IR Soil Moisture Inversion Derived from Multi-GNSS and Multi-Frequency Data Accounting for Vegetation Effects

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique provides a new remote sensing method that shows great potential for soil moisture detection and vegetation growth, as well as for climate research, water cycle management, and ecological environment monitoring. Considering that the land surface is always covered by vegetation, it is essential to take into account the impacts of vegetation growth when detecting soil moisture (SM). In this paper, based on the GNSS-IR technique, the SM was retrieved from multi-GNSS and multi-frequency data using a machine learning model, accounting for the impact of the vegetation moisture content (VMC). Both the signal-to-noise ratio (SNR) data that was used to retrieve SM and the multipath data that was used to eliminate the vegetation influence were collected from a standard geodetic GNSS station located in Nanjing, China. The normalized microwave reflectance index (NMRI) calculated by multipath data was mapped to a normalized difference vegetation index (NDVI), which was derived from Sentinel-2 data on the Google Earth Engine platform to estimate and eliminate the influence of VMC. Based on the characteristic parameters of amplitude and phase extracted from detrended SNR signals and NDVI derived from multipath data, three machine learning methods, including random forest (RF), multiple linear regression (MLR), and multivariate adaptive regression spline (MARS), were employed for data fusion. The results show that the vegetation effect can be well eliminated using the NMRI method. Comparing MLR and MARS, RF is more suitable for GNSS-IR SM inversion. Furthermore, the SM reversed from amplitude and phase fusion is better than only those from either amplitude fusion or phase fusion. The results prove the feasibility of the proposed method based on a multipath approach to characterize the vegetation effect, as well as the RF model to fuse multi-GNSS and multi-frequency data to retrieve SM with vegetation error-correcting.