Soil Moisture Retrieval Methods Research Articles

GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model’s Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm3/cm3, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm3/cm3, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture.

A GNSS-IR soil moisture retrieval method via multi-layer perceptron with consideration of precipitation and environmental factors

A GNSS-IR soil moisture retrieval method via multi-layer perceptron with consideration of precipitation and environmental factors

An Illustration of FY-3E GNOS-R for Global Soil Moisture Monitoring.

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm's development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

The Correction Method of Water and Fresnel Reflection Coefficient for Soil Moisture Retrieved by CYGNSS

Spaceborne GNSS-R technology is a new remote sensing method for soil moisture monitoring. Focusing on the significant influence of water on the surface reflectivity of CYGNSS, this paper improved the removal method of water influence according to the spatial resolution of CYGNSS data. Due to the disturbance effect of the incident angle, microwave frequency and soil type on the Fresnel reflection coefficient in surface reflectivity, a normalization method of Fresnel reflection coefficient was proposed after analyzing the data characteristics of variables in the Fresnel reflection coefficient. Finally, combined with the soil moisture retrieval method of linear equation, the accuracy was compared and verified by using measured data, SMAP products and official CYGNSS products. The results indicate that the normalization method of the Fresnel reflection coefficient could effectively reduce the influence of relevant parameters on the Fresnel reflection coefficient, but the normalization effect became worse at large incident angles (greater than 65°). Compared with the official CYGNSS product, the retrieval accuracy of optimized soil moisture was improved by 10%. The method proposed in this paper will play an important reference role in the study of soil moisture retrieval using spaceborne GNSS-R data.

Soil Moisture Retrieval From Sentinel-1 and Sentinel-2 Data Using Ensemble Learning Over Vegetated Fields

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

A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR

ABSTRACT Surface roughness plays an important role in microwave remote sensing. In the agricultural domain, surface roughness is crucial for soil moisture retrieval methods that use electromagnetic surface scattering or microwave radiative transfer models. Therefore, improved characterization of Soil Surface Roughness (SSR) is of considerable importance. In this study, three approaches, including a standard pin profiler, a LiDAR point cloud generated from an iPhone 12 Pro, and a Structure from Motion (SfM) photogrammetric point cloud, were applied over 24 surface profiles with different roughness variations to measure surface roughness. The objective of this study was to evaluate the capability of smartphone-based LiDAR technology to measure surface roughness parameters and compare the results of this technique with the more common approaches. Results showed that the iPhone LiDAR technology, when point cloud data is captured in a fine-resolution mode, has a significant correlation with SfM photogrammetry (R 2 = 0.70) and a relatively close agreement with pin profiler (R 2 = 0.60). However, this accuracy tends to be greater for random surfaces and rough profiles with row structure orientations. The results of this study confirm that smartphone-based LiDAR can be used as a cost-effective, fast, and time-efficient alternative tool for measuring surface roughness, especially for rough, wide, and inaccessible areas.

A method for estimating surface soil moisture from diurnal land surface temperature observations over vegetated regions: A preliminary result over an AmeriFlux site and the REMEDHUS network

Most current optical/thermal-based surface soil moisture (SSM) retrieval methods only utilize the instantaneous measurements made by onboard polar-orbit satellites, and they require soil texture as prior knowledge to obtain the volumetric SSM content. A recent study developed a diurnal land surface temperature (LST)-based SSM retrieval method to directly estimate the volumetric SSM content without the knowledge of soil texture. However, most subsequent studies have only examined the use of this method on bare soils. Therefore, the present study aims to investigate the feasibility of the method over vegetated regions with both experimental data and satellite observations. First, the impacts of vegetation on SSM retrieval were investigated through physics-based Common Land Model simulations and high accuracy with an overall root mean square error (RMSE) of 0.010 m3m−3 was achieved when the underlying surface was covered by moderate fractional vegetation cover from 0 to 0.6 with a given soil texture. Based on the simulated results, the SSM was then estimated using data from an AmeriFlux site dominated by crops during the growing season in 2018; the results were further verified using in-situ measurements, and this showed that the estimated SSM correlated well with in-situ measurements (with an RMSE of 0.078 m3m−3). A further evaluation of the Meteosat Second Generation (MSG)-derived SSM at the pixel scale was made using the proposed method, and the results revealed an overall acceptable accuracy (RMSE of 0.074 m3m−3 and bias of 0.071 m3m−3) against the SMAP/Sentinel-1 SSM product at the same spatial resolution of 3 km.

Retrieval of Soil Moisture Content Based on Multisatellite Dual-Frequency Combination Multipath Errors

Global navigation satellite system interferometric reflectometry (GNSS-IR) is a new type of microwave remote sensing technology that can measure soil moisture content (SMC). GNSS-IR soil moisture retrieval methods based on the satellite signal-to-noise ratio (SNR) and triple-frequency signal combination have the following shortcomings: SNR does not always exist in the original GNSS file, and the number of triple-frequency signal observation satellites is small, resulting in GNSS-IR soil moisture observation time resolution being low. Based on the above problems, in this study, we constructed a soil moisture inversion method based on multisatellite dual-frequency combined multipath error is proposed: the multipath error calculation model of dual-frequency carrier phase (L4 Ionosphere Free, L4_IF) and dual-frequency pseudorange (DFP) without ionospheric effect is constructed. We selected the data of the five epochs before and after the time point of the effective satellite period to construct the multipath error model and error equation, and we solved the delay phase for soil moisture retrieval. We verified the method using Plate Boundary Observatory (PBO) P041 site data. The results showed that the Pearson correlation coefficients (R) of L4_IF and DFP methods at P041 station are 0.97 and 0.91, respectively. To better verify the results’ reliability and the proposed method’s effectiveness, the soil moisture data of the MFLE station about 210 m away from P041 station are used as the verification data in this paper. The results showed that the delay phase solved by multipath error and soil moisture strongly correlate. Pearson correlation coefficients (R) of L4_IF and DFP methods at MFLE station are 0.93 and 0.86, respectively. In order to improve the inversion accuracy of GNSS-IR soil moisture, this paper constructs the prediction model of soil moisture by using the linear regression (ULR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), and evaluates the accuracy of each model. The results showed that the soil moisture retrieval method based on multisatellite dual-frequency combined multipath error can replace the traditional retrieval method and effectively improve the time resolution of GNSS-IR soil moisture estimation. To perform highly dynamic monitoring of soil moisture, higher retrieval accuracy can only be obtained with a small epoch multipath error.

Field-scale soil moisture estimation using sentinel-1 GRD SAR data

Soil moisture is a critical land variable that controls the energy and mass balance in land–atmosphere interactions. Spaceborne Synthetic Aperture Radar (SAR) sensors offer an efficient way to map and monitor soil moisture because of their sensitivity towards the dielectric and geometric properties of the target. In addition, SAR acquisitions are weather-independent, providing a significant advantage over optical imaging during periods of cloud cover. However, vegetation cover makes these processes more complex and influences the interaction of SAR backscatter resulting from combined soil matrix and vegetation cover. Therefore, using SAR data, it is necessary to compensate for vegetation contribution in total backscatter while estimating soil moisture over the vegetated soil surface. This study presents a technique that utilizes a vegetation index derived from SAR data to generate high-resolution soil moisture maps. It is noteworthy that this proposed soil moisture retrieval method uses only the dual-polarimetric Ground Range Detected (GRD) SAR product, i.e., only backscatter intensities. Hence, the proposed method has a high potential for operational soil moisture monitoring globally. We validated over 34 soil moisture stations of the Texas Soil Observation Network (TxSON) using time-series Sentinel-1 SAR data. The Root Mean Square Error (RMSE) values for estimated volumetric soil moisture are within the range of 0.048 m3 m−3 to 0.055 m3 m−3 with the Pearson correlation coefficient r>0.79. The code to generate DpRVIc in Google Earth Engine is available at: https://github.com/Narayana-Rao/dual_pol_descriptors.

Using BDS MEO and IGSO Satellite SNR Observations to Measure Soil Moisture Fluctuations Based on the Satellite Repeat Period

Soil moisture is an important geophysical parameter for studying terrestrial water and energy cycles. It has been proven that Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) can be applied to monitor soil moisture. Unlike the Global Positioning System (GPS) that has only medium earth orbit (MEO) satellites, the Beidou Navigation Satellite System (BDS) also has geosynchronous earth orbit (GEO) satellites and inclined geosynchronous satellite orbit (IGSO) satellites. Benefiting from the distribution of three different orbits, the BDS has better coverage in Asia than other satellite systems. Previous retrieval methods that have been confirmed on GPS cannot be directly applied to BDS MEO satellites due to different satellite orbits. The contribution of this study is a proposed multi-satellite soil moisture retrieval method for BDS MEO and IGSO satellites based on signal-to-noise ratio (SNR) observations. The method weakened the influence of environmental differences in different directions by considering satellite repeat period. A 30-day observation experiment was conducted in Fengqiu County, China and was used for verification. The satellite data collected were divided according to the satellite repeat period, and ensured the response data moved in the same direction. The experimental results showed that the BDS IGSO and MEO soil moisture estimation results had good correlations with the in situ soil moisture fluctuations. The BDS MEO B1I estimation results had the best performance; the estimation accuracy in terms of correlation coefficient was 0.9824, root mean square error (RMSE) was 0.0056 cm3cm−3, and mean absolute error (MAE) was 0.0040 cm3cm−3. The estimations of the BDS MEO B1I, MEO B2I, and IGSO B2I performed better than the GPS L1 and L2 estimations. For the BDS IGSO satellites, the B1I signal was more suitable for soil moisture retrieval than the B2I signal; the correlation coefficient was increased by 19.84%, RMSE was decreased by 42.64%, and MAE was decreased by 43.93%. In addition, the BDS MEO satellites could effectively capture sudden rainfall events.

An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data

Soil moisture monitoring using Global Navigation Satellite System (GNSS) multipath signals has gained continuous interests in recent years. However, traditional GNSS-interferometric reflectometry (GNSS-IR) soil moisture retrieval methods generally utilize a single frequency or single satellite, which fail to take full advantage of different and complementary of satellite signals with different frequencies. An improved algorithm for soil moisture retrieval based on principal component analysis (PCA) and entropy method using multi-frequency amplitude and phase offset fusion data was proposed in this research. The performance of the proposed soil moisture retrieval method was evaluated using data recorded by Plate Boundary Observatory (PBO) H2O networks and a self-built site in Henan, China. The results from GPS and BeiDou both showed that the retrieved soil moisture has a stronger correlation with in situ soil moisture, which can better reflect the fluctuation of ground truth measurements. Compared with the traditional method, the retrieval accuracy of the proposed method in terms of root-mean-square error (RMSE) was improved by 50.93%, and the average correlation coefficient were increased by 11.71%. This research proved that the proposed method could effectively improve retrieval accuracy due to the increasing number of frequencies and tracks clustering. Moreover, this study has illustrated the feasibility of BeiDou signals to precisely estimate surface soil moisture.

A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains

Soil moisture retrieval from Synthetic Aperture Radar (SAR) over vegetated terrains requires an isolation of soil and canopy signals from observed backscatter (σ°). This study develops a probabilistic soil moisture retrieval method from dual polarimetric C-band SAR Sentinel-1 (S-1) with uncertainty quantification at distinct vegetation covers (VCs). Both σVV° and σVH° were used to represent ground and volume scattering due to the high respective sensitivity to soil moisture and vegetation dynamics. A novel D-vine copula quantile regression (DVQR) was adopted to provide the soil moisture estimates based on modelling trivariate dependence of σVV°-σVH°-soil moisture anomalies (VV-VH-Mv), with a support from the innovative cosmic-ray soil moisture as ground-truth data. The feasibility of DVQR was underlined for: (1) multivariate nonlinear dependence structure modelling and (2) soil moisture retrieval with associated uncertainty. An inter-dependence analysis, which assesses the correlations among three major variables, indicated that the dependence between each pair of variables decreased as canopy density increases from herbs to forests, mainly due to the σ° attenuated by vegetation effect. The dependence structures simulated from the D-vine copula revealed highly nonlinear and asymmetric shapes with tail dependences occurred in most VCs, which can be well captured by different associated Archimedean copulas. Soil moisture anomaly (Mv) estimated using the DVQR and Multiple linear quantile regression (MLQR) were compared against ground-truth data for both in-sample and out-of-sample predictions. Superior performances of the DVQR in most VCs, with 10% and 16% improved in RMSE at grasslands and broadleaf forests, respectively, demonstrated the robustness of this method for S-1 soil moisture retrieval due to the highly nonlinear dependence structures captured by the D-vine models. Over VCs, better performances were obtained at low-canopy herbaceous regions (grasslands and croplands); whereas extremely dry conditions and complex structures in shrublands and dense forests resulted in inferior performances. A sensitivity analysis was then conducted to evaluate the change in Mv estimation accuracy given distinct VV and VH quantile levels. Result underlines that VV is the primary factor controlling the retrieval accuracy, but the increase in VH level also contributes to higher errors in soil moisture estimation, especially under wet conditions.

Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands

ABSTRACT Synthetic aperture radar (SAR) data have significant potential for soil moisture monitoring because of their high spatial resolution and independence from cloud coverage. However, it is challenging to retrieve soil moisture from SAR data over vegetated areas, as vegetation significantly affects backscattered radar signals. Auxiliary vegetation information obtained from optical images, such as the normalized difference vegetation index (NDVI) and the leaf area index (LAI), is commonly used to correct vegetation effects. However, it is generally difficult to obtain SAR and optical data in the same area simultaneously, because of the discrepancies in satellite coverage and the effects of cloud coverage. This study focuses on whether vegetation descriptors obtained directly from radar data at L-band can adequately parameterize the semi-empirical backscattering water cloud model (WCM) to support soil moisture retrieval. Four vegetation descriptors (three based on radar images and one based on optical images), were chosen to assess the parameterization and calibration of the WCM and the retrieval accuracy of soil moisture. The results showed that the vegetation descriptor of backscattering at VH polarization outperformed the other three vegetation descriptors (NDVI-derived vegetation water content, radar vegetation index, and the ratio of cross-polarization to VV polarization) in the investigation of four crop types (canola, corn, bean, and wheat) based on the Soil Moisture Active Passive Validation Experiment in 2012 (SMAPVEX12) in Canada. For the vegetation descriptor of VH, the overall accuracy of retrieved soil moisture was promising by separating into two growth stages, with unbiased root mean squared errors of 0.056, 0.053, 0.098, and 0.079 cm3/cm3 for canola, corn, bean, and wheat, respectively. The results also confirmed that variations in vegetation growth affect the accuracy of soil moisture retrieval. In addition, the retrieval performance was undermined when the vegetation changed dramatically, leading to variations or uncertainty in the vegetation structure. This study provides new insights into soil moisture retrieval methods with active L-band microwave observations.

Soil Moisture Estimation based on the Distributed Scatterers Adaptive Filter over the QTP Permafrost Region using Sentinel-1 and High-resolution TerraSAR-X Data

ABSTRACT Synthetic aperture radar (SAR) data have been widely and primarily used for soil-moisture estimation. The original radar backscattering coefficients show great noise and temporal-spatial decorrelation due to complex surface features and seasonal vegetation changes, which increase the difficulty of soil-moisture estimation over the Qinghai-Tibet Plateau (QTP). To overcome these limitations, a soil-moisture retrieval method based on the Distributed Scatterers Adaptive Filter (DSAF) is proposed and applied in this paper. First, the statistically homogeneous pixels (SHP) with similar scatter behaviour are identified using the Anderson-Darling (AD) test method. Then, the Distributed Scatterers (DSs) are selected according to the SHPs threshold, and the backscattering coefficients of DSs are filtered using the SHPs distribution and coherences. In order to eliminate the negative influence of vegetation and roughness, the filtered backscattering coefficients are further processed by coupling the Water Cloud Model (WCM) and the images acquired in the winter season. The response of soil moisture to backscattering coefficients is greatly improved. Finally, the backscatter coefficients () are transformed into soil-moisture units by using the Least Square Regression (LSR) model and multi-temporal Change Detection (CD) method, respectively. The 15 scenes of TerraSAR-X images and 18 scenes of Sentinel-1 images in Northern Piedmont river, Northern Tibet, were used for experimental study. The results reveal that the response of the filtered backscattering coefficients to in-situ soil moisture is greatly improved. The soil-moisture content estimated by LSR and CD model using TerraSAR-X data is consistent with in-situ measurements, with coefficient of determination (R 2) of 0.52 and 0.56, Root Mean Square Error (RMSE) of 0.08 and 0.06, respectively. The results show that the proposed method can improve the accuracy of soil-moisture estimation, especially over the complex surface features of the QTP permafrost region.

An observation-driven optimization method for continuous estimation of soil moisture over large heterogeneous areas

ABSTRACTIn most remote sensing-based soil moisture (SM) retrieval methods, in-situ SM measurements are commonly used for validation purposes. Few studies have investigated whether such measurements can be used for calibration. In this paper, an observation-driven optimization method was proposed to estimate SM from remote sensing observations. Specifically, the optimization method was developed within the surface temperature-vegetation index (TVX) framework for the definition of objective function and constraints. In-situ SM measurements were used to optimize the theoretical boundaries of the TVX feature space. We demonstrated the applicability of the new method with Moderate Resolution Imaging Spectroradiometer (MODIS) products over the Southern Great Plains (SGP) of the United States of America. Results indicate that the accuracy produced using only one site for calibration has reached a level comparable with those produced by traditional methods. Moreover, the method has not only bypassed the complex parameterization of aerodynamic and surface resistance but also achieved continuous monitoring of SM. That is just the capacity that the traditional TVX method does not possess. Therefore, although our optimization method requires the ancillary of in-situ observations, its simplicity proves that it is a useful tool for a quick and continuous monitoring of SM over large heterogeneous areas.

GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with different soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data.

Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA

ABSTRACTA soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.

Parameter Optimization of a Discrete Scattering Model by Integration of Global Sensitivity Analysis Using SMAP Active and Passive Observations

Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LA!) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters are defined as free variables, to separately calibrate the activeonly model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP). The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations. The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 0.87, 0.89, 0.78, and 0.43 and root-mean-square errors of 0.49 dB, 0.52 dB, 7.20 K, and 10.47 K for σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HH</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> , σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VV</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> , T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BH</sub> , and T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BV</sub> , respectively. In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperature, respectively. Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data. These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model. In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization. It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data.

Combining of the H/A/Alpha and Freeman–Durden Polarization Decomposition Methods for Soil Moisture Retrieval from Full-Polarization Radarsat-2 Data

Soil moisture (SM) plays important roles in surface energy conversion, crop growth, environmental protection, and drought monitoring. As crops grow, the associated vegetation seriously affects the ability of satellites to retrieve SM data. Here, we collected such data at different growth stages of maize using Bragg and X-Bragg scattering models based on the Freeman–Durden polarization decomposition method. We used the H/A/Alpha polarization decomposition approach to extract accurate threshold values of decomposed scattering components. The results showed that the H and Alpha values of bare soil areas were lower and those of vegetated areas were higher. The threshold values of the three scattering components were 0.2–0.4 H and 7–24° Alpha for the surface scattering component, 0.6–0.9 H and 22–50° Alpha for the volume scattering component, and other values for the dihedral scattering component. The SM data retrieved (using the X-Bragg model) on June 27, 2014, were better than those retrieved at other maize growth stages and were thus associated with the minimum root-mean-square error value (0.028). The satellite-evaluated SM contents were in broad agreement with data measured in situ. Our algorithm thus improves the accuracy of SM data retrieval from synthetic-aperture radar (SAR) images.

SOIL MOISTURE RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS: APPLICATION TO PASSIVE MICROWAVE REMOTE SENSING

Abstract. A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.