Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks

Abstract

As an important component of the earth ecosystem, soil moisture monitoring is of great significance in the fields of crop growth monitoring, crop yield estimation, variable irrigation, and other related applications. In order to mitigate or eliminate the impacts of sparse vegetation covers in farmland areas, this study combines multi-source remote sensing data from Sentinel-1 radar and Sentinel-2 optical satellites to quantitatively retrieve soil moisture content. Firstly, a traditional Oh model was applied to estimate soil moisture content after removing vegetation influence by a water cloud model. Secondly, support vector regression (SVR) and generalized regression neural network (GRNN) models were used to establish the relationships between various remote sensing features and real soil moisture. Finally, a regression convolutional neural network (CNNR) model is constructed to extract deep-level features of remote sensing data to increase soil moisture retrieval accuracy. In addition, polarimetric decomposition features for real Sentinel-1 PolSAR data are also included in the construction of inversion models. Based on the established soil moisture retrieval models, this study analyzes the influence of each input feature on the inversion accuracy in detail. The experimental results show that the optimal combination of R2 and root mean square error (RMSE) for SVR is 0.7619 and 0.0257 cm3/cm3, respectively. The optimal combination of R2 and RMSE for GRNN is 0.7098 and 0.0264 cm3/cm3, respectively. Especially, the CNNR model with optimal feature combination can generate inversion results with the highest accuracy, whose R2 and RMSE reach up to 0.8947 and 0.0208 cm3/cm3, respectively. Compared to other methods, the proposed algorithm improves the accuracy of soil moisture retrieval from synthetic aperture radar (SAR) and optical data. Furthermore, after adding polarization decomposition features, the R2 of CNNR is raised by 0.1524 and the RMSE of CNNR decreased by 0.0019 cm3/cm3 on average, which means that the addition of polarimetric decomposition features effectively improves the accuracy of soil moisture retrieval results.

Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China

“…In recent decades, experiments have demonstrated that CNN-based deep learning methods are reliable for predicting SM using multisource data [46][47][48]63]. Recently, CNNs have been used successfully for SM estimations based on Sentinel multisource data [49].…”

“…CNNs were originally designed to resolve image classification problems and they have been applied effectively for remote-sensing-based image classification [44][45][46][47][48]. Recently, CNNs have been successfully used for soil moisture estimations based on Sentinel multi-source data [49]. Integrating multiple machine learning methods for rainfall retrieving gains rare attention.…”

Surface Soil Moisture Estimation Using a Neural Network Model in Bare Land and Vegetated Areas

“…Some scholars have constructed an NN model based on microwave data for estimating soil moisture [36,37]. Tere is also a synergistic use of microwave and optical data to retrieve soil moisture using NN models [38,39]. In these studies, NN models estimated soil moisture with a high degree of accuracy.…”

Enhanced Generalized Regression Neural Network for Soil Moisture Estimation Over the Qinghai-Tibet Plateau

“…Finally, using multisource remote sensing data as the input. For example, Liu et al [33] used polarimetric decomposition features and vegetation indexes as the input of GRNN for SM estimation. Zhang et al [31] evaluated the importance of twenty-nine different features to SM estimation using GRNN.…”