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

Abstract

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.