Surface Soil Moisture Retrieval of China Using Multi-Source Data and Ensemble Learning

作者：Zhangjian Yang, Qisheng He, Shuqi Miao, Wei Feng, Mingxiao Yu · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15112786 · 被引用次数：31 · 研究领域：Soil Moisture and Remote Sensing、Climate change and permafrost、Precipitation Measurement and Analysis

Large-scale surface soil moisture (SSM) distribution is very necessary for agricultural drought monitoring, water resource management, and climate change research. However, the current large-scale SSM products have relatively coarse spatial resolution, which limits their application. In this study, we estimate the 1 km daily SSM in China based on ensemble learning using a multi-source data set including in situ soil moisture measurements from 2980 meteorological stations, MODIS Surface Reflectance products, SMAP (Soil Moisture Active Passive) soil moisture products, ERA5-Land dataset, SRTM DEM and soil texture. Among them, in situ measurements are used as independent variables, and other data are used as dependent variables. In order to improve the spatio-temporal completeness of SSM, the missing value in SMAP soil moisture products were reconstructed using the Discrete Cosine Transformation-penalized Partial Least Square (DCT-PLS) method to provide spatially complete background field information for soil moisture retrieval. The results show that the reconstructed soil moisture value has high quality, and the DCT-PLS method can fully utilize the three-dimensional spatiotemporal information to fill the data gaps. Subsequently, the performance of four ensemble learning models of random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) for soil moisture retrieval was evaluated. The LightGBM outperfo...