High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau

• Published in: Geo-spatial information science Vol. 28; no. 2; pp. 589 - 608
• Main Authors: Li, Zhenghao, Yuan, Qiangqiang, Su, Xin
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 04.03.2025; Taylor & Francis Group
• Subjects: cloud-based approach; Deep Forest model; high spatial resolution; Surface soil moisture; Tibetan Plateau
• ISSN: 1009-5020; 1993-5153
• DOI: 10.1080/10095020.2024.2307931

As a key climate variable, soil moisture plays a crucial role in drought detection, flood warning, and crop yield prediction. In recent years, the demand for high-spatial-resolution soil moisture has increased, particularly in environmental management. In this study, Copernicus Sentinel-1 synthetic aperture radar data, Sentinel-2 multi-spectral data, and other auxiliary data (land cover types, soil texture, etc.) were used to retrieve surface soil moisture (10 m) in the cloud environment (Google Earth Engine + Google Colab + Google Drive) over the Tibetan Plateau, and an entirely data-driven machine learning-based model called Deep Forest was adopted. We discussed the application of the Deep Forest model and compared it with other machine learning models. Overall, on the basis of 10-fold cross-validation, the modified Deep Forest model performed the best, with estimate accuracy of 0.834 and 0.038 m 3 ·m −3 in terms of coefficient of determination ( $R^2$ R 2 ) and unbiased Root Mean Square Error (ubRMSE), respectively. It also demonstrated the best performance in site-based validation ( $R^2$ R 2 of 0.606 and ubRMSE of 0.092 m 3 ·m −3 ). In addition, the framework for the data acquisition, data preprocessing, model training, and soil moisture mapping in this study was completed in the cloud environment, which facilitated the entire retrieval process. This work provides new ideas beyond the retrieval model for other related studies.