A stand-alone framework for predicting spatiotemporal errors in satellite-based soil moisture using tree-based models and deep neural networks

Abstract

Soil moisture (SM) has been recognized as one of the essential climate variables in Earth science. Incorporating spatiotemporal information from satellite SM observations into land surface models via land data assimilation (LDA) has emerged as a promising method to improve continuous SM modeling and climate extreme monitoring. Since the reliable satellite SM error dynamics are crucial for successful LDA applications but often assumed to remain static over time, this study presents a novel framework that combines triple collocation analysis (TCA) with advanced machine learning techniques, such as Light Gradient Boosting Machine (LGBM) and Deep Neural Networks (DNN), to accurately quantify spatially and temporally continuous satellite-based SM characteristics on a global scale. In this study, the stand-alone TCA-based time-variant SM error prediction models, which rely exclusively on data used for the Soil Moisture Active Passive (SMAP) retrieval, were developed and comprehensively evaluated. These models successfully recover error information over areas where SMAP SM error data were previously unusable due to uncertain error characteristics. In addition, the stand-alone SMAP-based model demonstrates superior performance compared to model that relies on external datasets, such as the Global Land Data Assimilation System (GLDAS). These findings provide valuable insights into the dynamic nature of satellite-based SM error under various environmental conditions and present a novel way to improve LDA. Furthermore, the proposed methodology can be extended to predict error dynamics for other satellite-based geophysical datasets, broadening its potential applications beyond SMAP. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.