Generalization of multiple depths soil temperature estimation using LSTM and CNN

作者：Nasrin Azad, Hailong He · 发表于：Journal of Hydrology · 年份：2025 · DOI：10.1016/j.jhydrol.2025.133687 · 被引用次数：8 · 研究领域：Soil Moisture and Remote Sensing、Landslides and related hazards、Soil and Unsaturated Flow

Accurate estimation of soil temperature (ST) as a key factor in soil physical, hydrological, chemical, and biological processes is essential in agriculture and Earth system science. Different machine learning methods have been used in previous studies in ST estimation and have gained remarkable progress. However, such studies are not only rare due to the scarce multi-depth ST data, but also the used modeling approaches are prone to modeling errors nor generalization, especially at deeper depths. The main purpose of this study was to assess generalizability of trained deep learning model in multi-depth ST estimation in different land covers, climates and soils. In this regard, this study aimed to improve the accuracy of multiple depths ST estimation in multi-feature-temporal modeling of LSTM (Long Short-Term Memory) and variable-based modeling of CNN (Convolutional Neural Network). Applicability of trained DL model in multiple depths ST time series estimation of other regions with same condition was evaluated to assess its generalization potential. Daily time series of meteorological and soil data at depths of 5, 10, 20, 50 and 100 cm were gathered from 14 stations of U.S. Climate Reference Network with various land covers, climates and soils. The results indicated that estimation of ST with time series of meteorological parameters + ST (of each soil depth or upper depth) performed best with <5 % error in almost all regions and soil depths. Evaluation of the generalization pot...