Machine learning approaches for soil moisture prediction: enhancing agricultural water management with integrated data
Abstract
Effective soil moisture prediction is essential for precision agriculture, enabling optimised irrigation scheduling and sustainable water management. This study investigates the predictive performance of various machine learning (ML) models for soil moisture at two depths (0–30 cm and 0–100 cm) using integrated datasets of climate variables, soil properties, and leaf area index (LAI). We tested models including Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Gaussian Process Regression (GPR), alongside traditional models such as Partial Least Squares (PLS) and Multiple Linear Regression (MLR). The RF and XGBoost models provided the highest accuracy among all the models tested, with the RF model yielding an NSE of 0.81 and R2 of 0.84 at 0–30 cm depth. Notably, RF seemed to perform reasonably well even when forced to rely solely on climate data inputs, with NSE values above 0.75, a performance supporting the robustness of the models in data-scarce conditions. The study further shows that ensemble ML models are better at capturing nonlinear interactions among soil, climate, and crop factors compared to traditional models, by up to 25% improvement in NSE. Soil depth was found to be one of the main factors that influence model performance. The highest prediction accuracy was recorded at shallower depths, which points toward its sensitivity to the dynamics of moisture content within the root zone. Similarly, the feature importance analysis has identified LAI and organic matter in the soil as critical predictors. These findings underline the potential of the use of ensemble ML models in operational soil moisture monitoring in agriculture as a scalable approach to improved water-use efficiency with sustained variable climate conditions.
Document Type
Journal Article
Date of Publication
1-1-2026
Volume
12
Issue
1
Publication Title
Modeling Earth Systems and Environment
Publisher
Springer
School
School of Science
RAS ID
84610
Funders
University of Southern Queensland / Queensland Department of Primary Industries
Copyright
subscription content
Comments
Ali, A. (2025). Machine learning approaches for soil moisture prediction: enhancing agricultural water management with integrated data. Modeling Earth Systems and Environment, 12. https://doi.org/10.1007/s40808-025-02607-5