Work

Satellite-Based Machine Learning for Soil Moisture Prediction and Land Conservation Practice Assessment in West African Drylands

Year: 2025

Type: article

Abstract: In semiarid, fragmented landscapes where data scarcity challenges effective land management, accurate soil moisture monitoring is critical. This study presents a high-resolution analysis that integrates remote sensing, in situ data, and machine learning to predict soil moisture and evaluate the impact of land conservation practices. A Long Short-Term Memory (LSTM) model combined with Random Forest gap-filling achieved strong predictive performance (R2 = 0.84; RMSE = 0.103 cm3 cm−3), outperforming SMAP satellite estimates by approximately 30% across key accuracy metrics. The model was applied to 222 field sites in northern Ghana to quantify the effects of stone bunds on soil moisture retention. The results revealed that fields with stone bunds maintained 4–6% higher moisture than non-bunded fields, particularly on steep slopes and in areas with low to moderate topographic wetness. These findings demonstrate the capability of combining remote sensing and deep learning for fine-scale soil-moisture prediction and provide quantitative evidence of how nature-based solutions enhance water retention and climate resilience in dryland agricultural systems.

Source: Remote Sensing

DOI: 10.3390/rs17213651

Authors: Meron Lakew Tefera, Ethiopia B. Zeleke, Mario Pirastru, Assefa M. Melesse, Giovanna Seddaiu, Hassan Awada

Institutions: University of Sassari, Florida International University

Language: English

Topic: Soil Moisture and Remote Sensing

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Zero hunger, Climate action

Open Access status: gold

Estimated APC paid: $3,258