Explainable XGBoost-based models of root-zone soil moisture profiling using coupled Sentinel-2 and IoT data in loam and silt loam soil

Abstract Background Accurate prediction of soil moisture content (SMC) is crucial for sustainable irrigation management and enhancing resilience against climate change. However, in-situ sensing offers accurate point-scale measurements but lacks spatial representativeness, while satellite-based offer...

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Principais autores:	Tarek Alahmad, Miklós Neményi, Anikó Nyéki
Formato:	Artigo
Idioma:	Inglês
Publicado:	Springer 2026-04-01
Series:	Discover Applied Sciences
Assuntos:	Root-zone soil moisture Depth-specific modeling Multi-source data fusion SHAP XGBoost Decision support
Acceso en liña:	https://doi.org/10.1007/s42452-026-08673-3
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