Spatiotemporal prediction of soil moisture content at various depths in three soil types using machine learning algorithms

IntroductionAccurate prediction of soil moisture content (SMC) is crucial for agricultural systems as it affects hydrological cycles, crop growth, and resource management. Considering the challenges with prediction accuracy and determining the effect of soil texture, depth, and meteorological data o...

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Библиографические подробности

Главные авторы:	Tarek Alahmad, Miklós Neményi, Adrienn Széles, Nour Ali, Omar Hijazi, Anikó Nyéki
Формат:	Artigo
Язык:	Inglês
Опубликовано:	Frontiers Media S.A. 2025-09-01
Серии:	Frontiers in Soil Science
Предметы:	machine learning soil moisture content RFR LSTM XGBoost spatio-temporal prediction
Online-ссылка:	https://www.frontiersin.org/articles/10.3389/fsoil.2025.1612908/full
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