An Interpretable Machine Learning Framework for Unraveling the Dynamics of Surface Soil Moisture Drivers

An Interpretable Machine Learning Framework for Unraveling the Dynamics of Surface Soil Moisture Drivers

Understanding the impacts of the spatial non-stationarity of environmental factors on surface soil moisture (SSM) in different seasons is crucial for effective environmental management. Yet, our knowledge of this phenomenon remains limited. This study introduces an interpretable machine learning fra...

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Bibliographic Details
Main Authors:	Zahir Nikraftar, Esmaeel Parizi, Mohsen Saber, Mahboubeh Boueshagh, Mortaza Tavakoli, Abazar Esmaeili Mahmoudabadi, Mohammad Hassan Ekradi, Rendani Mbuvha, Seiyed Mossa Hosseini
Format:	Article
Language:	English
Published:	MDPI AG 2025-07-01
Series:	Remote Sensing
Subjects:	surface soil moisture dynamics machine learning random forest SHapley additive exPlanations environmental factors
Online Access:	https://www.mdpi.com/2072-4292/17/14/2505
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