Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models

Groundwater is an important resource for sustainable crop growth and agricultural productivity, and groundwater level (GWL) fluctuations directly influence agroecosystems. However, GWL data for most regions is unavailable because of spatial scarcity and discontinuous groundwater observations. In rec...

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Bibliographic Details

Published in:	International Journal of Applied Earth Observations and Geoinformation
Main Authors:	Ahsan Raza, Masahiro Ryo, Gohar Ghazaryan, Roland Baatz, Magdalena Main-Knorn, Leonardo Inforsato, Claas Nendel
Format:	Article
Language:	English
Published:	Elsevier 2025-11-01
Subjects:	Machine learning Spatial interpolation Data integration Random forest for spatial interpolation Groundwater level
Online Access:	http://www.sciencedirect.com/science/article/pii/S1569843225005655