Raza et al. (2025) Corrigendum to “Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models” [Int. J. Appl. Earth Obs. and Geoinf. 144C (2025) 104918]

Identification

• Journal: International Journal of Applied Earth Observation and Geoinformation
• Year: 2025
• Date: 2025-12-08
• Authors: Ahsan Raza, Masahiro Ryo, Gohar Ghazaryan, Roland Baatz, Magdalena Main‐Knorn, Leonardo Inforsato, Claas Nendel
• DOI: 10.1016/j.jag.2025.105001

Research Groups

• Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
• Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
• Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany
• Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany
• Global Change Research Institute CAS, Brno, Czech Republic

Short Summary

This document is a corrigendum correcting an error in the legends of Fig. 1(c) in the original paper titled "Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models".

Objective

• To predict regional-scale groundwater levels at high spatial resolution using spatial Random Forest models (objective of the original paper).

Study Configuration

• Spatial Scale: Regional-scale
• Temporal Scale: Not available in the corrigendum.

Methodology and Data

• Models used: Spatial Random Forest models (from original paper title).
• Data sources: Sentinel-2 Land Cover Explorer (2023) for major land use/land cover classes; irrigated land data from Ghazaryan et al. (2024). Other primary data sources for the groundwater model are not detailed in the corrigendum.

Main Results

• Not available in the corrigendum.

Contributions

• Not available in the corrigendum.

Funding

• Not available in the corrigendum.

Citation

@article{Raza2025Corrigendum,
  author = {Raza, Ahsan and Ryo, Masahiro and Ghazaryan, Gohar and Baatz, Roland and Main‐Knorn, Magdalena and Inforsato, Leonardo and Nendel, Claas},
  title = {Corrigendum to “Predicting regional-scale groundwater levels at high spatial resolution using spatial Random Forest models” [Int. J. Appl. Earth Obs. and Geoinf. 144C (2025) 104918]},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  year = {2025},
  doi = {10.1016/j.jag.2025.105001},
  url = {https://doi.org/10.1016/j.jag.2025.105001}
}