Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-1325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating soil moisture conditions for drought monitoring with random forests and a simple soil moisture accounting scheme
HSM, University of Montpellier, CNRS, IRD, IMT, Montpellier, France
Pere Quintana Seguí
Observatori de l'Ebre (OE), Ramon Llull University, CSIC, 43520
Roquetes, Spain
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Cited
20 citations as recorded by crossref.
- Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data M. Dabboor et al.
- Toward a Redefinition of Agricultural Drought Periods—A Case Study in a Mediterranean Semi-Arid Region K. Oukaddour et al.
- Advances in remote sensing based soil moisture retrieval: applications, techniques, scales and challenges for combining machine learning and physical models A. Abbes et al.
- High-resolution soil moisture mapping in northern boreal forests using SMAP data and downscaling techniques E. Jääskeläinen et al.
- Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands S. Wang & G. Fu
- Response Analysis of Terrestrial Water Storage Components to Drought Based on Random Forests During 2011–2020 in Yunnan, China Z. Zhu et al.
- Surface Soil Moisture Retrieval over Winter Wheat Fields Based on Fused Multispectral and L-Band MiniSAR Data Z. Luo et al.
- Effects of auxiliary and ancillary data on LULC classification in a heterogeneous environment using optimized random forest algorithm T. Kavzoglu & F. Bilucan
- Soil moisture measurements: a review E. Eishoeei et al.
- Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya A. Abebe et al.
- Machine learning approaches for soil moisture prediction: enhancing agricultural water management with integrated data A. Ali
- Evaluating Performance of Multiple Machine Learning Models for Drought Monitoring: A Case Study of Typical Grassland in Inner Mongolia Y. Wang et al.
- Soil water content prediction across seasons using random forest based on precipitation-related data P. Chen et al.
- Quality control and improvement of GNSS-IR soil moisture robust inversion model Y. Li et al.
- Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress J. Hernández-López et al.
- High-Resolution Quantitative Retrieval of Soil Moisture Based on Multisource Data Fusion with Random Forests: A Case Study in the Zoige Region of the Tibetan Plateau Y. Ma et al.
- Classification and Regression Tree (CART)-based estimation of soil water content based on meteorological inputs and explorations of hydrodynamics behind T. Wu et al.
- Land Use–Future Climate Coupling Mechanism Analysis of Regional Agricultural Drought Spatiotemporal Patterns J. Wang et al.
- Predictive drivers and transferability of multi-scale machine learning based crop yield prediction under drought across European and Asian climates H. Sahu et al.
- Prediction of soil moisture via feature selection, model optimization, and climate data integration O. Katipoğlu et al.
20 citations as recorded by crossref.
- Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data M. Dabboor et al.
- Toward a Redefinition of Agricultural Drought Periods—A Case Study in a Mediterranean Semi-Arid Region K. Oukaddour et al.
- Advances in remote sensing based soil moisture retrieval: applications, techniques, scales and challenges for combining machine learning and physical models A. Abbes et al.
- High-resolution soil moisture mapping in northern boreal forests using SMAP data and downscaling techniques E. Jääskeläinen et al.
- Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands S. Wang & G. Fu
- Response Analysis of Terrestrial Water Storage Components to Drought Based on Random Forests During 2011–2020 in Yunnan, China Z. Zhu et al.
- Surface Soil Moisture Retrieval over Winter Wheat Fields Based on Fused Multispectral and L-Band MiniSAR Data Z. Luo et al.
- Effects of auxiliary and ancillary data on LULC classification in a heterogeneous environment using optimized random forest algorithm T. Kavzoglu & F. Bilucan
- Soil moisture measurements: a review E. Eishoeei et al.
- Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya A. Abebe et al.
- Machine learning approaches for soil moisture prediction: enhancing agricultural water management with integrated data A. Ali
- Evaluating Performance of Multiple Machine Learning Models for Drought Monitoring: A Case Study of Typical Grassland in Inner Mongolia Y. Wang et al.
- Soil water content prediction across seasons using random forest based on precipitation-related data P. Chen et al.
- Quality control and improvement of GNSS-IR soil moisture robust inversion model Y. Li et al.
- Agricultural drought assessment in dry zones of Tolima, Colombia, using an approach based on water balance and vegetation water stress J. Hernández-López et al.
- High-Resolution Quantitative Retrieval of Soil Moisture Based on Multisource Data Fusion with Random Forests: A Case Study in the Zoige Region of the Tibetan Plateau Y. Ma et al.
- Classification and Regression Tree (CART)-based estimation of soil water content based on meteorological inputs and explorations of hydrodynamics behind T. Wu et al.
- Land Use–Future Climate Coupling Mechanism Analysis of Regional Agricultural Drought Spatiotemporal Patterns J. Wang et al.
- Predictive drivers and transferability of multi-scale machine learning based crop yield prediction under drought across European and Asian climates H. Sahu et al.
- Prediction of soil moisture via feature selection, model optimization, and climate data integration O. Katipoğlu et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
Short summary
Monitoring soil moisture is important during droughts, but very few measurements are available. Consequently, land-surface models are essential tools for reproducing soil moisture dynamics. In this study, a hybrid approach allowed for regionalizing soil water content using a machine learning method. This approach proved to be efficient, compared to the use of soil property maps, to run a simple soil moisture accounting model, and therefore it can be applied in various regions.
Monitoring soil moisture is important during droughts, but very few measurements are available....
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