13 citations as recorded by crossref.

1. Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data M. Dabboor et al. 10.3390/rs15071916
2. Toward a Redefinition of Agricultural Drought Periods—A Case Study in a Mediterranean Semi-Arid Region K. Oukaddour et al. 10.3390/rs16010083
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4. Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands S. Wang & G. Fu 10.3389/fenvs.2023.1130448
5. Response Analysis of Terrestrial Water Storage Components to Drought Based on Random Forests During 2011–2020 in Yunnan, China Z. Zhu et al. 10.1109/JSTARS.2024.3507853
6. Effects of auxiliary and ancillary data on LULC classification in a heterogeneous environment using optimized random forest algorithm T. Kavzoglu & F. Bilucan 10.1007/s12145-022-00874-9
7. Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya A. Abebe et al. 10.3390/rs17101763
8. Evaluating Performance of Multiple Machine Learning Models for Drought Monitoring: A Case Study of Typical Grassland in Inner Mongolia Y. Wang et al. 10.3390/land13060754
9. Soil water content prediction across seasons using random forest based on precipitation-related data P. Chen et al. 10.1016/j.compag.2024.109802
10. Quality control and improvement of GNSS-IR soil moisture robust inversion model Y. Li et al. 10.1016/j.asr.2024.07.069
11. 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. 10.1016/j.scitotenv.2024.171144
12. 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. 10.3390/rs15061531
13. Classification and Regression Tree (CART)-based estimation of soil water content based on meteorological inputs and explorations of hydrodynamics behind T. Wu et al. 10.1016/j.agwat.2024.108869