69 citations as recorded by crossref.

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29. Pedotransfer functions development for modeling FC and PWP using Vis-NIR spectra combined with PLSR and regression models X. Zhu et al. 10.1016/j.vibspec.2024.103731
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31. Development and functional evaluation of pedotransfer functions for soil hydraulic properties for the Zambezi River Basin M. Kalumba et al. 10.1111/ejss.13077
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37. Building pedotransfer functions for estimating soil erodibility in southeastern China X. Zhu et al. 10.1016/j.ecolind.2022.109720
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53. Comparison of Four Methods for Vertical Extrapolation of Soil Moisture Contents from Surface to Deep Layers in an Alpine Area J. Li & L. Zhang 10.3390/su13168862
54. Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture A. Nanda et al. 10.1016/j.agwat.2024.108918
55. A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware river basin, USA P. Goodling et al. 10.1016/j.envsoft.2024.106124
56. The important role of reliable land surface model simulation in high-resolution multi-source soil moisture data fusion by machine learning J. Zeng et al. 10.1016/j.jhydrol.2024.130700
57. Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset S. Mallah et al. 10.3390/agronomy12112613
58. Dominant factors determining the hydraulic conductivity of sedimentary aquitards: A random forest approach M. van Leer et al. 10.1016/j.jhydrol.2023.130468
59. Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2) B. Szabó et al. 10.5194/gmd-14-151-2021
60. Accounting for the spatial range of soil properties in pedotransfer functions S. Wang et al. 10.1016/j.geoderma.2023.116411
61. Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin M. Kalumba et al. 10.3390/land11040591
62. Comparing the prediction performance, uncertainty quantification and extrapolation potential of regression kriging and random forest while accounting for soil measurement errors B. Takoutsing & G. Heuvelink 10.1016/j.geoderma.2022.116192
63. Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North U. Acharya et al. 10.3390/soilsystems5040057
64. Building inexpensive topsoil saturated hydraulic conductivity maps for land planning based on machine learning and geostatistics H. Aguilera et al. 10.1016/j.catena.2021.105788
65. Modeling the Spatial Distribution of Soil Organic Carbon and Carbon Stocks in the Casanare Flooded Savannas of the Colombian Llanos J. Martín-López et al. 10.1007/s13157-023-01705-3
66. Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks M. Rehman et al. 10.1016/j.heliyon.2024.e28854
67. Comparison of spectral and spatial-based approaches for mapping the local variation of soil moisture in a semi-arid mountainous area S. Fathololoumi et al. 10.1016/j.scitotenv.2020.138319
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