68 citations as recorded by crossref.

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25. Developing pedotransfer functions using Sentinel-2 satellite spectral indices and Machine learning for estimating the surface soil moisture A. Sedaghat et al. 10.1016/j.jhydrol.2021.127423
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29. Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil M. Rezaei et al. 10.1016/j.compag.2023.107821
30. Development and functional evaluation of pedotransfer functions for soil hydraulic properties for the Zambezi River Basin M. Kalumba et al. 10.1111/ejss.13077
31. Indirect Prediction of Salt Affected Soil Indicator Properties through Habitat Types of a Natural Saline Grassland Using Unmanned Aerial Vehicle Imagery L. Pásztor et al. 10.3390/land12081516
32. Geospatial monitoring and analysis of agricultural drought to identify hotspots and risk assessment for Senegal G. Singh et al. 10.1016/j.geosus.2024.10.004
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37. The Effects of Soil Representation in WRF–CLM on the Atmospheric Moisture Budget E. Dennis & E. Berbery 10.1175/JHM-D-21-0101.1
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40. Pedotransfer Function for the Brunswick Soil Hydraulic Property Model and Comparison to the van Genuchten‐Mualem Model T. Weber et al. 10.1029/2019WR026820
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42. Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques A. Nouraki et al. 10.1016/j.rsase.2024.101354
43. Hand-feel soil texture classes and particle-size distribution as predictors of soil water content at field capacity. Further insights into the sources of uncertainty A. Richer-de-Forges et al. 10.1016/j.catena.2024.108268
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48. Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs N. Fodor et al. 10.1080/17538947.2021.1953161
49. Characterization and prediction of soil hydraulic and solute transport parameters using a random forest model X. Ju et al. 10.1111/ejss.13471
50. Modelling of soil depth and hydraulic properties at regional level using environmental covariates- A case study in India S. Dharumarajan et al. 10.1016/j.geodrs.2021.e00439
51. Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques S. Araya et al. 10.5194/hess-25-2739-2021
52. 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
53. 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
54. 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
55. 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
56. Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset S. Mallah et al. 10.3390/agronomy12112613
57. Dominant factors determining the hydraulic conductivity of sedimentary aquitards: A random forest approach M. van Leer et al. 10.1016/j.jhydrol.2023.130468
58. Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2) B. Szabó et al. 10.5194/gmd-14-151-2021
59. Accounting for the spatial range of soil properties in pedotransfer functions S. Wang et al. 10.1016/j.geoderma.2023.116411
60. Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin M. Kalumba et al. 10.3390/land11040591
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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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