67 citations as recorded by crossref.

1. Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database M. Turek et al. 10.1016/j.iswcr.2022.08.001
2. Pedotransfer functions and machine learning: Advancements and challenges in tropical soils L. de Castro Moreira da Silva et al. 10.1016/j.geodrs.2023.e00720
3. Dynamic monitoring of aboveground biomass in inner Mongolia grasslands over the past 23 Years using GEE and analysis of its driving forces D. Yang et al. 10.1016/j.jenvman.2024.120415
4. Predicting spatial variability of selected soil properties using digital soil mapping in a rainfed vineyard of central Chile L. Mashalaba et al. 10.1016/j.geodrs.2020.e00289
5. Evaluation of pedotransfer functions for predicting soil hydraulic properties: A voyage from regional to field scales across Europe P. Nasta et al. 10.1016/j.ejrh.2021.100903
6. Spatiotemporal dynamics of grassland aboveground biomass and its driving factors in North China over the past 20 years J. Ge et al. 10.1016/j.scitotenv.2022.154226
7. Field-scale digital mapping of top- and subsoil Chernozem properties A. Suleymanov et al. 10.1007/s11119-024-10128-x
8. Enhancing the prediction of hydraulic parameters using machine learning, integrating multiple attributes of GIS and geophysics P. Gupta & S. Maiti 10.1007/s10040-022-02567-5
9. Towards a cost-effective framework for estimating soil nitrogen pools using pedotransfer functions and machine learning L. Laurence et al. 10.1016/j.geoderma.2023.116692
10. Visualizing preferential flow paths using dye tracer and species diversity theory methods to explore their correlation to soil properties with random forest algorithm Y. Zhang et al. 10.1016/j.jhydrol.2024.131570
11. Machine learning-based soil aggregation assessment under four scenarios in northwestern Iran P. Nazeri et al. 10.31545/intagr/188506
12. Can pedotransfer functions based on environmental variables improve soil total nutrient mapping at a regional scale? X. Song et al. 10.1016/j.still.2020.104672
13. Assessing the Reliability of Pedotransfer Functions for Predicting Water Retention Variability in Tropical Soils: Implications for Environmental Process Modeling E. Wimalasiri et al. 10.1134/S1064229324601513
14. Estimation of important points on soil water retention curve (SWRC): comparison experimental-physical models and data mining technique A. Tafteh et al. 10.1007/s12517-022-10232-0
15. Application GIS and remote sensing for soil organic carbon mapping in a farm-scale in the hilly area of central Vietnam C. Van Huynh et al. 10.1177/11786221221114777
16. Evaluating ensemble learning in developing pedotransfer functions to predict soil hydraulic properties X. Li et al. 10.1016/j.jhydrol.2024.131658
17. Countrywide mapping and assessment of organic carbon saturation in the topsoil using machine learning-based pedotransfer function with uncertainty propagation G. Szatmári et al. 10.1016/j.catena.2023.107086
18. Synergistic estimation of soil salinity based on Sentinel-1 image texture and Sentinel-2 salinity spectral indices H. Yin et al. 10.1117/1.JRS.17.018502
19. Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard A. Laborczi et al. 10.3390/ijgi9040268
20. Integrated assessment of potentially toxic elements in soil of the Kangdian metallogenic province: A two-point machine learning approach W. Yang et al. 10.1016/j.ecoenv.2024.116248
21. An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources Z. Su et al. 10.3390/w12051495
22. Digital soil mapping of key secondary soil properties using pedotransfer functions and Indian legacy soil data N. Reddy & B. Das 10.1016/j.geoderma.2022.116265
23. Modelling soil water retention and water‐holding capacity with visible–near‐infrared spectra and machine learning P. Baumann et al. 10.1111/ejss.13220
24. SoilKsatDB: global database of soil saturated hydraulic conductivity measurements for geoscience applications S. Gupta et al. 10.5194/essd-13-1593-2021
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
26. Radar remote sensing-based inversion model of soil salt content at different depths under vegetation Y. Chen et al. 10.7717/peerj.13306
27. Spatial mapping of hydrologic soil groups using machine learning in the Mediterranean region E. Faouzi et al. 10.1016/j.catena.2023.107364
28. 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
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. The spatial dependence of base saturation on forest soil grain and chemical composition seen through individual and typological divisions P. Samec et al. 10.37040/geografie.2023.019
33. Toward Developing a Generalizable Pedotransfer Function for Saturated Hydraulic Conductivity Using Transfer Learning and Predictor Selector Algorithm S. Jena et al. 10.1029/2020WR028862
34. Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations S. Araya & T. Ghezzehei 10.1029/2018WR024357
35. Building pedotransfer functions for estimating soil erodibility in southeastern China X. Zhu et al. 10.1016/j.ecolind.2022.109720
36. The Effects of Soil Representation in WRF–CLM on the Atmospheric Moisture Budget E. Dennis & E. Berbery 10.1175/JHM-D-21-0101.1
37. The effect of future land use changes on hydrologic ecosystem services: a case study from the Zala catchment, Hungary B. Decsi et al. 10.1007/s42977-020-00032-6
38. Progress in the elaboration of GSM conform DSM products and their functional utilization in Hungary L. Pásztor et al. 10.1016/j.geodrs.2020.e00269
39. Pedotransfer Function for the Brunswick Soil Hydraulic Property Model and Comparison to the van Genuchten‐Mualem Model T. Weber et al. 10.1029/2019WR026820
40. Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale M. Ghavami et al. 10.1007/s11629-023-8056-z
41. 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
42. 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
43. Estimating the soil water retention curve: Comparison of multiple nonlinear regression approach and random forest data mining technique M. Rastgou et al. 10.1016/j.compag.2020.105502
44. Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative G. Szatmári et al. 10.3390/rs12244073
45. Available water capacity from a multidisciplinary and multiscale viewpoint. A review I. Cousin et al. 10.1007/s13593-022-00774-8
46. Satellite-Based Estimation of Soil Moisture Content in Croplands: A Case Study in Golestan Province, North of Iran S. Bandak et al. 10.3390/rs15082155
47. Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs N. Fodor et al. 10.1080/17538947.2021.1953161
48. Characterization and prediction of soil hydraulic and solute transport parameters using a random forest model X. Ju et al. 10.1111/ejss.13471
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset S. Mallah et al. 10.3390/agronomy12112613
56. Dominant factors determining the hydraulic conductivity of sedimentary aquitards: A random forest approach M. van Leer et al. 10.1016/j.jhydrol.2023.130468
57. Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2) B. Szabó et al. 10.5194/gmd-14-151-2021
58. Accounting for the spatial range of soil properties in pedotransfer functions S. Wang et al. 10.1016/j.geoderma.2023.116411
59. Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin M. Kalumba et al. 10.3390/land11040591
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model L. Zhang et al. 10.3390/rs13234893
67. From EU-SoilHydroGrids to HU-SoilHydroGrids: A leap forward in soil hydraulic mapping B. Szabó et al. 10.1016/j.scitotenv.2024.171258