77 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.
2. Pedotransfer functions and machine learning: Advancements and challenges in tropical soils L. de Castro Moreira da Silva et al.
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.
4. Predicting spatial variability of selected soil properties using digital soil mapping in a rainfed vineyard of central Chile L. Mashalaba et al.
5. Evaluation of pedotransfer functions for predicting soil hydraulic properties: A voyage from regional to field scales across Europe P. Nasta et al.
6. Spatiotemporal dynamics of grassland aboveground biomass and its driving factors in North China over the past 20 years J. Ge et al.
7. Field-scale digital mapping of top- and subsoil Chernozem properties A. Suleymanov et al.
8. Enhancing the prediction of hydraulic parameters using machine learning, integrating multiple attributes of GIS and geophysics P. Gupta & S. Maiti
9. Towards a cost-effective framework for estimating soil nitrogen pools using pedotransfer functions and machine learning L. Laurence et al.
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.
11. Machine learning-based soil aggregation assessment under four scenarios in northwestern Iran P. Nazeri et al.
12. Assessing Roles of Aggregate Structure on Hydraulic Properties of Saline/Sodic Soils in Coastal Reclaimed Areas Y. Fei et al.
13. Can pedotransfer functions based on environmental variables improve soil total nutrient mapping at a regional scale? X. Song et al.
14. Assessing the Reliability of Pedotransfer Functions for Predicting Water Retention Variability in Tropical Soils: Implications for Environmental Process Modeling E. Wimalasiri et al.
15. Estimation of important points on soil water retention curve (SWRC): comparison experimental-physical models and data mining technique A. Tafteh et al.
16. 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.
17. Evaluating ensemble learning in developing pedotransfer functions to predict soil hydraulic properties X. Li et al.
18. Countrywide mapping and assessment of organic carbon saturation in the topsoil using machine learning-based pedotransfer function with uncertainty propagation G. Szatmári et al.
19. Synergistic estimation of soil salinity based on Sentinel-1 image texture and Sentinel-2 salinity spectral indices H. Yin et al.
20. Application of Hybrid Prediction Methods in Spatial Assessment of Inland Excess Water Hazard A. Laborczi et al.
21. Integrated assessment of potentially toxic elements in soil of the Kangdian metallogenic province: A two-point machine learning approach W. Yang et al.
22. 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.
23. Digital soil mapping of key secondary soil properties using pedotransfer functions and Indian legacy soil data N. Reddy & B. Das
24. Modelling soil water retention and water‐holding capacity with visible–near‐infrared spectra and machine learning P. Baumann et al.
25. Terrain is a stronger predictor of peat depth than airborne radiometrics in Norwegian landscapes J. Vollering et al.
26. SoilKsatDB: global database of soil saturated hydraulic conductivity measurements for geoscience applications S. Gupta et al.
27. Developing pedotransfer functions using Sentinel-2 satellite spectral indices and Machine learning for estimating the surface soil moisture A. Sedaghat et al.
28. Radar remote sensing-based inversion model of soil salt content at different depths under vegetation Y. Chen et al.
29. The impact of direct and indirect digital soil mapping approaches on spatial uncertainty G. Szatmári & L. Pásztor
30. Spatial mapping of hydrologic soil groups using machine learning in the Mediterranean region E. Faouzi et al.
31. Pedotransfer functions development for modeling FC and PWP using Vis-NIR spectra combined with PLSR and regression models X. Zhu et al.
32. 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.
33. Development and functional evaluation of pedotransfer functions for soil hydraulic properties for the Zambezi River Basin M. Kalumba et al.
34. Implementation of global soil databases in the Noah-MP model and the effects on simulated mean and extreme soil hydrothermal changes K. Ishola et al.
35. 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.
36. Geospatial monitoring and analysis of agricultural drought to identify hotspots and risk assessment for Senegal G. Singh et al.
37. The spatial dependence of base saturation on forest soil grain and chemical composition seen through individual and typological divisions P. Samec et al.
38. Toward Developing a Generalizable Pedotransfer Function for Saturated Hydraulic Conductivity Using Transfer Learning and Predictor Selector Algorithm S. Jena et al.
39. Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations S. Araya & T. Ghezzehei
40. Building pedotransfer functions for estimating soil erodibility in southeastern China X. Zhu et al.
41. The Effects of Soil Representation in WRF–CLM on the Atmospheric Moisture Budget E. Dennis & E. Berbery
42. Adjusting Bulk Density Observations in the Hungarian Soil Information and Monitoring System Using Pedotransfer Functions S. Sohrab et al.
43. The effect of future land use changes on hydrologic ecosystem services: a case study from the Zala catchment, Hungary B. Decsi et al.
44. Progress in the elaboration of GSM conform DSM products and their functional utilization in Hungary L. Pásztor et al.
45. Pedotransfer Function for the Brunswick Soil Hydraulic Property Model and Comparison to the van Genuchten‐Mualem Model T. Weber et al.
46. Digital mapping of soil physical and mechanical properties using machine learning at the watershed scale M. Ghavami et al.
47. Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques A. Nouraki et al.
48. 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.
49. Estimating the soil water retention curve: Comparison of multiple nonlinear regression approach and random forest data mining technique M. Rastgou et al.
50. Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative G. Szatmári et al.
51. Available water capacity from a multidisciplinary and multiscale viewpoint. A review I. Cousin et al.
52. Satellite-Based Estimation of Soil Moisture Content in Croplands: A Case Study in Golestan Province, North of Iran S. Bandak et al.
53. Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs N. Fodor et al.
54. Characterization and prediction of soil hydraulic and solute transport parameters using a random forest model X. Ju et al.
55. Modelling of soil depth and hydraulic properties at regional level using environmental covariates- A case study in India S. Dharumarajan et al.
56. Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques S. Araya et al.
57. Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, eastern Iran M. Pahlavan-Rad et al.
58. Comparison of Four Methods for Vertical Extrapolation of Soil Moisture Contents from Surface to Deep Layers in an Alpine Area J. Li & L. Zhang
59. Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture A. Nanda et al.
60. 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.
61. 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.
62. Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset S. Mallah et al.
63. Dominant factors determining the hydraulic conductivity of sedimentary aquitards: A random forest approach M. van Leer et al.
64. Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2) B. Szabó et al.
65. Accounting for the spatial range of soil properties in pedotransfer functions S. Wang et al.
66. Machine Learning Techniques for Estimating Hydraulic Properties of the Topsoil across the Zambezi River Basin M. Kalumba et al.
67. 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
68. 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.
69. Building inexpensive topsoil saturated hydraulic conductivity maps for land planning based on machine learning and geostatistics H. Aguilera et al.
70. 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.
71. Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks M. Rehman et al.
72. 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.
73. Spatial dynamics of soil organic carbon and total nitrogen concerning aggregate size fractions using machine learning models P. Nazeri et al.
74. In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model L. Zhang et al.
75. From EU-SoilHydroGrids to HU-SoilHydroGrids: A leap forward in soil hydraulic mapping B. Szabó et al.
76. Development of Mathematical and Computational Models for Predicting Agricultural Soil–Water Management Properties (ASWMPs) to Optimize Intelligent Irrigation Systems and Enhance Crop Resilience B. Tóth et al.
77. Probabilistic and Data-Efficient Modelling of Soil Properties: Review, Tool Development and Practical Applications G. He et al.