35 citations as recorded by crossref.

1. On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization I. Dunkl & M. Ließ 10.5194/soil-8-541-2022
2. Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils T. Broeg et al. 10.3390/rs15040876
3. Machine Learning With GA Optimization to Model the Agricultural Soil-Landscape of Germany: An Approach Involving Soil Functional Types With Their Multivariate Parameter Distributions Along the Depth Profile M. Ließ et al. 10.3389/fenvs.2021.692959
4. Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics G. Szatmári et al. 10.1016/j.geoderma.2021.115356
5. High-performance soil class delineation via UMAP coupled with machine learning in Kurdistan Province, Iran R. Taghizadeh-Mehrjardi et al. 10.1016/j.geodrs.2024.e00754
6. Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-year Periods for Soil Organic Carbon Content Mapping in Central France D. Urbina-Salazar et al. 10.3390/rs15092410
7. High-resolution digital soil mapping of amorphous iron- and aluminium-(hydr)oxides to guide sustainable phosphorus and carbon management M. van Doorn et al. 10.1016/j.geoderma.2024.116838
8. Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area M. Sakizadeh et al. 10.1007/s10653-023-01845-9
9. High-Resolution Soil Property Maps from Digital Soil Mapping Methods, Czech Republic D. Žížala et al. 10.2139/ssrn.3928321
10. Downscaling digital soil maps using electromagnetic induction and aerial imagery A. Bjørn Møller et al. 10.1016/j.geoderma.2020.114852
11. Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH M. Nussbaum et al. 10.1016/j.geoderma.2023.116579
12. Can We Use Machine Learning for Agricultural Land Suitability Assessment? A. Møller et al. 10.3390/agronomy11040703
13. Fine Resolution Mapping of Soil Organic Carbon in Croplands with Feature Selection and Machine Learning in Northeast Plain China X. Zhang et al. 10.3390/rs15205033
14. Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network H. Chen & J. Wang 10.3390/s23052365
15. Inequality in heat: The role of spatial patterns of urban green infrastructure P. Chen 10.1016/j.uclim.2024.101820
16. Spatial Downscaling of Global Categorical Soil Information Into Data Suitable for Land-Use Management T. FLYNN 10.2139/ssrn.4496677
17. Colombian soil texture: building a spatial ensemble model V. Varón-Ramírez et al. 10.5194/essd-14-4719-2022
18. Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA D. Warner et al. 10.1016/j.ejrh.2021.100946
19. The patchiness of soil 13C versus the uniformity of 15N distribution with geomorphic position provides evidence of erosion and accelerated organic matter turnover M. Ghotbi et al. 10.1016/j.agee.2023.108616
20. Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning C. Patriche et al. 10.1371/journal.pone.0289286
21. Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland T. Broeg et al. 10.1016/j.geoderma.2024.116850
22. Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales Y. Xia et al. 10.3389/fsoil.2022.890437
23. Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree N. Kebonye et al. 10.1016/j.atech.2022.100106
24. Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework P. Bogaert et al. 10.1016/j.geoderma.2023.116604
25. Mapping the phosphorus sorption capacity of Danish soils in four depths with quantile regression forests and uncertainty propagation A. Møller et al. 10.1016/j.geoderma.2022.116316
26. Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping X. Zhang et al. 10.1016/j.geoderma.2023.116383
27. Gap-free global annual soil moisture: 15 km grids for 1991–2018 M. Guevara et al. 10.5194/essd-13-1711-2021
28. Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland A. Baltensweiler et al. 10.1016/j.geodrs.2021.e00437
29. Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset S. Mallah et al. 10.3390/agronomy12112613
30. Impact of Land Cover Changes on Soil Type Mapping in Plain Areas: Evidence from Tongzhou District of Beijing, China X. Wu et al. 10.3390/land12091696
31. Using remote sensing to identify soil types based on multiscale image texture features M. Duan & X. Zhang 10.1016/j.compag.2021.106272
32. Soilscapes of New Zealand: Pedologic diversity as organised along environmental gradients P. Roudier et al. 10.1016/j.geoderma.2021.115637
33. Improving model performance in mapping cropland soil organic matter using time-series remote sensing data X. Zhang et al. 10.1016/j.jia.2024.01.015
34. A machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands M. Viljanen et al. 10.1186/s12942-022-00304-5
35. Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region) A. Zeyliger et al. 10.3390/s22166153