9 citations as recorded by crossref.

1. Can We Use Machine Learning for Agricultural Land Suitability Assessment? A. Møller et al. 10.3390/agronomy11040703
2. 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
3. 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
4. Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA D. Warner et al. 10.1016/j.ejrh.2021.100946
5. Gap-free global annual soil moisture: 15 km grids for 1991–2018 M. Guevara et al. 10.5194/essd-13-1711-2021
6. 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
7. Using remote sensing to identify soil types based on multiscale image texture features M. Duan & X. Zhang 10.1016/j.compag.2021.106272
8. Soilscapes of New Zealand: Pedologic diversity as organised along environmental gradients P. Roudier et al. 10.1016/j.geoderma.2021.115637
9. Downscaling digital soil maps using electromagnetic induction and aerial imagery A. Bjørn Møller et al. 10.1016/j.geoderma.2020.114852