Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree

[1]  B. Minasny,et al.  Digital mapping of GlobalSoilMap soil properties at a broad scale: A review , 2022, Geoderma.

[2]  R. Vašát,et al.  Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape , 2022, Soil and Water Research.

[3]  Haibin Shi,et al.  Modeling salinized wasteland using remote sensing with the integration of decision tree and multiple validation approaches in Hetao irrigation district of China , 2022, CATENA.

[4]  High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic , 2022, CATENA.

[5]  G. Heuvelink,et al.  Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics , 2021 .

[6]  Christoph Molnar,et al.  Beyond prediction: methods for interpreting complex models of soil variation , 2021, Geoderma.

[7]  Ndiye M. Kebonye,et al.  Exploring the novel support points-based split method on a soil dataset , 2021, Measurement.

[8]  G. Heuvelink,et al.  SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty , 2021, SOIL.

[9]  Philippe Lagacherie,et al.  Density of soil observations in digital soil mapping: A study in the Mayenne region, France , 2021 .

[10]  A. Lausch,et al.  Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images. , 2020, The Science of the total environment.

[11]  Gerard B.M. Heuvelink,et al.  Model averaging for mapping topsoil organic carbon in France , 2020 .

[12]  Mogens Humlekrog Greve,et al.  Oblique geographic coordinates as covariates for digital soil mapping , 2019, SOIL.

[13]  Zhou Shi,et al.  High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling. , 2019, The Science of the total environment.

[14]  Zhou Shi,et al.  A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution. , 2019, The Science of the total environment.

[15]  Randal S. Olson,et al.  Relief-Based Feature Selection: Introduction and Review , 2017, J. Biomed. Informatics.

[16]  Alfred E. Hartemink,et al.  Digital Mapping of Soil Organic Carbon Contents and Stocks in Denmark , 2014, PloS one.

[17]  B. Minasny,et al.  Digital Mapping of Soil Classes Using Decision Tree and Auxiliary Data in the Ardakan Region, Iran , 2014 .

[18]  Witold R. Rudnicki,et al.  Feature Selection with the Boruta Package , 2010 .

[19]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[20]  Budiman Minasny,et al.  A conditioned Latin hypercube method for sampling in the presence of ancillary information , 2006, Comput. Geosci..

[21]  B. Henderson,et al.  Australia-wide predictions of soil properties using decision trees , 2005 .

[22]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[23]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[24]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[25]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[26]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[27]  Leland Wilkinson Tests of significance in stepwise regression. , 1979 .

[28]  J. Morgan,et al.  Problems in the Analysis of Survey Data, and a Proposal , 1963 .