Advances in digital soil mapping based on machine learning
Digital soil mapping can facilitate acquiring soil information efficiently and precisely.In recent years,owing to the rapid development of computer disciplines and widespread recognition of soil-landscape models,digital soil modeling using machine learning has become a mainstream idea to provide new models for soil spatial distribution interpretation.These models differ from traditional mapping techniques such as geostatistics,expert knowledge,and individual representation.This study reviews the recent findings in the field of digital soil mapping nationally and internationally,and provides a complete and systematic description of digital soil mapping from three perspectives:basic theory,mapping method and outlook of soil mapping using machine learning technology,and digital soil mapping methods including the selection of feature information,selection of mapping models,and accuracy verification of soil maps.Finally,future research directions of digital soil mapping are discussed to provide reference for comprehensive,real-time,and accurate acquisition of spatial distribution of soil information.
digital soil mappingmachine learningenvironmental covariatepredictive modelaccuracy validation
国家科技期刊平台
NSTL
万方数据
