XIE Xianli
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaXIA Chengye
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China;Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaYIN Biao
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaLI Anbo
Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, ChinaLI Kaili
School of Geography, Jiangsu Second Normal University, Nanjing 210013, ChinaPAN Xianzhang
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaS159
Supported by the Cropland Degradation Monitoring (No. NK2022180104) and the National Natural Science Foundation of China (No. 41971068)
Soil is a complex with high heterogeneity. The early research on digital soil mapping mainly focused on the lateral variation of soil, with less consideration of the vertical variation and three-dimensional (3D) digital soil mapping. In recent years, the rapid developments of 3D geographic information technology and earth observation and detection technology have greatly promoted research on soil 3D data acquisition, 3D prediction, 3D data modeling, 3D model and visualization. In this paper, we reviewed the existing research on soil prediction and soil model construction in 3D space, to provide suggestions for the application and development of 3D digital soil mapping. We searched the Web of Science database by using 3D soil mapping, 3D GIS, 3D data model, 3D geological modeling, 3D visualization, soil spatial variability, spatial prediction, Kriging interpolation, soil-landscape analysis, depth function, machine learning, geostatistics, random simulation as keywords, and selected the key literatures for analysis based on correlation, citation rate and literature sources. We summarized the popular methodologies for soil spatial variability, 3D spatial soil prediction, soil 3D data model, and 3D model construction, and evaluated the advantages, disadvantages and application scenarios of each method. This review presents the common problems of 3D soil mapping, such as sparse soil profile data, low accuracy of 3D soil prediction, and insufficient information to create the data source for 3D soil modelling, and put forward some feasible research prospects.
XIE Xianli, XIA Chengye, YIN Biao, LI Anbo, LI Kaili, PAN Xianzhang. A Review of Soil 3D Prediction and Modelling Techniques[J]. Acta Pedologica Sinica,2025,62(1):14-28.
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