Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review

• Published in: Geoderma Vol. 352; pp. 395 - 413
• Main Authors: Lamichhane, Sushil, Kumar, Lalit, Wilson, Brian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2019
• Subjects: Algorithms; Australia; China; climate; computer-aided mapping; Covariates; data collection; Digital soil mapping; kriging; land use; linear models; prediction; regression analysis; Soil organic carbon; soil surveys; support vector machines; Systematic mapping; topography; uncertainty; United States; Validation; vegetation index; Australia; United States; China; Validation; Algorithms; Digital soil mapping; Soil organic carbon; Covariates; Systematic mapping
• DOI: 10.1016/j.geoderma.2019.05.031
• ISSN: 0016-7061

This article reviews the current research and applications of various digital soil mapping (DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks following a systematic mapping approach from 2013 until present (18 February 2019). It is intended that this review of relevant literature will assist prospective researchers by identifying knowledge clusters and gaps in relation to the digital mapping of SOC. Of 120 studies, most were clustered in some specific countries such as China, Australia and the USA. The highest number publications were in 2016 and 2017. Regarding the predictive models, there was a progression from Linear Models towards Machine Learning (ML) techniques, and hybrid models in Regression Kriging (RK) framework performed better than individual models. Multiple Linear Regression (MLR) was the most frequently used method for predicting SOC, although it was outperformed by other ML techniques in most studies. Random Forest (RF) was found to perform better than MLR and other ML techniques in most comparative studies. Other common and competitive techniques were Cubist, Neural Network (NN), Boosted Regression Tree (BRT), Support Vector Machine (SVM) and Geographically Weighted Regression (GWR). Due to the inconsistency in various comparative studies, it would be advisable to calibrate the competitive algorithms using specific experimental datasets. This review also reveals the environmental covariates that have been identified as the most important by RF technique in recent years in regard to digital mapping of SOC, which may assist in selecting optimum sets of environmental covariates for mapping SOC. Covariates representing organism/organic activities were among the most frequent among top five covariates, followed by the variables representing climate and topography. Climate was reported to be influential in determining the variation in SOC level at regional scales, followed by parent materials, topography and land use. However, for mapping at a resolution that represents smaller areas such as a farm- or plot-scale, land use and vegetation indices were stated to be more influential in predicting SOC. Furthermore, unlike a previous review work, all recent studies in this review incorporated validation and 41% of them estimated spatially explicit prediction of uncertainty. Only 9.16% studies performed external validation, whereas most studies used data-splitting and cross-validation techniques which may not be the best options for datasets obtained through non-probability sampling.