Journal
GEOCARTO INTERNATIONAL
Volume 37, Issue 8, Pages 2198-2214Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10106049.2020.1815864
Keywords
Soil organic carbon; digital soil mapping; artificial neural network; geostatistical method; remote sensing; empirical Bayesian kriging regression
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This study successfully predicted soil organic carbon using remote sensing and terrain derivatives, with the advanced geostatistical method outperforming other techniques. The results can aid policymakers in adopting sustainable land use management.
Prediction and accurate digital soil mapping (DSM) of soil organic carbon (SOC) at a local scale is a key factor for any agro-ecological modelling. This study aims to use remote sensing and terrain derivatives to provide a reliable method for SOC prediction. An advanced geostatistical-based empirical Bayesian Kriging regression (EBKR) method was used and performance was compared with the artificial neural network (ANN) and hybrid ANN, i.e. ANN-OK (ordinary kriging) and ANN-CK (cokriging). The result showed that the hybrid ANN model performs better than ANN, whereas the EBKR method outperforms all other methods with the highestR(2)of 0.936. The DSM map shows that the highest SOC concentration was found in easternmost part of the study area with grass and agricultural land. This work shows the robustness of the EBKR prediction method over other techniques. The study will also aid the policymakers in adopting sustainable land use management.
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