Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 7; p. 1095
• Main Authors: Taghizadeh-Mehrjardi, Ruhollah, Schmidt, Karsten, Amirian-Chakan, Alireza, Rentschler, Tobias, Zeraatpisheh, Mojtaba, Sarmadian, Fereydoon, Valavi, Roozbeh, Davatgar, Naser, Behrens, Thorsten, Scholten, Thomas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29.03.2020
• Subjects: Arid regions; artificial intelligence; Artificial neural networks; Carbon; Carbon content; Climate change; climatic zones; Deep learning; Digital mapping; digital soil mapping; Information processing; Iran; Learning algorithms; Learning theory; Machine learning; machine learning models; Model accuracy; Neural networks; Organic carbon; Organic soils; Precipitation; prediction; Predictions; Remote sensing; Sea level; Soil depth; Soil improvement; Soil mapping; soil organic carbon; Soil profiles; Soil properties; soil surveys; Soils; Spatial analysis; spatial block cross-validation; Spatial distribution; Stacking; stacking of models; Studies; Teaching methods; Topography; Iran; France
• DOI: 10.3390/rs12071095
• ISSN: 2072-4292

Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0–5 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models—the best individual model. This indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy. Overall, our results suggest that the stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions.