Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau

Abstract

Soil organic matter (SOM) content is considered as an important indicator of soil quality. An accurate spatial prediction of SOM content is so important for estimating soil organic carbon pool and monitoring change in it over time at a regional scale. Due to the unfavourable natural conditions in Tibetan Plateau, soil sampling with high density is time consuming and expensive. As a result, little research has focused on the spatial prediction of SOM content in Tibet because of shortage of data. We used a two-stage process that integrated an artificial neural network (ANN) and the estimation of its residuals by ordinary kriging to produce accurate SOM content maps based on sparsely distributed observations and available auxiliary information. SOM content data were obtained from a soil survey in Tibet and were used to train and validate the ANN-kriging methodology. Available environmental information including elevation, temperature, precipitation, and normalized difference vegetation index were used as auxiliary variables in the ANN training. The prediction accuracy of SOM content was compared with those of ANN, universal kriging, and inverse distance weighting (IDW). A more accurate prediction of SOM content was obtained by ANN-kriging, with lower global prediction errors (root mean square error=6.02gkg−1) and higher Lin's concordance correlation coefficient (0.75) for validation sampling sites compared with other methods. Relative improvements of 26.94–37.10% over other methods were observed in the prediction of SOM content. In conclusion, the proposed ANN-kriging methodology is particularly capable of improving the accuracy of SOM content mapping at large scale.