Extended model prediction of high-resolution soil organic matter over a large area using limited number of field samples

Abstract

Detailed soil organic matter (SOM) spatial distribution maps are essential for soil management and forestry operations. However, mapping of spatial SOM distribution over a large area is a difficult challenge, especially in regions where field samples are difficult to obtain. The objective of this research was to develop a two-stage approach to map SOM content with 10 m-resolution in Yunfu, South China with an area of 7785 km2. In the first stage, using 10-fold cross-validation 511 artificial neural network (ANN) models were built to map SOM content based on 318 field samples from three of five sub-areas of Yunfu (ANN model area). Results indicated that the optimal ANN model with six DEM-derived variables as model inputs, i.e. ANN6, had a good model performance in ANN model area, 5.6 g/kg of root mean squared error (RMSE), 0.81 of R2, and 84.1% of relative overall accuracy (ROA) ± 10%, and the best generalization capability in the rest two of five sub-areas of Yunfu (extended model area), with 7.7 g/kg of RMSE, 0.58 of R2, and 60.7% of ROA ± 10%. In the second stage, using the reverse k-fold cross-validation extended models were developed to adapt ANN6-produced SOM content to fit field samples in the extended model areas. Results indicated the optimal extended model only required 20% of 386 field samples (5-fold) to build a stable and significant linear relationship between ANN6-produced SOM content and measured SOM content from the extended model area, and improved model accuracy with 9–21% of RMSE, 28–29% of R2, and 6–21% of ROA ± 10%. Thus, the two-stage method is a viable way to generate SOM content over a large area with limited number of field samples.