Predicting soil organic matter content in a plain-to-hill transition belt using geographically weighted regression with stratification

In contrasting landscapes, the ignorance of diverse relationships between environmental covariates and soil organic matter (SOM) will likely lead to a great deal of prediction uncertainty. To deal with this issue, this study aimed to develop a method to predict SOM in a plain-to-hill transition belt through defining a landform-based stratified model (i.e., separate estimation for different landforms). Initially, the area was split into two strata based on landform types (low-relief areas and hill areas). And then in each stratum the dominant environmental variables were determined. Finally, geographically weighted regression (GWR) was applied to explore the relationships between SOM and environmental variables for the whole study area as well as for each stratum. The results showed that the dominant variables for each stratum were different. The model with stratification outperformed the model without stratification with regards to mean error (0.1 vs. 1.0, respectively), mean absolute error (3.1 vs. 3.8, respectively) and root mean square error (4.1 vs. 5.4, respectively). We conclude that the developed strategy that based on landscape stratification and GWR will be useful for predicting SOM in areas with high variation in topography.