Semi-supervised learning for the spatial extrapolation of soil information

Abstract

Digital soil mapping (DSM) can be used to predict soils at unvisited sites, but problems arise when predictions are needed in areas without any soil observations. In such situations, DSM can still extend the results from reference areas with soil data to target areas that are alike in terms of soil-forming factors and obey the same rules. Such DSM methods have low accuracy due to the complexity of spatial variation in soil, and the difficulty of matching soil-forming factors exactly between reference and target areas. A new approach for extrapolating soil information from reference to target areas is proposed in the current research. We evaluated the ability of a semi-supervised learning (SSLR→T) approach compared to a supervised learning (SLR→T) approach for extrapolating soil classes in two areas (reference and target areas) in central Iran. The SSLR→T used soil observations from the reference area and covariates from both areas. Then, the learned knowledge produced by SSLR→T was transferred to the target area to estimate soil classes. The findings revealed that SSLR→T resulted in higher overall accuracy (0.65) and kappa index (0.44) in the target area compared to the SLR→T (overall accuracy = 0.40 and kappa index = 0.18). Furthermore, the SSLR→T produced the lower values of the confusion index (mean = 0.66) compared to the SLR→T (mean = 0.80). This indicated that the SSLR→T could not only increase the accuracy but also decrease the uncertainty of the soil class predictions, compared to the spatial extrapolation predictions derived from the SLR→T. Generally, these findings indicated that leveraging covariate information from the target area during the training of DSM models in the reference area could successfully improve the generalization power of the models, indicating the effectiveness of SSLR→T for spatial extrapolation.