Semi-supervised learning for the spatial extrapolation of soil information

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 infor-mation 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.

Automated summary

Digital soil mapping can be used to predict soils at unvisited sites, but problems arise when predictions are needed in areas without any soil observations. A new semi-supervised learning approach was found to outperform supervised learning in extrapolating soil classes in target areas, resulting in higher accuracy and lower uncertainty.