A representativeness heuristic for mitigating spatial bias in existing soil samples for digital soil mapping

Digital soil mapping (DSM) often relies on existing soil samples obtained from various sources. However, the spatial distribution of such soil samples can be biased, for example, towards areas of better accessibility. Such biased coverage over the geographic space (i.e., spatial bias) often leads to biased coverage of the soil samples over the environmental covariate space. As a result, spatial bias degrades the correlation or statistical relationship between samples and covariates in the study area and impedes DSM accuracy. This paper presents a representativeness heuristic for mitigating spatial bias in existing soil samples for improving DSM accuracy. The key idea of the heuristic was to define and quantify sample representativeness as the goodness-of-coverage of the soil samples over the environmental covariate space. Spatial bias was then mitigated by weighting the samples towards maximizing their representativeness. Determination of the sample weights was conceived as an optimization problem and accordingly the optimal weights were determined using a genetic algorithm. To evaluate the effectiveness of the representativeness heuristic, a case study of mapping soil organic matter (SOM) content using existing soil samples was conducted in Heshan study area, northeastern China. Results showed that weighting soil samples using the optimal weights determined from the representativeness heuristic improved SOM content mapping accuracy. Moreover, a positive relationship between sample representativeness and mapping accuracy was observed, suggesting sample representativeness is an effective indicator of mapping accuracy. Additionally, the determined optimal weights were informative of individual sample importance and thus can be used as guidance to filter existing soil samples to improve DSM accuracy.