Spatial downscaling of global soil texture classes into 30 m images at the province scale

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Soil categorical data is an important aspect in soil science because it effectively facilitates communication between policymakers and stakeholders. Furthermore, soil categorical data exceeds single-property data in terms of depth of information and is an essential component of various scientific disciplines such as hydrological, ecological and pollution frameworks. However, datasets containing such information are usually too coarse for local needs and regional policies. In this study, an algorithm was introduced, known as rafikisol, to spatially downscale (to a finer detail/resolution) soil texture classes from 1 km SoilGrids images into 30 m images in three environmentally diverse provinces (Gauteng, KwaZulu-Natal, and the Western Cape) in South Africa. Rafikisol surpassed the performance of another high-resolution soil dataset (Innovative Solutions for Decision Agriculture) by 9% and 27% in Gauteng and the Western Cape, respectively (accuracy ∼75% and ∼72%). Conversely, iSDAsoil outperformed rafikisol by 34% in KwaZulu-Natal (11% accuracy). The spatial soil texture class distribution predicted by rafikisol was considerably different and heavier (clayey) in Gauteng and KwaZulu-Natal but had a similar spatial distribution with lighter (sandy) soil texture in the Western Cape compared to iSDAsoil. With improvements such as the introduction of new sampling techniques, algorithm optimization and the use of expert knowledge, this method has the potential to increase the accuracy of additional modeling frameworks that require high-resolution soil information, especially in data-scarce or resource-constrained regions. This has implications for proper land-use management, affecting aspects ranging from food security and urban expansion to biodiversity.