Finer soil properties mapping framework for broad-scale area: A case study of Hubei Province, China

Abstract

Fine-scale spatial distribution of soil physicochemical properties is crucial for soil quality management, agriculture planning and geotechnical engineering. Existing soil map databases are usually developed in national scale, potentially leading to issues of coarse resolution and restricted applicability in fine-scaled studies. For broad-scale area, conventional digital soil mapping methods are challenging due to the lack of representative soil profiles and their uneven distribution. This study addresses these challenges by developing a downscaling-based framework to map 11 soil physicochemical properties at a 30-m resolution. The methodology involves constructing regression models using soil properties derived from coarse national soil maps and soil-forming covariates. Predictions are subsequently refined using fine-resolution covariates. To capture fine-scale spatial variability across diverse landscapes, 30-m resolution remote sensing data, relief, climate, and spatial covariates were prepared. The fine-gridding framework employs tree-based machine learning models, particularly random forest (RF), to enhance prediction accuracy. Evaluation of the fine-gridded soil maps demonstrated commendable results, with high consistency in summary statistics, semi-variograms, and evaluation metrics compared to reference maps. Mass preservation of RF-predicted maps exhibit high performances with both concordance correlation coefficients (CCCs) and coefficients of determination (R2s) exceeding 0.9 across most scenarios. This study provides a robust approach for enhancing the spatial resolution of soil maps, facilitating their use in local fine-scale applications. The proposed strategy offers a valuable solution for broad-scale areas that require finer soil maps but lack sufficient qualified soil profiles.

Keywords

• Digital soil mapping
• Broad-scale
• Fine gridding
• Downscaling
• Machine learning