Geoderma (Sep 2024)
On the effectiveness of multi-scale landscape metrics in soil organic carbon mapping
Abstract
Soil organic carbon (SOC) mapping delivers invaluable information to the global carbon budget and climate change mitigation endeavour. Environmental variables at sample locations are frequently used as explanatory variables for simulating the spatial distribution of soil properties. However, these may not fully capture the spatial information generated by soil-forming processes. We applied multi-scale landscape metrics that can comprehensively characterize the surrounding landscape information of sample points. The metrics were extracted at two levels (landscape level and class level) and include the diversity, shape and area, fragmentation and connectivity with a buffer distance from 500 m to 5,000 m. We then investigated its effectiveness as environmental variables via recursive feature elimination, random forests, and quantile regression forests. The Jianghan Plain, China, was selected as the study area, where over 19,000 topsoil samples were collected. Results indicated that multi-scale landscape metrics enhanced the predictability of SOC mapping, with R2 increased by 43 %. Specifically, Shannon’s diversity index, the percentage of landscape index, interspersion and juxtaposition index, and patch cohesion index outperformed environmental variables that were extracted at the sample location. In addition, the relationships between SOC and landscape metrics were found to be scale-dependent. Landscape metrics demonstrated significant explanatory capacity for SOC across various spatial scales. Notably, as the scale surpassed 3,000 m, there was a discernible improvement in the explanatory effectiveness of the landscape metrics for SOC. Our findings highlight that landscape metrics are effective in characterising the soil-landscape relationship that is generated by multi-scale natural and anthropogenic soil-forming processes. Meanwhile, knowledge of the intricate relationship between landscape characteristics and SOC is crucial for informing land management decisions aimed at enhancing carbon sequestration, mitigating climate change, and maintaining soil fertility.
