Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain

• Published in: Environmental monitoring and assessment 2023-08, Vol.195 (8), p.994-994, Article 994
• Main Authors: Rengma, Nyenshu Seb, Yadav, Manohar, Kalambukattu, Justin George, Kumar, Suresh
• Format: Article
• Language: English
• Subjects: algorithms; Atmospheric Protection/Air Quality Control/Air Pollution; Carbon; Carbon - analysis; Clay; Climate change; Conservation; Data augmentation; data collection; Digital mapping; Digital maps; Earth and Environmental Science; Ecological effects; Ecology; Ecotoxicology; Environment; Environmental factors; Environmental Management; Environmental monitoring; Environmental Monitoring - methods; Environmental science; Land degradation; landscapes; Machine Learning; Mapping; Modelling; Monitoring/Environmental Analysis; Mountain regions; Mountain soils; Mountains; Organic carbon; Organic soils; Regression analysis; Regression models; sand; Sensitivity analysis; silt; Soil; Soil formation; Soil mapping; soil organic carbon; Soil properties; Soil texture; Sustainability management; Terrain; Texture; Topography
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-023-11608-9

Mountain soils have received significant attention due to their profound influence on ecological processes and environmental factors. However, mapping these soils in digital soil mapping technique encounters several challenges, including high local variability, non-linear relationships between environmental covariates and soil properties, limited accessibility in complex topographical settings, and the absence of universally applicable covariates for soil formation. To address these issues, this study integrates soil-forming factors of the scorpan model to map soil organic carbon (SOC) and soil texture in the mid-Himalayas. By considering over 100 environmental covariates, with a focus on terrain parameters relevant to mountainous environments, the study aims to enhance the accuracy of ML regression models through augmentation techniques that overcome data insufficiency. Using augmented soil observations and covariates, a non-parametric random forest regression model is trained and applied to predict soil variables across the study area, generating a continuous fine-resolution map. The model’s performance, evaluated against an unknown dataset, was significant with an R-square of 0.80, 0.79, 0.72, and 0.84 for clay, sand, silt, and SOC, respectively. Furthermore, a sensitivity analysis of the environmental covariates and their impact on the model revealed that all the soil-forming factors make a significant contribution to the model’s effectiveness. The insights gained from this research contribute to a better understanding of mountain soils and facilitate the development of effective conservation and sustainable management strategies for mountainous regions.