Abstract
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.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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The authors acknowledge the Indian Institute of Remote Sensing, Indian Space Research Organisation, Dehradun for providing the necessary datasets.
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Nyenshu Seb Rengma, Manohar Yadav, and Justin George Kalambukattu designed the conceptual framework of the study. Nyenshu Seb Rengma designed the methodology, conducted the experiment, and wrote the initial manuscript with inputs from Manohar Yadav. Justin George Kalambukattu and Suresh Kumar provided the soil samples. Nyenshu Seb Rengma and Manohar Yadav reviewed and edited the final manuscript.
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Rengma, N.S., Yadav, M., Kalambukattu, J.G. et al. Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain. Environ Monit Assess 195, 994 (2023). https://doi.org/10.1007/s10661-023-11608-9
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DOI: https://doi.org/10.1007/s10661-023-11608-9