In the heart of the Indian Himalayas, where rugged terrain and diverse ecosystems pose significant challenges to soil mapping, a groundbreaking study has emerged, promising to revolutionize sustainable land management and conservation efforts. Led by Justin George Kalambukattu from the Agriculture and Soils Department at the Indian Institute of Remote Sensing (IIRS), part of the Indian Space Research Organisation (ISRO), this research employs advanced machine-learning techniques to map soil organic carbon (SOC) at an unprecedented 30-meter resolution.
The Himalayan region’s complex topography has long hindered high-resolution soil mapping, with previous studies achieving resolutions ranging from 250 to 90 meters. However, Kalambukattu and his team have overcome this obstacle by integrating machine-learning algorithms such as random forest regression, support vector regression, and extreme gradient boosting. “The increase in spatial resolution definitely enhances the data quality and supports decision-making for sustainable soil management,” Kalambukattu explains.
The team collected surface soil samples from 421 georeferenced locations, representing various elevation zones, geology, and land use types. They then generated environmental covariates using diverse data sources and the Google Earth Engine platform. These covariates, representing pedogenic factors like climate, terrain, spectral indices, land use, and lithology, were crucial in developing accurate spatial models for predicting SOC.
The study found that the random forest model outperformed the other two models, with an R-squared value of 0.72 during the testing phase. This model’s success can be attributed to the equal dominance of covariates representing vegetation, climate, and topography among its top predictors. The resulting SOC maps revealed that the northern, northeastern, and northwestern parts of the study area exhibited higher SOC contents. Among the predominant land use types, evergreen forests showed the highest SOC values at 2.88%.
The implications of this research are far-reaching, particularly for the energy sector. Soil organic carbon plays a crucial role in carbon sequestration, which is vital for mitigating climate change. Accurate SOC mapping can help identify areas suitable for reforestation and afforestation projects, enhancing carbon capture and storage. Moreover, understanding SOC distribution can aid in developing sustainable bioenergy crops, further contributing to the energy sector’s decarbonization efforts.
This study, published in ‘Discover Soil’ (translated from Russian as ‘Explore Soil’), demonstrates the potential of digital soil mapping techniques enhanced by remote sensing and machine learning. As Kalambukattu notes, “This detailed database can prove beneficial in devising effective land management and resource conservation strategies in the fragile mountain ecosystems of the Indian Himalayas.”
The future of soil mapping in challenging terrains looks promising, with this research paving the way for more accurate and detailed SOC maps. As machine-learning techniques and remote sensing technologies continue to advance, we can expect even higher resolutions and more precise predictions, enabling better-informed decisions for sustainable land management and conservation. The energy sector, in particular, stands to gain significantly from these developments, as accurate SOC mapping can support the growth of sustainable bioenergy and carbon sequestration initiatives.