Research Projects

Climate change poses escalating threats to ecosystems, with rising greenhouse gas levels increasing the urgency for accurate carbon accounting. Soils are the largest terrestrial carbon reservoir, storing over 1500 Pg of carbon in the top meter. Within this reservoir, Soil Organic Carbon (SOC) and Black Carbon (BC) play distinct roles in the carbon cycle. SOC supports plant productivity, microbial diversity, and nutrient retention, while BC, formed by incomplete combustion of biomass and fossil fuels, resists decomposition and contributes to long-term carbon sequestration. These carbon forms span labile and stable pools critical to ecosystem health and climate mitigation. Despite their importance, monitoring SOC and BC variability remains difficult, especially in India, where rapid land-use change, high emissions, and ecological diversity converge. Traditional carbon assessments are labor-intensive and often limited to surface-level SOC, with BC frequently misclassified due to spectral overlap with aromatic-rich SOC. Remote sensing and spectroscopic methods show promise but often fail to distinguish chemically distinct carbon forms and lack validation in data-poor tropical regions. India lacks a high-resolution, field-calibrated, and chemically verified framework for mapping stable carbon like BC, leading to its under representation in global models and strategies. To bridge this gap, the project proposes an integrated analytical–spectral approach to map and characterize SOC and BC in Indian soils. Hyperspectral imaging (400–1200 nm) will record spectral signatures across soil profiles from ecologically diverse regions. These will be validated using FTIR spectroscopy, SEM, and elemental analysis to ensure chemical distinction of labile and recalcitrant carbon. Chemometric modeling will generate India’s first SOC–BC spectral reference library to enable scalable carbon pool classification and satellite sensor calibration. The project will also develop a Carbon Stability Index (CSI), quantifying the persistence of carbon forms and integrating them into models such as RothC and the Millennial Model for better prediction of carbon turnover. By combining imaging technologies with chemical data, this study will offer a novel, non-destructive, scalable method for carbon pool differentiation. The outcomes will support India’s National Carbon Accounting System (NCAS) and aid climate-smart land use, soil health monitoring, and mitigation planning strengthening carbon management in one of the world’s most ecologically diverse and vulnerable regions. Which is aligns strongly with the Government of India’s Viksit Bharat vision by enhancing national capacity for climate-resilient agriculture, digital environmental monitoring, and carbon-neutral growth.