August 27, 2020 | Critical Reviews in Environmental Science and Technology |
Introduction: Led by an India-based team from Bidhan Chandra Krishi Viswavidyalaya, the Indian Institute of Remote Sensing (IIRS)-ISRO, and Punjab Agricultural University, the authors synthesize evidence on how common agronomic practices influence soil organic carbon (SOC) stocks and evaluate how process-based models (e.g., RothC, Century, DNDC) together with satellite and airborne remote sensing can support scalable SOC assessment.
Key findings: Across the literature reviewed, conservation-oriented practices such as reduced or no tillage, residue retention, diversified rotations or cover crops, balanced fertilization, and organic amendments are consistently associated with higher SOC accumulation, although effect sizes vary with climate, soil texture, baseline SOC, and duration of adoption. SOC gains are often concentrated in the topsoil, which may overestimate whole-profile sequestration if deeper layers are not measured or modelled. For monitoring, the authors emphasize that process-based models can estimate SOC trajectories and distinguish management effects from climate variability, but they require well-calibrated inputs and long-term field validation. Remote sensing is most useful for mapping indicators related to SOC dynamics, including vegetation productivity, residue cover, and soil moisture, and for scaling model outputs rather than directly measuring SOC from space. The review concludes that robust MRV requires integrated workflows combining field sampling, modelling, and remote sensing, together with transparent uncertainty reporting to support carbon credit or results-based policy programs.
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