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Post-harvest rice residue burning in India has important implications for air quality and soil nutrient retention, yet its spatial and temporal dynamics remain difficult to quantify because widely used burned-area products are relatively coarse for fragmented agricultural fields. This study provides a high-resolution, temporally resolved assessment of rice residue burning across India during the 2025 Kharif season. Sentinel-2 imagery was used to delineate rice-growing areas through spectral matching and to map the first detection of burned residue at 10-day intervals using an empirically selected threshold-separability rule. Image-interpreted Sentinel-2 reference points were used to assess spectral separability, while MODIS MCD64A1 was used for an inter-product comparison rather than as independent ground-truth validation. The analysis detected 8.33 million hectares of burned rice residue, with the five highest-burning states accounting for 72.1% of the national total. The typical burned area per district was highest in mid-November, whereas the number of districts reaching their peak first-detection interval was greatest in December. Estimated losses included 291,543 tonnes of nitrogen, 26,655 tonnes of phosphorus, 174,926 tonnes of potassium, 22,490 tonnes of sulphur and 19.99 million tonnes of organic carbon. The corresponding theoretical mass of CO₂ under complete oxidation of the estimated carbon was 73.30 million tonnes; this is not a CO₂-equivalent greenhouse-gas inventory. The nutrient-subsidy-equivalent valuation based on Government of India Nutrient-Based Subsidy rates was approximately INR 1430 crore, with a Monte Carlo median of approximately INR 1416 crore and a fixed burned-area 5th to 95th percentile sensitivity interval of INR 1023 to 1901 crore. About 31.3% of districts exceeding the analyst-defined 500-ha reporting cutoff accounted for 80% of the nutrient-subsidy-equivalent valuation. These results provide a season-specific spatial and temporal inventory of rice residue burning and identify areas where high-resolution monitoring may support targeted residue-management planning.
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Murali Krishna Gumma (2026) studied this question.
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