Larger methane emissions from Indian paddies than expected: towards tracking wetland methane emissions over India using multi-satellite measurements and modeling, Part B (non-natural wetlands)
Abstract
India is the world’s second-largest rice producer, accounting for ∼22% of global production from cultivating over 40 million hectares and exporting more than 15 million tons annually. This makes the country a key player in global food security, but a significant contributor to the global rice methane (CH 4) emissions. Current estimates of Indian agricultural CH 4 emissions are highly uncertain, exceeding 50%, and lack spatially explicit details across the region, which poses challenges in implementing sustainable mitigation strategies. This study developed a satellite-based method that resolves seasonal and subnational variability in rice CH 4 emissions across India at 10 m resolution. By integrating Sentinel-1 SAR and Sentinel-2 multispectral observations with a semi-empirical, process-based model, we developed the first observationally-driven spatially explicit, multi-season dataset of rice CH 4 emissions for 2019–2025 over India. The methodological evaluation achieved 90.12% pixel-level validation accuracy and demonstrated strong agreement with agricultural census statistics ( R2 = 0.896) for rice extent mapping. By utilizing unprecedented details on rice extent, we estimated national annual mean emissions to be 9.73 ± 2.03 Tg CH 4 yr −1, which is larger than previously reported. We find a dominant contribution from the Kharif monsoon season (74.7% of annual emissions), followed by the Summer season (17.5%) and the Rabi winter season (7.8%), highlighting untapped mitigation opportunities in rice fields through targeted water-management practices during Kharif rice cultivation. A 18-member model ensemble configuration captures seasonal and spatial variability in emissions driven by temperature-dependent methanogenesis, heterotrophic respiration, and precipitation-modulated flooding dynamics, and enables the quantification of structural uncertainty arising from model parameters and environmental sensitivities. Thus, the study’s findings address key structural limitations in existing inventory approaches and provide robust, highly-resolved rice-based methane emission estimates for atmospheric inverse modeling applications, subnational mitigation targeting, and the evaluation of water management strategies. Overall, this study lays the foundation framework for assessing agricultural CH 4 emissions and their mitigation efficiency in India, with the potential for adaptation in other regions.