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• A phenology-based method was developed for rice-crayfish field (RCF) mapping. • The RCF mapping method showed high accuracy and generalization ability over years. • DNDC model was used to assess the impact of RCF expansion on regional CH 4 emission. • RCF generated the least CH 4 flux in comparison to traditional rice cropping systems. • Evaluation results provided valuable insights for agricultural land use planning. The rice-crayfish field (i.e., RCF), a recently emerged rice cultivation pattern, has experienced remarkable growth in China over the last decade due to its significant socioeconomic advantages. However, the impacts of expanding RCF areas on the regional-scale ecological environment, particularly concerning methane (CH 4) emissions, remain unclear. A major obstacle in addressing this knowledge gap is the absence of accurate and up-to-date spatial distribution information on RCF across years. Here, we selected Jianghan Plain which has the largest RCF area in China as the study area. First, we developed a phenology-based identification algorithm using Landsat-7/8 satellite data, which considered the distinctive flooding signatures of RCF during the rice fallow periods, to identify RCF at the regional scale. Second, we employed the DeNitrification–DeComposition (DNDC) model to simulate the CH 4 fluxes of various rice cropping systems, including RCF, rice monoculture (RM), rice-rapeseed rotation (RR), and rice-wheat rotation (RW). Finally, the effects of RCF expansion during 2014–2019 on regional CH 4 emissions were analyzed by comparing six scenarios that simulated the conversion of different rice cropping systems to RCF. Results showed the phenology-based algorithm performed well in extracting RCFs, achieving an overall accuracy >92 % for all years based on 1025 RCF and 2096 non-RCF validation samples. RCF generated the least CH 4 flux, followed by RM, RR, and RW. Moreover, shifting from traditional rice cropping systems to RCF reduced CH 4 emissions across all cases, with mitigation rates ranging from 4.82 % to 21.85 %, indicating RCF's substantial CH 4 mitigation potential. These findings significantly improve our understanding of the ecological effects of RCF cultivation, which is critical for advancing land use planning and decision-making for sustainable agricultural development in China. Our presented evaluation method of integrating the remote sensing mapping algorithm and DNDC model can be easily generalized for other crop types in other regions. [ABSTRACT FROM AUTHOR]