Plastic-mulching technology has a crucial role to play in modern agriculture by optimizing crop growth environments and enhancing yields. Accurately detecting and mapping the distribution of plastic-mulched farmlands (PMFs) is essential for improving both agricultural management and production efficiency. By analyzing the temporal spectral characteristics of PMFs and crop phenological information, we developed a phenology-based plastic-mulched farmland index (PPMFI). This index, when combined with Sentinel-2 imagery and an automated high-precision extraction process via the Google Earth Engine platform, effectively distinguishes PMFs from other land cover types, especially in complex agricultural landscapes. Validation across areas varying in their background complexity and PMF coverage demonstrated that the proposed PPMFI consistently achieves an overall accuracy rate that exceeds 90%, showcasing its robust performance and significantly outperforming other comparative extraction methods. Applying the PPMFI to the Yudong agricultural region of Henan Province, China, further confirmed its capability for large-scale PMF monitoring, thereby offering critical technical support for sustainable agricultural management and environmental protection.

Current research on the relationship between land use change and habitat quality and the driving factors, interactions, and future changes under multi-scenarios remains limited, especially in the ecologically fragile Yellow River Basin (YRB). Using the 2000, 2010, and 2020 land use data, we coupled FLUS with the InVEST model to analyze the relationship between land use change and habitat quality and predict habitat quality under 4 development scenarios. The driving factors were quantitatively identified by their interactive effects by utilizing Geodetector. The results showed that: (1) Overall, habitat quality of the YRB was moderate with an improving trend but exhibited significant spatial heterogeneity. The spatial distribution of habitat quality aligned closely with land use patterns. Habitat degradation showed a spatial pattern of “central-high, peripheral-low; eastern-high, westernlow”. (2) Land use was the primary driver of habitat quality differentiation (q > 0.8), with population density and GDP gaining influence. Factor interactions, especially between land use and other variables, exceeded individual effects. (3) Under the ecological space priority scenario, habitat quality improves significantly, especially in fragile zones where policy interventions are effective. Conversely, the production space priority scenario risks habitat degradation, necessitating measures to mitigate ecological pressure caused by industrial mining and urban expansion.