Remote Sensing-Based Synergistic Analysis of Urban Flood Monitoring and Green Infrastructure

Urban expansion, together with an increasing frequency of extreme rainfall, has significantly raised both the occurrence and the economic losses of urban flooding. Green infrastructure (GI), the backbone of low-impact development (LID), is widely regarded as capable of reducing surface runoff, delaying peak discharge, and strengthening the resilience of urban drainage. Yet a unified and transferable framework for quantifying, spatiotemporally linking, and scaling the flood-mitigation effects of “green space–flood” interactions is still lacking. This paper reviews recent progress in the use of remote-sensing techniques for urban flood monitoring and in the coupled analysis between these observations and the spatial configuration of GI. We first outline the key optical and SAR approaches for mapping water bodies and inundated areas—NDWI/MNDWI, threshold-based segmentation, and deep-learning semantic segmentation—and then summarize the indices and metrics commonly used to identify and characterize urban green space (NDVI, EVI, landscape-pattern indices, connectivity, etc.). We detail how these green-space layers are overlaid with flood extents, examined through buffer statistics, and synthesized into a Green-space Flood-Mitigation Index (GFMI). Next, we dissect three persistent challenges: scale mismatch, SAR misclassification in dense built-up areas, and the difficulty of attributing flood reduction to GI alone. Finally, we advocate an integrated “remote sensing–3-D urban/drainage model–machine learning” approach, emphasizing the need to feed remote-sensing-derived metrics directly into urban green-space planning, sponge-city design, and coupled watershed–urban management.