Regional Development Assessment at Grid Scale: A Multisource Remote Sensing Approach in Chongqing, China
Regional development disparities remain a persistent global challenge, yet existing assessment approaches often face a trade-off between spatial detail and temporal coverage. Conventional socioeconomic statistics provide relatively reliable information but are typically limited by coarse spatial representation and low update frequency, whereas high-resolution remote sensing-based studies often focus on individual time points, making it difficult to capture the temporal evolution of regional development. Remote sensing observations provide valuable opportunities for regional development assessment by offering extensive spatial coverage and repeated observations over time. To address this gap, this study proposes a multisource remote sensing framework for characterizing the spatiotemporal dynamics of regional development in Chongqing Municipality across four temporal nodes (2014, 2016, 2018, and 2020). We first construct a county-level Development Intensity Index (DII) using socioeconomic indicators derived from statistical data. Subsequently, we integrate nighttime light, DEM, NDVI, and POI data to generate a 500 m gridded Comprehensive Spatial Development Index (CSDI), which captures spatial heterogeneity at a fine spatial scale. The CSDI exhibits strong correspondence with the DII, and its spatial validity is further corroborated through visual interpretation of Google Earth imagery. Results indicate that areas with higher development levels are predominantly concentrated in Chongqing’s central urban core, while less-developed counties are concentrated in the northeastern and southeastern peripheries. Although a general upward trend in development is observed across the study period, notable spatial disparities persist. Overall, the proposed CSDI-based framework offers an effective and replicable approach for gridded regional development assessment, with implications for targeted regional planning and differentiated policy design.