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There exists a spatial mismatch between socioeconomic data, such as Gross Domestic Product (GDP), and physical and environmental datasets. This study provides a dasymetric approach for GDP estimation at a fine scale by combining the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) nighttime imagery, enhanced vegetation index (EVI), and land cover data. Despite the advantages of DMSP/OLS nighttime imagery in estimating human activities, its drawbacks, including coarse resolution, overglow, and saturation effects, limit its application. Hence, high-resolution EVI data were integrated with DMSP/OLS in this study to create a Human Settlement Index (HSI) for estimating the GDP of secondary and tertiary industries. The GDP of the primary industry was then estimated on the basis of land cover data, and the area with the GDP of the primary industry was classified by a threshold technique (DN ≤ 8). The regression model for GDP distribution estimation was implemented in Zhejiang Province in southeast China, and a GDP density map was generated at a resolution of 250 m × 250 m. Compared with the outcome of taking DMSP/OLS as a unique parameter, estimation errors obviously decreased. This study offers a low-cost and accurate approach for rapidly estimating  high-resolution GDP distribution to construct an important database for the government when formulating developmental strategies

Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A Case Study in Zhejiang Province, East China