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Accurate estimation of gross domestic product (GDP) at small geographies is of great significance to evaluate the distribution and dynamics of socio-economic development. Nighttime light (NTL) data is becoming increasingly important in socio-economic data estimation. However, previous research has found that using NTL alone is insufficient to accurately measure the GDP at small geographies, and the contribution of NTL for time-series GDP estimation is unreliable. This article proposed a deep learning method for the Contiguous United States (CONUS) time-series (2012-2015) GDP estimation at county level. The model is developed by combining the NTL data from the visible infrared imaging radiometer suite day/night band and the MODIS land cover data. The proposed method can improve the existing methods mainly in two ways. First, by taking advantage of the great computing power of the Google Earth Engine, a histogram-based feature regulation method was employed, which not only keeps more information over regions but also provides dimension-reduced tensors from mass remote sensing data. Second, a multi-inputs convolutional neural network-based model was proposed instead of the traditional linear regression model for multisource feature exploration and learning. The proposed method was evaluated by leave-one-year-out cross-validation with the time-series (2012-2015) data. The results show that the R<sup>2</sup> between the actual and estimated GDP are 0.81, 0.83, 0.83, and 0.83 for years from 2012 to 2015, indicating a good predictive power of the proposed model. Given that the data employed are globally and publicly available, the proposed method would also be applicable in other countries or regions where socio-economic survey data is hard to obtain.

Estimation of GDP Using Deep Learning With NPP-VIIRS Imagery and Land Cover Data at the County Level in CONUS