Modeling and Prediction of NPP-VIIRS Nighttime Light Imagery Based on Spatiotemporal Statistical Method

The cloud-free monthly composite of the global nighttime light (NTL) data derived from the Suomi National Polar orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) has gained popularity for detecting anthropogenic and socioeconomic activities. However, the monthly VIIRS DNB composite suffers from a data missing problem induced by continuous cloud cover. The full potential of the VIIRS DNB time series is consequently hindered by low-quality and missing observations. This article proposes a spatiotemporal statistical method (STSM) to predict the VIIRS DNB imagery in severe absence of valid observations' situation. The polynomial with the harmonic model was applied to describe the long-term trends and seasonal cycles in time series. A spatial marginal semivariogram was established to quantify the data dependence in space; we then used spatial interpolation to correct the predicted results from temporal curve fitting. The final predicted values were validated with the actual values based on cross-validation. The results suggest that the STSM is suitable for predicting with a high coefficient of determination ( R2 = 0.922) and a relatively low root-mean-square error (RMSE = 3.40 nW/cm2/sr). We extended the proposed method to forecast future imagery for a five-month period, the performance of which was more stable, with the highest R2/RMSE (0.158 +/- 0.010), compared with two other methods. Therefore, the STSM is effective and stable for modeling and predicting the VIIRS DNB monthly composite and will help address the data missing issue.

Automated summary

This article proposes a spatiotemporal statistical method to predict VIIRS DNB imagery in severe absence of valid observations. The method performs well with high coefficient of determination and relatively low root-mean-square error, demonstrating strong capability in addressing data missing issues.