Temporal Gap Filling of Nighttime Light Composites
DOI:
https://doi.org/10.58825/jog.2025.19.1.152Keywords:
Nighttime light, DMSP-OLS, Deep Learning, LSTMAbstract
The temporal nighttime light (NTL) data generated by DMSP-OLS sensors was discovered to have large gaps (missing values) over time. The research aims to provide a scientifically valid gap-filling mechanism for having consistent DMPS-OLS time series data (1992-2013) and predicting the historic NTL (1991-1985) for long-term studies. A deep learning neural network, Long Short Term Memory (LSTM) has been proposed in the study for temporal gap filling and historic NTL prediction. The developed LSTM model is being tested in a time distributed wrapper way having window size (3-7) for the temporal gap filling and prediction of the historic NTL. According to the accuracy evaluation, the developed model has a testing accuracy of R2 = 0.96 with a window size of 5. The historic population, Gross Domestic Product (GDP), and Electric Power Consumption per capita (EPC) data are utilized to validate the gap-filled and historic NTL. R2 = 0.91 w.r.t population, R2 = 0.71 w.r.t GDP, and R2 = 0.69 w.r.t EPC, is been found during the assessment of these parameters with the sum of light of the year (1985-2013). The historic & gap-filled NTL data can be used in various studies to monitor temporal development.
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