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Continuous monitoring of nighttime light changes based on daily NASA's Black Marble product suite
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  • Tian Li,
  • Zhe Zhu,
  • Zhuosen Wang,
  • Miguel Román,
  • Virginia Kalb,
  • Yongquan Zhao
Tian Li
University of Connecticut, University of Connecticut

Corresponding Author:tianli@uconn.edu

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Zhe Zhu
University of Connecticut, University of Connecticut
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Zhuosen Wang
University of Maryland, University of Maryland
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Miguel Román
Leidos Civil Group, Leidos Civil Group
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Virginia Kalb
NASA Goddard Space Flight Center, NASA Goddard Space Flight Center
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Yongquan Zhao
University of Connecticut
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Monitoring nighttime light (NTL) change enables us to quantitatively analyze the dynamic patterns of human activity and socioeconomic features. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) atmospheric- and Lunar-BRDF-corrected Black Marble product (VNP46A2) provides daily global nighttime radiances with high temporal consistency. However, timely and continuous monitoring of NTL changes based on the dense daily DNB time series is still lacking. In this study, we proposed a novel Viewing Zenith Angle (VZA) stratified COntinuous monitoring of Land Disturbance (COLD) algorithm (VZA-COLD) to detect NTL change at 15 arc-second spatial resolution with daily updating capability based on NASA’s Black Marble products. Specifically, we divided the clear observations into four VZA intervals (0–20°, 20°–40°, 40°–60°, and 0–60°) to mitigate the temporal variation of the NTL data caused by the combined angular effects of viewing geometry and the complex surface conditions (e.g., building heights, vegetation canopy covers, etc.). Single-term harmonic models were continuously estimated for new observations from each VZA interval, and by comparing the model predictions with the actual DNB observations, a unified set of NTL changes can be captured continuously among the different VZA intervals. The final NTL change maps were generated after excluding the consistent dark pixels. Results showed that the VZA-COLD algorithm reduced the DNB data temporal variations caused by disparities among different viewing angles and surface conditions, and successfully detected NTL changes for six globally distributed test sites with an overall accuracy of 99.71%, a user’s accuracy of 87.18%, and a producer’s accuracy of 68.88% for the NTL change category.
Dec 2022Published in Remote Sensing of Environment volume 282 on pages 113269. 10.1016/j.rse.2022.113269