The VIIRS nighttime lights dataset constitutes progress in the measurement of night lights radiance, with monthly data at a pixel of roughly 0.5km × 0.5km. We identify a downward bias in the reported radiance when the number of cloud-free images in a month is low. This bias often takes on large values from -10% to -30%. We develop a cautious bias-correction scheme which partially addresses this problem. This scheme is applied upon the pixel-level dataset to create an improved dataset. The bias-corrected data hews closer to the ground truth as seen in household survey data.
Citation: But clouds got in my way: Bias and bias correction of VIIRS nighttime lights data in the presence of clouds, Ayush Patnaik, Ajay Shah, Anshul Tayal, Susan Thomas, xKDR Working Paper 7, October 2021.
Paper talk |
Reproducible research |
A package that implements these methods |
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A 20-minute paper talk that has the gist of the idea. xKDR Forum YouTube Channel,6th October 2021 |
A 5-minute walk through of the reproducible research included in the paper. xKDR Forum YouTube Channel,8th October 2021 |
Our paper does two things: The first open-source implementation of conventional cleaning methods, and a new bias-correction scheme. Both these are implemented in this Julia package. This video is an introduction to this package. xKDR Forum YouTube Channel,7th October 2021 |
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The pros and cons of big data used as economic signals
by Niranjan Rajadhyaksha |
Mint, 12th January 2022 |
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But clouds got in my way: Bias and bias correction of nighttime lights data in the presence of clouds by Ayush Patnaik, Ajay Shah, Anshul Tayal, Susan Thomas |
The Leap Blog, 11th October 2021 |
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Webinar on Measuring economic activities from outer space by Ayush Patnaik |
Department of Finance and Business Economics, Delhi University, YouTube, 15th September 2021 |