Preliminary Feasibility of a Single-Channel Nighttime Cloud Detection in Artificially Lit Regions Using Ground Light Source Observations from VIIRS/DNB Images.
Saved in:
| Title: | Preliminary Feasibility of a Single-Channel Nighttime Cloud Detection in Artificially Lit Regions Using Ground Light Source Observations from VIIRS/DNB Images. |
|---|---|
| Authors: | Chen, Mingyu1 (AUTHOR), Hu, Shensen1,2,3 (AUTHOR) hushensen18@nudt.edu.cn, Li, Haoran3,4 (AUTHOR), Ma, Shuo1,4 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1956. 25p. |
| Subjects: | Light scattering, Random forest algorithms, Radar, Remote sensing, Machine learning |
| Abstract: | Highlights: What are the main findings? A novel single-channel nighttime cloud detection algorithm was developed using VIIRS/DNB to leverage artificial light scattering under moonless conditions. Validation against millimeter-wave cloud radar confirms the Random Forest model achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) on 97 rigorously synchronized independent test samples. What are the implications of the main findings? The method effectively overcomes traditional thermal infrared limitations in detecting low clouds with minimal surface temperature contrast. This approach provides a reliable, complementary technique for nighttime monitoring, particularly optimized for urban and artificially lit regions. Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A novel single-channel nighttime cloud detection algorithm was developed using VIIRS/DNB to leverage artificial light scattering under moonless conditions. Validation against millimeter-wave cloud radar confirms the Random Forest model achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) on 97 rigorously synchronized independent test samples. What are the implications of the main findings? The method effectively overcomes traditional thermal infrared limitations in detecting low clouds with minimal surface temperature contrast. This approach provides a reliable, complementary technique for nighttime monitoring, particularly optimized for urban and artificially lit regions. Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121956 |
