Abstract
Abstract. Cloud detection for night-time panchromatic visible and near-infrared (VNIR) satellite imagery is typically performed based on synchronized observations in the thermal infrared (TIR). To be independent of TIR and to improve existing algorithms, we realize and analyze cloud detection based on VNIR only, here NPP/VIIRS/DNB observations. Using Random Forest for classifying cloud vs. clear and focusing on urban areas, we illustrate the importance of features describing a) the scattering by clouds especially over urban areas with their inhomogeneous light emissions and b) the normalized differences between Earth’s surface and cloud albedo especially in presence of Moon illumination. The analyses substantiate the influences of a) the training site and scene selections and b) the consideration of single scene or multi-temporal scene features on the results for the test sites. As test sites, diverse urban areas and the challenging land covers ocean, desert, and snow are considered. Accuracies of up to 85% are achieved for urban test sites.
Highlights
The footprint of human activities on Earth is visible at night, because human life and work at night usually involves artificial lighting (Levin et al, 2019)
This is clearly visible in night-time satellite imagery in the visible and near-infrared (VNIR) spectral range from 0.4 μm to 1.1 μm
We study how a feature-based algorithm for semantic segmentation of clouds has to be robustly realized using exclusively night-time observations in the VNIR spectral range with two major objectives: First, to evaluate, if TIR bands are avoidable concerning cloud detection for night-time optical satellite missions focusing on VNIR bands
Summary
The footprint of human activities on Earth is visible at night, because human life and work at night usually involves artificial lighting (Levin et al, 2019). This is clearly visible in night-time satellite imagery in the visible and near-infrared (VNIR) spectral range from 0.4 μm to 1.1 μm. Such data contains wealth of information that is not so explicitly derivable from any other remote sensing product. Analyses of nighttime artificial lighting are used to derive statements regarding its effects on the environment, for example in the form of light pollution affecting wildlife and human health, astronomical observations, and energy consumption
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