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Articles | Volume XLIX-B4-2026
https://doi.org/10.5194/isprs-archives-XLIX-B4-2026-619-2026
https://doi.org/10.5194/isprs-archives-XLIX-B4-2026-619-2026
04 Aug 2026
 | 04 Aug 2026

Leveraging SDGSAT-1 data for exploring the interactions of nighttime lights and human settlement structure at high spatial resolution

Johannes H. Uhl, Martino Pesaresi, Michele Melchiorri, Panagiotis Politis, Linlin Lu, and Thomas Kemper

Keywords: Sustainable Development Goals, Global Human Settlement Layer, Glimmer Imager for Urbanization, Nighttime light data compositing, Copernicus Exposure Mapping Component

Abstract. Nighttime light (NTL) Earth observation data represent an invaluable resource for measuring population distribution, disaster impact, economic activity, and socio-economic inequalities from space. Traditional NTL data sources are spatially coarse, impeding detailed analyses of nighttime lights. Novel, high-resolution NTL data from the SDGSAT-1 satellite capture NTL variations at fine spatial detail of 10 to 40 m and open new research avenues. Herein, we demonstrate the potential of jointly assessing annual SDGSAT-1 composites and human settlement data from the Global Human Settlement Layer (GHSL) and other data, characterizing the built environment, human population distribution, and the rural-urban continuum. For a study area in Northern Italy, we illustrate that such data integration generates new insights on the interactions of nighttime lights and settlement-related characteristics at unprecedented detail. For example, we find that NTL emissions tend to be highest in old parts of settlements (<1975) and lowest in very recently developed land. The brightness of non-residential areas at night approximately doubles, on average, compared to residential built-up areas. Similarly, main roads in urban settings tend to be twice as bright as residential built-up areas, while this difference largely disappears towards rural settlements. Moreover, ~80% of urban population resides in areas characterized by luminous, stationary NTL, while this population share drops to ~15% in very rural areas. Looking at infrastructure-related land use, we find that airports emit the highest levels of stationary and non-stationary NTL in our study area. These results illustrate the potential of SDGSAT-1 data for settlement analysis and population modelling.

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