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Bennett, M. M., & Smith, L. C. (2017). Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sensing of Environment, 192, 176–197.
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Cao, X., Chen, J., Imura, H., & Higashi, O. (2009). A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data. Remote Sensing of Environment, 113(10), 2205–2209.
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Cao, X., Hu, Y., Zhu, X., Shi, F., Zhuo, L., & Chen, J. (2019). A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images. Remote Sensing of Environment, 224, 401–411.
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Elvidge, C. D., Baugh, K. E., Dietz, J. B., Bland, T., Sutton, P. C., & Kroehl, H. W. (1999). Radiance calibration of DMSP-OLS low-light imaging data of human settlements. Remote Sensing of Environment, 68(1), 77–88.
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Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., et al. (2020). Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, in press.
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Imhoff, M. (1997). Using nighttime DMSP/OLS images of city lights to estimate the impact of urban land use on soil resources in the United States. Remote Sensing of Environment, 59(1), 105–117.
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Katz, Y., & Levin, N. (2016). Quantifying urban light pollution -- A comparison between field measurements and EROS-B imagery. Remote Sensing of Environment, 177, 65–77.
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Kotarba, A. Z., & Aleksandrowicz, S. (2016). Impervious surface detection with nighttime photography from the International Space Station. Remote Sensing of Environment, 176, 295–307.
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Kuechly, H. U., Kyba, C. C. M., Ruhtz, T., Lindemann, C., Wolter, C., Fischer, J., et al. (2012). Aerial survey and spatial analysis of sources of light pollution in Berlin, Germany. Remote Sensing of Environment, 126, 39–50.
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L.Imhoff, M., Lawrence, W. T., Stutzer, D. C., & Elvidge, C. D. (1997). A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area. Remote Sensing of Environment, 61(3), 361–370.
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