Nighttime light remote sensing for urban applications: Progress, challenges, and prospects
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Yuyu Zhou | K. Seto | Qihao Weng | Qiming Zheng | Shixue You
[1] Biyun Guo,et al. Potentiality of SDGSAT-1 glimmer imagery to investigate the spatial variability in nighttime lights , 2023, Int. J. Appl. Earth Obs. Geoinformation.
[2] T. Long,et al. Modelling the Public Perception of Urban Public Space Lighting Based on SDGSAT-1 Glimmer Imagery , 2022, Sustainable Cities and Society.
[3] R. Goldblatt,et al. Fifty years of nightly global low-light imaging satellite observations , 2022, Frontiers in Remote Sensing.
[4] Gabriel Cadamuro,et al. The electricity scene from above: Exploring power grid inconsistencies using satellite data in Accra, Ghana , 2022, Applied Energy.
[5] Jun Zhang,et al. Extraction of Urban Built-Up Area Based on Deep Learning and Multi-Sources Data Fusion—The Application of an Emerging Technology in Urban Planning , 2022, Land.
[6] Tingting Li,et al. Dynamic Characteristics of Urbanization Based on Nighttime Light Data in China’s “Plain–Mountain Transition Zone” , 2022, International journal of environmental research and public health.
[7] Zhipeng Tang,et al. City-level carbon emissions accounting and differentiation integrated nighttime light and city attributes , 2022, Resources, Conservation and Recycling.
[8] Yuyu Zhou,et al. Impact of temporal compositing on nighttime light data and its applications , 2022, Remote Sensing of Environment.
[9] F. Sun,et al. Global gridded GDP data set consistent with the shared socioeconomic pathways , 2022, Scientific data.
[10] Eleanor C. Stokes,et al. Artificial light at night: an underappreciated effect on phenology of deciduous woody plants , 2022, PNAS nexus.
[11] Deren Li,et al. Using radiant intensity to characterize the anisotropy of satellite-derived city light at night , 2022, Remote Sensing of Environment.
[12] G. McCord,et al. Nightlights and Subnational Economic Activity: Estimating Departmental GDP in Paraguay , 2022, Remote. Sens..
[13] J. Tao,et al. Association of exposure to artificial light at night with atopic diseases: A cross-sectional study in college students. , 2022, International journal of hygiene and environmental health.
[14] Jin Chen,et al. Modeling the direction and magnitude of angular effects in nighttime light remote sensing , 2022, Remote Sensing of Environment.
[15] W. Cheng,et al. Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China , 2022, Remote. Sens..
[16] Zhi-feng Wu,et al. Identifying China’s polycentric cities and evaluating the urban centre development level using Luojia-1A night-time light data , 2022, Ann. GIS.
[17] C. Elvidge,et al. Extending the DMSP Nighttime Lights Time Series beyond 2013 , 2021, Remote. Sens..
[18] R. Fensholt,et al. The impact of conflict-driven cropland abandonment on food insecurity in South Sudan revealed using satellite remote sensing , 2021, Nature Food.
[19] Feng Lu,et al. Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery , 2021 .
[20] Eleanor C. Stokes,et al. Tracking COVID-19 urban activity changes in the Middle East from nighttime lights , 2021, Scientific Reports.
[21] Miao Liu,et al. High-resolution mapping of mainland China’s urban floor area , 2021 .
[22] Lina Tang,et al. An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI , 2021, Journal of Cleaner Production.
[23] Adugna G. Mullissa,et al. Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series , 2021 .
[24] Eleanor C. Stokes,et al. Retired satellites: A chance to shed light , 2021, Science.
[25] Zhuosen Wang,et al. Quantifying uncertainties in nighttime light retrievals from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data , 2021 .
[26] Dénes Száz,et al. Measurements and Modelling of Aritificial Sky Brightness: Combining Remote Sensing from Satellites and Ground-Based Observations , 2021, Remote. Sens..
[27] K. Błażejczyk,et al. Characteristics of light pollution - A case study of Warsaw (Poland) and Fukuoka (Japan). , 2021, Environmental pollution.
[28] Yunming Ye,et al. An Investigation on Deep Learning Approaches to Combining Nighttime and Daytime Satellite Imagery for Poverty Prediction , 2021, IEEE Geoscience and Remote Sensing Letters.
[29] J. Bennie,et al. Colour remote sensing of the impact of artificial light at night (II): Calibration of DSLR-based images from the International Space Station , 2021, 2108.07050.
[30] M. Aubé,et al. Modeling the Spectral Properties of Obtrusive Light Incident on a Window: Application to Montréal, Canada , 2021, Remote. Sens..
[31] Kaifang Shi,et al. Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data , 2021, Int. J. Digit. Earth.
[32] Xi Li,et al. Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities , 2021, International Journal of Applied Earth Observation and Geoinformation.
[33] K. Fristrup,et al. Changes in night sky brightness after a countywide LED retrofit. , 2021, Journal of environmental management.
[34] Xin Huang,et al. Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery , 2021 .
[35] R. M. Guido,et al. Presence of Light Pollution as a Latent Anthropogenic Influence of Bat Dispersal in Mindanao, Philippines , 2021, Indian Journal of Science and Technology.
[36] Zehao Shen,et al. Comparing Luojia 1-01 and VIIRS Nighttime Light Data in Detecting Urban Spatial Structure Using a Threshold-Based Kernel Density Estimation , 2021, Remote. Sens..
[37] Qihao Weng,et al. Characterizing urban land changes of 30 global megacities using nighttime light time series stacks , 2021 .
[38] Lian Pin Koh,et al. Artificial Light at Night Advances Spring Phenology in the United States , 2021, Remote. Sens..
[39] Hanlin Zhou,et al. Linking Luojia 1-01 nightlight imagery to urban crime , 2020 .
[40] Zhenhong Du,et al. An Unsupervised Urban Extent Extraction Method from NPP-VIIRS Nighttime Light Data , 2020, Remote. Sens..
[41] Yuyu Zhou,et al. Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS , 2020 .
[42] Jay Taneja,et al. Indicators of Electric Power Instability from Satellite Observed Nighttime Lights , 2020, Remote. Sens..
[43] Yuyu Zhou,et al. An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration , 2020, Earth System Science Data.
[44] Xiaoling Zhang,et al. Revisiting the environmental Kuznets curve for city-level CO2 emissions: based on corrected NPP-VIIRS nighttime light data in China , 2020, Journal of Cleaner Production.
[45] Deren Li,et al. Monitoring hourly night-time light by an unmanned aerial vehicle and its implications to satellite remote sensing , 2020 .
[46] E. Mayo-Wilson,et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews , 2020, BMJ.
[47] Mikhail Zhizhin,et al. The Dimming of Lights in China during the COVID-19 Pandemic , 2020, Remote. Sens..
[48] Yuyu Zhou,et al. A harmonized global nighttime light dataset 1992–2018 , 2020, Scientific Data.
[49] Yi Qiang,et al. Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster , 2020 .
[50] Karen C. Seto,et al. A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change , 2020 .
[51] Michael Jendryke,et al. Mapping urban expansion using night-time light images from Luojia1-01 and International Space Station , 2020 .
[52] Yong Wang,et al. Urban Nighttime Leisure Space Mapping with Nighttime Light Images and POI Data , 2020, Remote. Sens..
[53] J. Spinelli,et al. Outdoor light at night at residences and breast cancer risk in Canada , 2020, European Journal of Epidemiology.
[54] Bin Cheng,et al. Automated Extraction of Street Lights From JL1-3B Nighttime Light Data and Assessment of Their Solar Energy Potential , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[55] C. Elvidge,et al. Remote sensing of night lights: A review and an outlook for the future , 2020, Remote Sensing of Environment.
[56] G. Cao,et al. Time series analysis of VIIRS-DNB nighttime lights imagery for change detection in urban areas: A case study of devastation in Puerto Rico from hurricanes Irma and Maria , 2019 .
[57] Karen C. Seto,et al. Characterizing urban infrastructural transitions for the Sustainable Development Goals using multi-temporal land, population, and nighttime light data , 2019 .
[58] K. Seto,et al. Projecting global urban land expansion and heat island intensification through 2050 , 2019, Environmental Research Letters.
[59] Mark R. Pickering,et al. A Nighttime Lights Adjusted Impervious Surface Index (NAISI) with Integration of Landsat Imagery and Nighttime Lights Data from International Space Station , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[60] Na Ta,et al. Delineating Seasonal Relationships Between Suomi NPP-VIIRS Nighttime Light and Human Activity Across Shanghai, China , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[61] G. A. Abubakar,et al. Mapping the fine-scale spatial pattern of artificial light pollution at night in urban environments from the perspective of bird habitats. , 2019, The Science of the total environment.
[62] Lingfei Shi,et al. Night-time lights are more strongly related to urban building volume than to urban area , 2019, Remote Sensing Letters.
[63] M. Imran,et al. Spatial distribution and opportunity mapping: Applicability of evidence-based policy implications in Punjab using remote sensing and global products , 2019, Sustainable Cities and Society.
[64] F. Stevens,et al. Evaluating nighttime lights and population distribution as proxies for mappinganthropogenic CO2 emission in Vietnam, Cambodia and Laos , 2019, IOP conference series. Materials science and engineering.
[65] Chenghu Zhou,et al. Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives , 2019, Remote. Sens..
[66] Qiuxiao Chen,et al. Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou , 2019, Remote. Sens..
[67] Liang Cheng,et al. Automated Extraction of Built-Up Areas by Fusing VIIRS Nighttime Lights and Landsat-8 Data , 2019, Remote. Sens..
[68] Qiming Zheng,et al. Developing a new cross-sensor calibration model for DMSP-OLS and Suomi-NPP VIIRS night-light imageries , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.
[69] Zhe Zhu,et al. Understanding an urbanizing planet: Strategic directions for remote sensing , 2019, Remote Sensing of Environment.
[70] Rasmus Fensholt,et al. Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017 , 2019, Remote Sensing of Environment.
[71] Peng Fu,et al. Temporal variations of artificial nighttime lights and their implications for urbanization in the conterminous United States, 2013–2017 , 2019, Remote Sensing of Environment.
[72] Cecilia Nilsson,et al. Bright lights in the big cities: migratory birds’ exposure to artificial light , 2019, Frontiers in Ecology and the Environment.
[73] Kevin J. Gaston,et al. Colour remote sensing of the impact of artificial light at night (I): The potential of the International Space Station and other DSLR-based platforms , 2019, Remote Sensing of Environment.
[74] Gorden Videen,et al. Night-sky radiometry can revolutionize the characterization of light-pollution sources globally , 2019, Proceedings of the National Academy of Sciences.
[75] Michael Jendryke,et al. A preliminary investigation of Luojia-1 night-time light imagery , 2019, Remote Sensing Letters.
[76] Zhizhu Lai,et al. Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets , 2019, Applied Energy.
[77] Yuyu Zhou,et al. A global record of annual urban dynamics (1992–2013) from nighttime lights , 2018, Remote Sensing of Environment.
[78] A. Knudby,et al. Mapping ambient light at night using field observations and high-resolution remote sensing imagery for studies of urban environments , 2018, Building and Environment.
[79] Xi Li,et al. Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery , 2018, Sensors.
[80] Xiao Xiang Zhu,et al. Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets , 2018, Remote. Sens..
[81] Guojin He,et al. Potentiality of Using Luojia 1-01 Nighttime Light Imagery to Investigate Artificial Light Pollution , 2018, Sensors.
[82] Qihao Weng,et al. A new source of multi-spectral high spatial resolution night-time light imagery—JL1-3B , 2018, Remote Sensing of Environment.
[83] Guohua Liu,et al. Urban Land Extraction Using DMSP/OLS Nighttime Light Data and OpenStreetMap Datasets for Cities in China at Different Development Levels , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[84] G. di Baldassarre,et al. Nighttime light data reveal how flood protection shapes human proximity to rivers , 2018, Science Advances.
[85] Binbin Guo,et al. Analysis of Lighting Changes in the Tourist City Edogawa Using Nighttime Light Data , 2018, Journal of the Indian Society of Remote Sensing.
[86] Ting Ma,et al. Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social Media-Derived Human Population Dynamics , 2018, Remote. Sens..
[87] Hongwei Xu,et al. Delineating Urban Boundaries Using Landsat 8 Multispectral Data and VIIRS Nighttime Light Data , 2018, Remote. Sens..
[88] Chen Peng,et al. Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data , 2018, IEEE Geoscience and Remote Sensing Letters.
[89] Xiaolan Li,et al. Mapping nighttime PM2.5 from VIIRS DNB using a linear mixed-effect model , 2018 .
[90] Xiaolin Zhu,et al. Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions , 2018, Remote. Sens..
[91] Eleanor C. Stokes,et al. NASA's Black Marble nighttime lights product suite , 2018, Remote Sensing of Environment.
[92] Ke Wang,et al. Monitoring the trajectory of urban nighttime light hotspots using a Gaussian volume model , 2018, Int. J. Appl. Earth Obs. Geoinformation.
[93] T. Smyth,et al. Why artificial light at night should be a focus for global change research in the 21st century , 2018, Global change biology.
[94] Robert C. Balling,et al. Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover , 2018 .
[95] A. Irwin. The dark side of light: how artificial lighting is harming the natural world , 2018, Nature.
[96] Demetris Stathakis,et al. Seasonal population estimates based on night-time lights , 2017, Comput. Environ. Urban Syst..
[97] Bo Huang,et al. Using multi-source geospatial big data to identify the structure of polycentric cities , 2017 .
[98] C. Elvidge,et al. VIIRS night-time lights , 2017, Remote Sensing of Night-time Light.
[99] L. Guanter,et al. Artificially lit surface of Earth at night increasing in radiance and extent , 2017, Science Advances.
[100] Deren Li,et al. Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during Syrian Civil War , 2017 .
[101] Jeffrey J. Buler,et al. Seasonal associations with urban light pollution for nocturnally migrating bird populations , 2017, Global change biology.
[102] Wenli Xiang,et al. Changes in Light Pollution and the Causing Factors in China's Protected Areas, 1992-2012 , 2017, Remote. Sens..
[103] Andreas Jechow,et al. Measuring night sky brightness: methods and challenges , 2017, 1709.09558.
[104] Boris A. Portnov,et al. Estimating geographic concentrations of quaternary industries in Europe using Artificial Light-At-Night (ALAN) data , 2017, Int. J. Digit. Earth.
[105] X. Cui,et al. A new global anthropogenic heat estimation based on high-resolution nighttime light data , 2017, Scientific Data.
[106] Shaojian Wang,et al. China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces , 2017 .
[107] Guojin He,et al. Ongoing Conflict Makes Yemen Dark: From the Perspective of Nighttime Light , 2017, Remote. Sens..
[108] Ning Zhang,et al. Feasibility of a new-generation nighttime light data for estimating in-use steel stock of buildings and civil engineering infrastructures , 2017 .
[109] Wei Song,et al. A New Approach for Detecting Urban Centers and Their Spatial Structure With Nighttime Light Remote Sensing , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[110] Karen C. Seto,et al. Comparative evaluation of relative calibration methods for DMSP/OLS nighttime lights , 2017 .
[111] Noam Levin,et al. The impact of seasonal changes on observed nighttime brightness from 2014 to 2015 monthly VIIRS DNB composites , 2017 .
[112] M. Bennett,et al. Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics , 2017 .
[113] Xiaohua Tong,et al. Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas , 2017, Remote. Sens..
[114] Zhifeng Liu,et al. Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods , 2017, Remote. Sens..
[115] Xiaoping Liu,et al. Analyzing Parcel-Level Relationships between Urban Land Expansion and Activity Changes by Integrating Landsat and Nighttime Light Data , 2017, Remote. Sens..
[116] Jiaguo Qi,et al. Spatially Explicit Mapping of Heat Health Risk Utilizing Environmental and Socioeconomic Data. , 2017, Environmental science & technology.
[117] Yuyu Zhou,et al. Urban mapping using DMSP/OLS stable night-time light: a review , 2017, Remote Sensing of Night-time Light.
[118] Guofeng Cao,et al. Forecasting China’s GDP at the pixel level using nighttime lights time series and population images , 2017 .
[119] Bailang Yu,et al. Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data , 2016 .
[120] P. McGregor,et al. Light pollution is associated with earlier tree budburst across the United Kingdom , 2016, Proceedings of the Royal Society B: Biological Sciences.
[121] X. Sala-i-Martin,et al. Lights, Camera … Income! Illuminating the National Accounts-Household Surveys Debate , 2016 .
[122] Noam Levin,et al. Quantifying urban light pollution — A comparison between field measurements and EROS-B imagery , 2016 .
[123] Xia Li,et al. A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area , 2015, Remote. Sens..
[124] Bin Jiang,et al. Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges , 2015, ArXiv.
[125] Yaping Yang,et al. Mapping Urban Areas with Integration of DMSP/OLS Nighttime Light and MODIS Data Using Machine Learning Techniques , 2015, Remote. Sens..
[126] Eleanor C. Stokes,et al. Holidays in lights: Tracking cultural patterns in demand for energy services , 2015, Earth's future.
[127] A. Thomson,et al. A global map of urban extent from nightlights , 2015 .
[128] Xiangnan Liu,et al. Estimation of the PM2.5 Pollution Levels in Beijing Based on Nighttime Light Data from the Defense Meteorological Satellite Program-Operational Linescan System , 2015 .
[129] Kevin J. Gaston,et al. The biological impacts of artificial light at night: the research challenge , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.
[130] Xi Chen,et al. A Test of the New VIIRS Lights Data Set: Population and Economic Output in Africa , 2015, Remote. Sens..
[131] Maria Francisca Archila Bustos,et al. Nighttime lights and population changes in Europe 1992–2012 , 2015, Ambio.
[132] Alison J. Fairbrass,et al. The ecological impact of city lighting scenarios: exploring gap crossing thresholds for urban bats , 2015, Global change biology.
[133] Richard Inger,et al. Worldwide variations in artificial skyglow , 2015, Scientific Reports.
[134] Christopher D. Elvidge,et al. DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration , 2015, Remote. Sens..
[135] Wen Wang,et al. Estimating Land Development Time Lags in China Using DMSP/OLS Nighttime Light Image , 2015, Remote. Sens..
[136] Wei Song,et al. Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China , 2014, Int. J. Geogr. Inf. Sci..
[137] Stuart R. Phinn,et al. A new source for high spatial resolution night time images — The EROS-B commercial satellite , 2014 .
[138] A. Thomson,et al. A cluster-based method to map urban area from DMSP/OLS nightlights , 2014 .
[139] Jianping Wu,et al. Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas , 2014 .
[140] P. Reynolds,et al. A cross-sectional analysis of light at night, neighborhood sociodemographics and urinary 6-sulfatoxymelatonin concentrations: implications for the conduct of health studies , 2013, International Journal of Health Geographics.
[141] C. Elvidge,et al. Night on Earth: Mapping decadal changes of anthropogenic night light in Asia , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[142] Sara E. Wagner,et al. A case-referent study: light at night and breast cancer risk in Georgia , 2013, International Journal of Health Geographics.
[143] Xiaoling Chen,et al. Satellite-Observed Nighttime Light Variation as Evidence for Global Armed Conflicts , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[144] K. Seto,et al. The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .
[145] Christian Wolter,et al. Aerial survey and spatial analysis of sources of light pollution in Berlin, Germany , 2012 .
[146] Italy,et al. The propagation of light pollution in the atmosphere , 2012, 1209.2031.
[147] Zhifeng Liu,et al. Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .
[148] José A. Sobrino,et al. Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects , 2012 .
[149] C. Elvidge,et al. Limiting the impact of light pollution on human health, environment and stellar visibility. , 2011, Journal of environmental management.
[150] K. Seto,et al. Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data , 2011 .
[151] K. Seto,et al. A Meta-Analysis of Global Urban Land Expansion , 2011, PloS one.
[152] W. Nordhaus,et al. Using luminosity data as a proxy for economic statistics , 2011, Proceedings of the National Academy of Sciences.
[153] Xiaolin Zhu,et al. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions , 2010 .
[154] Christopher Doll,et al. Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery , 2010 .
[155] Christopher D. Elvidge,et al. Spectral Identification of Lighting Type and Character , 2010, Sensors.
[156] Ludo Waltman,et al. Software survey: VOSviewer, a computer program for bibliometric mapping , 2009, Scientometrics.
[157] Osamu Higashi,et al. A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data , 2009 .
[158] Budhendra L. Bhaduri,et al. A global poverty map derived from satellite data , 2009, Comput. Geosci..
[159] J. Henderson,et al. Measuring Economic Growth from Outer Space , 2009, The American economic review.
[160] Zhao-Liang Li,et al. Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling , 2009, Sensors.
[161] 이재윤,et al. 계량서지적 네트워크 분석을 위한 중심성 척도에 관한 연구 , 2006 .
[162] C. Elvidge,et al. Spatial analysis of global urban extent from DMSP-OLS night lights , 2005 .
[163] P. Gong,et al. Validation of urban boundaries derived from global night-time satellite imagery , 2003 .
[164] Boulder,et al. The first World Atlas of the artificial night sky brightness , 2001, astro-ph/0108052.
[165] J. Muller,et al. Night-time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions , 2000 .
[166] C. Elvidge,et al. Mapping City Lights With Nighttime Data from the DMSP Operational Linescan System , 1997 .
[167] C. Elvidge,et al. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption , 1997 .
[168] R. H. Garstang,et al. MODEL FOR ARTIFICIAL NIGHT-SKY ILLUMINATION. , 1984 .
[169] T. Croft. Nighttime Images of the Earth from Space , 1978 .
[170] Xiaolin Zhu,et al. Assessment of a New Fine-Resolution Nighttime Light Imagery From the Yangwang-1 (“Look up 1”) Satellite , 2022, IEEE Geoscience and Remote Sensing Letters.
[171] Zhuosen Wang,et al. NASA’s Black Marble Multiangle Nighttime Lights Temporal Composites , 2022, IEEE Geoscience and Remote Sensing Letters.
[172] Jay Taneja,et al. Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019 , 2021, Remote. Sens..
[173] Xiaobin Guan,et al. Time series remote sensing image classification framework using combination of deep learning and multiple classifiers system , 2021, Int. J. Appl. Earth Obs. Geoinformation.
[174] Ziheng Sun,et al. Estimation of GDP Using Deep Learning With NPP-VIIRS Imagery and Land Cover Data at the County Level in CONUS , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[175] Peijun Li,et al. A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis , 2018 .
[176] F. R. Colomb,et al. SAC-C mission, an example of international cooperation , 2002 .