Listed below are known citations to the NASA Socioeconomic Data and Applications Center (SEDAC) Global High Resolution Urban Data from Landsat data collection. The data collection, and specific data set (if known), being cited are beneath each citation. Citations to multiple collections/sets are listed on separate lines. If a publication cites remotely sensed earth observation data, whether from NASA or another source, those instruments and/or platforms are listed as well.
List last updated on 3 October 2023.

Ahmad, M., & Zeeshan, M. (2022). Validation of weather reanalysis datasets and geospatial and techno-economic viability and potential assessment of concentrated solar power plants. Energy Conversion and Management, 256, 115366. doi:10.1016/j.enconman.2022.115366
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
REMOTE SENSING (ALOS Global Digital Surface Model - ALOS World 3D - 30m (AW3D30))
REMOTE SENSING (Landsat)
REMOTE SENSING (Meteosat)

Alamanos, A., & Linnane, S. (2021). Estimating SDG Indicators in data-scarce areas: The transition to the use of new technologies and multidisciplinary studies. Earth, 2(3), 635-652. doi:10.3390/earth2030037
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Bagaria, P., Thapa, A., Sharma, L. K., Joshi, B. D., Singh, H., Sharma, C. M., . . . Chandra, K. (2021). Distribution modelling and climate change risk assessment strategy for rare Himalayan Galliformes species using archetypal data abundant cohorts for adaptation planning. Climate Risk Management, 31, 100264. doi:10.1016/j.crm.2020.100264
Global Agricultural Lands (Cropland) - 10.7927/H4C8276G
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Land Use and Land Cover (LULC) (Development Threat Index, v1) - 10.7927/61jv-th84
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
NASA REMOTE SENSING (MODIS - MOD13Q1)
NASA REMOTE SENSING (SRTM)

Bergs, R., & Budde, R. (2022). The potential of small-scale spatial data in regional science. Review of Regional Research, 42(2), 97-110. doi:10.1007/s10037-022-00172-3
Satellite-Derived Environmental Indicators (Global High Resolution Daily Extreme Urban Heat Exposure (UHE-Daily), v1)

Bhowmik, M., Sarkar, A., & Das, R. (2015, 7-8 Feb. 2015). Shannon entropy based fuzzy distance norm for pixel classification in remote sensing imagery. Paper presented at the Computer, Communication, Control and Information Technology (C3IT), 2015 Third International Conference on.
Global High Resolution Urban Data from Landsat (Cities from Space) - 10.7927/H4SQ8XB1
REMOTE SENSING (Landsat)

Bissiri, M., Moura, P., Figueiredo, N. C., & Pereira da Silva, P. (2020). A geospatial approach towards defining cost-optimal electrification pathways in West Africa. Energy, 200, 117471. doi:10.1016/j.energy.2020.117471
Global Rural-Urban Mapping Project (GRUMP) v1.01 (urban extent)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Carlson, C. J., Kracalik, I. T., Ross, N., Alexander, K. A., Hugh-Jones, M. E., Fegan, M., . . . Blackburn, J. K. (2019). The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife. Nature Microbiology, 4, 1337-1343. doi:10.1038/s41564-019-0435-4
Gridded Population of the World (GPW) v4 (population density)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (AVHRR)

Chang, Y., Xiao, J., Li, X., Middel, A., Zhang, Y., Gu, Z., . . . He, S. (2021). Exploring diurnal thermal variations in urban local climate zones with ECOSTRESS land surface temperature data. Remote Sensing of Environment, 263, 112544. doi:10.1016/j.rse.2021.112544
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (ECOSTRESS LST)
REMOTE SENSING (Landsat)

Chen, B., Song, Y., Huang, B., & Xu, B. (2020). A novel method to extract urban human settlements by integrating remote sensing and mobile phone locations. Science of Remote Sensing, 1, 100003. doi:10.1016/j.srs.2020.100003
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Chen, B., Tu, Y., Song, Y., Theobald, D. M., Zhang, T., Ren, Z., . . . Xu, B. (2021). Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 203-218. doi:10.1016/j.isprsjprs.2021.06.010
Gridded Population of the World (GPW) v4.11 (population count)
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (USDA National Agriculture Imagery Program (NAIP) very high resolution multispectral imagery)
REMOTE SENSING (Sentinel-1 SAR)
REMOTE SENSING (Sentinel-2 Multispectral Imager (MSI))
REMOTE SENSING (VIIRS NTL)

Chen, B., Xu, B., & Gong, P. (2021). Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities. Big Earth Data, 5(3), 410-441. doi:10.1080/20964471.2021.1939243
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)

Chen, D., Shevade, V., Baer, A., He, J., Hoffman-Hall, A., Ying, Q., . . . Loboda, T. V. (2021). A disease control-oriented land Cover land use map for Myanmar. Data, 6(6), 63. doi:10.3390/data6060063
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Cheval, S., Micu, D., Dumitrescu, A., Irimescu, A., Frighenciu, M., Iojă, C., . . . Antonescu, B. (2020). Meteorological and Ancillary Data Resources for Climate Research in Urban Areas. Climate, 8(3), 37. doi:10.3390/cli8030037
Satellite-Derived Environmental Indicators (Global Urban Heat Island (UHI) Data Set, v1)
Global High Resolution Urban Data from Landsat (Cities from Space)
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Di Baldassarre, G., Nardi, F., Annis, A., Odongo, V., Rusca, M., & Grimaldi, S. (2020). Brief communication: Comparing hydrological and hydrogeomorphic paradigms for global flood hazard mapping. Natural Hazards and Earth System Sciences, 20, 1415-1419. doi:10.5194/nhess-20-1415-2020
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF

Du, W., Gong, Y., & Chen, N. (2022). PSO-WELLSVM: An integrated method and its application in urban waterlogging susceptibility assessment in the central Wuhan, China. Computers & Geosciences, 161, 105079. doi:10.1016/j.cageo.2022.105079
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF

Elumalai, V., Godwyn-Paulson, P., Logesh, N., Muthusankar, G., Lakshumanan, C., & Jonathan, M. P. (2022). Burning urban cities of South Africa due to civil turmoil 2021: Socio-economic and environmental consequences. Cities, 124, 103612. doi:10.1016/j.cities.2022.103612
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (MODIS)

Esch, T., Bachofer, F., Heldens, W., Hirner, A., Marconcini, M., Palacios-Lopez, D., . . . Gorelick, N. (2018). Where we live—a summary of the achievements and planned evolution of the Global Urban Footprint. Remote Sensing, 10(6), 895. doi:10.3390/rs10060895
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Terra SAR-X (TSX))
REMOTE SENSING (TanDEM-X (TDX))

Esch, T., Brzoska, E., Dech, S., Leutner, B., Palacios-Lopez, D., Metz-Marconcini, A., . . . Zeidler, J. (2022). World Settlement Footprint 3D - A first three-dimensional survey of the global building stock. Remote Sensing of Environment, 270, 112877. doi:10.1016/j.rse.2021.112877
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
REMOTE SENSING (Sentinel)
REMOTE SENSING (TanDEM-X (TDX))
REMOTE SENSING (Terra SAR-X (TSX))

Farina, A. (2022). Human-Dependent Landscapes Around the World – An Ecological Perspective. In Principles and Methods in Landscape Ecology: An Agenda for the Second Millennium (pp. 339-399). Cham: Springer International Publishing.
National Aggregates of Geospatial Data Collection (NAGDC) (Population, Landscape, And Climate Estimates (PLACE), v2)
Global High Resolution Urban Data from Landsat (GMIS)

Florczyk, A. J., Melchiorri, M., Zeidler, J., Corbane, C., Schiavina, M., Freire, S., . . . Pesaresi, M. (2020). The Generalised Settlement Area: mapping the Earth surface in the vicinity of built-up areas. International Journal of Digital Earth, 13(1), 45-60. doi:10.1080/17538947.2018.1550121
Global Rural-Urban Mapping Project (GRUMP) v1.01 (urban extent)
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-1)
REMOTE SENSING (Terra SAR-X (TSX))

Forget, Y., Shimoni, M., Gilbert, M., & Linard, C. (2021). Mapping 20 years of urban expansion in 45 urban areas of Sub-Saharan Africa. Remote Sensing, 13(3), 525. doi:10.3390/rs13030525
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-1)

Fung, K. Y., Yang, Z.-L., & Niyogi, D. (2022). Improving the local climate zone classification with building height, imperviousness, and machine learning for urban models. Computational Urban Science, 2(1), 16. doi:10.1007/s43762-022-00046-x
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

García-Álvarez, D., Lara Hinojosa, J., & Jurado Pérez, F. J. (2022). Global Thematic Land Use Cover Datasets Characterizing Artificial Covers. In D. García-Álvarez, M. T. Camacho Olmedo, M. Paegelow, & J. F. Mas (Eds.), Land Use Cover Datasets and Validation Tools: Validation Practices with QGIS (pp. 419-442). Cham: Springer International Publishing.
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)

Godwyn-Paulson, P., Jonathan, M. P., Rodríguez-Espinosa, P. F., Abdul Rahaman, S., Roy, P. D., Muthusankar, G., & Lakshumanan, C. (2022). Multi-hazard risk assessment of coastal municipalities of Oaxaca, Southwestern Mexico: An index based remote sensing and geospatial technique. International Journal of Disaster Risk Reduction, 77, 103041. doi:10.1016/j.ijdrr.2022.103041
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-2)

Guo, K., Guan, M., & Yu, D. (2021). Urban surface water flood modelling – a comprehensive review of current models and future challenges. Hydrology and Earth System Sciences, 25(5), 2843-2860. doi:10.5194/hess-25-2843-2021
Global High Resolution Urban Data from Landsat (GMIS)

Han, X. X., Li, G. Y., Lu, W. F., & Jiang, Y. W. (2019). Comparing statistical and semi-distributed rainfall–runoff models for a large subtropical watershed: A case study of Jiulong River catchment, China. Atmosphere, 10(2), 62. doi:10.3390/atmos10020062
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (MODIS - MCD12)
NASA REMOTE SENSING (SRTM)

He, W., Li, X., Zhou, Y., Shi, Z., Yu, G., Hu, T., . . . Gong, P. (2023). Global urban fractional changes at a 1-km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Earth System Science Data, 15(8), 3623-3639. doi:10.5194/essd-15-3623-2023
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Heidari, E., Mahmoudzadeh, A., & Mansouri Daneshvar, M. R. (2021). Urban flood susceptibility evaluation and prediction during 2010–2030 in the southern watersheds of Mashhad city, Iran. Environmental Systems Research, 10(1), 41. doi:10.1186/s40068-021-00245-1
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (GIOVANNI)

Hoffman-Hall, A., Puett, R., Silva, J. A., Chen, D., Baer, A., Han, K. T., . . . Loboda, T. V. (2020). Malaria exposure in Ann Township, Myanmar as a function of land cover and land use: Combining satellite earth observations and field surveys. GeoHealth, 4(12), e2020GH000299. doi:10.1029/2020GH000299
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Huang, M., Chen, N., Du, W., Wen, M., Zhu, D., & Gong, J. (2021). An on-demand scheme driven by the knowledge of geospatial distribution for large-scale high-resolution impervious surface mapping. GIScience & Remote Sensing, 58(4), 562-586. doi:10.1080/15481603.2021.1909304
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Gaofen 2)
REMOTE SENSING (Landsat)

Jin, K., Wang, F., Chen, D., Liu, H., Ding, W., & Shi, S. (2019). A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series. Scientific Data, 6(1), 139. doi:10.1038/s41597-019-0143-1
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
Population Dynamics (Global Population Count Grid Time Series Estimates, v1) - 10.7927/H4CC0XNV
Global High Resolution Urban Data from Landsat (GMIS)
REMOTE SENSING (DMSP-OLS)

Jin, K., Wang, F., & Wang, S. (2020). Assessing the spatiotemporal variation in anthropogenic heat and its impact on the surface thermal environment over global land areas. Sustainable Cities and Society, 63, 102488. doi:10.1016/j.scs.2020.102488
Gridded Population of the World (GPW) v4 (population density)
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
NASA REMOTE SENSING (MODIS)

Karanja, F. N., & Mwangi, P. W. (2023). Advanced Remote Sensing for Sustainable Decent Housing for the Economically Challenged Urban Households. In S. Mustak, D. Singh, & P. K. Srivastava (Eds.), Advanced Remote Sensing for Urban and Landscape Ecology (pp. 63-82). Singapore: Springer Nature Singapore.
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Sentinel-2A)

Kardani-Yazd, N., Kardani-Yazd, N., & Mansouri Daneshvar, M. R. (2019). Strategic spatial analysis of urban greenbelt plans in Mashhad city, Iran. Environmental Systems Research, 8(1), 30. doi:10.1186/s40068-019-0158-9
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Landsat)

Korpach, A. M., Garroway, C. J., Mills, A. M., von Zuben, V., Davy, C. M., & Fraser, K. C. (2022). Urbanization and artificial light at night reduce the functional connectivity of migratory aerial habitat. Ecography, 2022(8), e05581. doi:10.1111/ecog.05581
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
REMOTE SENSING (VIIRS NTL)

Kuang, W., Zhang, S., Li, X., & Lu, D. (2021). A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018. Earth System Science Data, 13, 63-82. doi:10.5194/essd-13-63-2021
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
REMOTE SENSING (Landsat)
REMOTE SENSING (China Brazil Earth Resources Satellite (CBERS-1)
REMOTE SENSING (Huan Jing (HJ-1A/B))

Landau, V. A., Noon, B. R., Theobald, D. M., Hobbs, N. T., & Nielsen, C. K. (2022). Integrating presence-only and occupancy data to model habitat use for the northernmost population of jaguars. Ecological Applications, 32(6), e2619. doi:10.1002/eap.2619
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-2 ALOS)

Liu, C., Huang, H., Zhang, Q., Chen, X., Xu, X., Xu, H., & Cheng, X. (2022). Arctic’s man-made impervious surfaces expanded by over two-thirds in the 21st century. Science Bulletin, 67(14), 1425-1429. doi:10.1016/j.scib.2022.06.001
Global High Resolution Urban Data from Landsat (GMIS)
REMOTE SENSING (Landsat)

Liu, C., Yang, K., Bennett, M. M., Guo, Z., Cheng, L., & Li, M. (2019). Automated extraction of built-up areas by fusing VIIRS Nighttime Lights and Landsat-8 data. Remote Sensing, 11(13), 1571. doi:10.3390/rs11131571
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Landsat)
REMOTE SENSING (VIIRS NTL)

Liu, F., Wang, S., Xu, Y., Ying, Q., Yang, F., & Qin, Y. (2020). Accuracy assessment of Global Human Settlement Layer (GHSL) built-up products over China. PLoS ONE, 15(5), e0233164. doi:10.1371/journal.pone.0233164
Global High Resolution Urban Data from Landsat (HBASE)

Liu, N., Dobbs, G. R., Caldwell, P. V., Miniat, C. F., Sun, G., Duan, K., . . . Carlson, C. P. (2022). Inter-basin transfers extend the benefits of water from forests to population centers across the conterminous U.S. Water Resources Research, 58(5), e2021WR031537. doi:10.1029/2021WR031537
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (MODIS)

Liu, X., de Sherbinin, A., & Zhan, Y. (2019). Mapping urban extent at large spatial scales using machine learning methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data. Remote Sensing, 11(10), 1247. doi:10.3390/rs11101247
Global Rural-Urban Mapping Project (GRUMP) v1.01 (urban extent)
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS)

Lonsdale, C. R., & Sun, K. (2023). Nitrogen oxides emissions from selected cities in North America, Europe, and East Asia observed by the TROPOspheric Monitoring Instrument (TROPOMI) before and after the COVID-19 pandemic. Atmospheric Chemistry and Physics, 23(15), 8727-8748. doi:10.5194/acp-23-8727-2023
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
REMOTE SENSING (TROPOMI)

Maantay, J. A. (2013). The collapse of place:  Derelict land, deprivation, and health inequality in Glasgow, Scotland. Cities and the Environment, 6(1), 52. Retrieved from http://digitalcommons.lmu.edu/cate/vol6/iss1/10
Global High Resolution Urban Data from Landsat (Cities from Space)

Maantay, J. A. (2017). The collapse of place:  Derelict land, deprivation, and health inequality in Glasgow, Scotland. In K. Etingoff (Ed.), Urban Land Use:  Community-Based Planning: Apple Academic Press.
Global High Resolution Urban Data from Landsat (Cities from Space)

Maier, S. D., Lindner, J. P., & Francisco, J. (2019). Conceptual framework for biodiversity assessments in global value chains. Sustainability, 11(7), 1841. doi:10.3390/su11071841
Global Agricultural Inputs (nitrogen fertilizer application)
Land Use and Land Cover (LULC) (Global Grid of Probabilities of Urban Expansion to 2030, v1)
Global High Resolution Urban Data from Landsat (GMIS)

Manos, E., Witharana, C., Udawalpola, M. R., Hasan, A., & Liljedahl, A. K. (2022). Convolutional neural networks for automated built infrastructure detection in the Arctic using sub-meter spatial resolution satellite imagery. Remote Sensing, 14(11), 2719. doi:10.3390/rs14112719
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Quickbird 2)
REMOTE SENSING (WorldView-2)

Marivoet, W., Ulimwengu, J., & Sedano, F. (2019). Spatial typology for targeted food and nutrition security interventions. World Development, 120, 62-75. doi:10.1016/j.worlddev.2019.04.003
Global High Resolution Urban Data from Landsat (GMIS)

Marivoet, W., Ulimwengu, J. M., Bugeme, D. M., Sanginga, B., & Thontwa, S. (2020). Spatial Food and Nutrition Security Typologies for Agriculture and Food Value Chain Interventions in Eastern DRC. Retrieved from Washington DC: https://doi.org/10.2499/p15738coll2.134139
Global High Resolution Urban Data from Landsat (GMIS)

Marivoet, W., Uluimwengu, J. M., & Sall, L. M. (2020). Policy Atlas on Food and Nutrition Security and Resilience: Kenya. Retrieved from Washington DC: https://doi.org/10.2499/p15738coll2.133781
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
REMOTE SENSING (Landsat)

Meerow, S. (2019). A green infrastructure spatial planning model for evaluating ecosystem service tradeoffs and synergies across three coastal megacities. Environmental Research Letters, 14(12), 125011. doi:10.1088/1748-9326/ab502c
Global High Resolution Urban Data from Landsat (GMIS)

Meerow, S. (2020). The politics of multifunctional green infrastructure planning in New York City. Cities, 100, 102621. doi:10.1016/j.cities.2020.102621
Global High Resolution Urban Data from Landsat (GMIS)
REMOTE SENSING (Landsat)

Mei, Y., Mai, J., Do, H. X., Gronewold, A., Reeves, H., Eberts, S., . . . Hunt, R. J. (2023). Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets? Water Resources Research, 59(2), e2022WR032064. doi:10.1029/2022WR032064
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (MODIS - MOD44B)
NASA REMOTE SENSING (DAYmet: Daily Surface Weather Data on a 1-km Grid for North America, v4)

Meng, S., Pang, Y., Huang, C., & Li, Z. (2022). Improved forest cover mapping by harmonizing multiple land cover products over China. GIScience & Remote Sensing, 59(1), 1570-1597. doi:10.1080/15481603.2022.2124044
Global High Resolution Urban Data from Landsat (GMIS)
REMOTE SENSING (Landsat)

Morgan, B., & Guénard, B. (2019). New 30 m resolution Hong Kong climate, vegetation, and topography rasters indicate greater spatial variation than global grids within an urban mosaic. Earth System Science Data, 11(3), 1083-1098. doi:10.5194/essd-11-1083-2019
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
REMOTE SENSING (Landsat)

Mussetti, G., Brunner, D., Henne, S., Allegrini, J., Krayenhoff, E. S., Schubert, S., . . . Carmeliet, J. (2020). COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees. Geoscientific Model Development, 13, 1685-1710. doi:10.5194/gmd-13-1685-2020
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (ASTER GDEM)

Naito, H., Ismailov, A., & Kimaro, A. B. (2021). The effect of mobile money on borrowing and saving: Evidence from Tanzania. World Development Perspectives, 23, 100342. doi:10.1016/j.wdp.2021.100342
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
REMOTE SENSING (DMSP-OLS)

Osman, S. A., & Das, J. (2023). GIS-based flood risk assessment using multi-criteria decision analysis of Shebelle River Basin in southern Somalia. SN Applied Sciences, 5(5), 134. doi:10.1007/s42452-023-05360-5
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (ASTER GDEM)
REMOTE SENSING (Landsat)

Ou, J., Liu, X., Liu, P., & Liu, X. (2019). Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection: A comparison with NPP-VIIRS nighttime light data. International Journal of Applied Earth Observation and Geoinformation, 81, 1-12. doi:10.1016/j.jag.2019.04.017
Global High Resolution Urban Data from Landsat (GMIS)
REMOTE SENSING (Luojia 1-01)
REMOTE SENSING (VIIRS DNB)

Paschalis, A., Chakraborty, T., Fatichi, S., Meili, N., & Manoli, G. (2021). Urban forests as main regulator of the evaporative cooling effect in cities. AGU Advances, 2(2), e2020AV000303. doi:10.1029/2020AV000303
Gridded Population of the World (GPW) v4.11 (population count)
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (SPOT VGT)
REMOTE SENSING (TROPOMI)

Qiu, C., Gamba, P., Schmitt, M., & Zhu, X. X. (2020). Learning from noisy samples for man-made impervious surface mapping. ISPRS Annals of the Photogrammetry, Remote Sensing, and Spatial Information Sciences, V-3-2020, 787-794. doi:10.5194/isprs-annals-V-3-2020-787-2020
Global High Resolution Urban Data from Landsat (HBASE)

Qiu, C., Mou, L., Schmitt, M., & Zhu, X. X. (2019). Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network. ISPRS Journal of Photogrammetry and Remote Sensing, 154, 151-162. doi:10.1016/j.isprsjprs.2019.05.004
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
REMOTE SENSING (SEntinel-2)

Rabbani, G., Kardani-Yazd, N., & Mansouri Daneshvar, M. R. (2020). Factors affecting severe weather threat index in urban areas of Turkey and Iran. Environmental Systems Research, 9(1), 9. doi:10.1186/s40068-020-00173-6
Global High Resolution Urban Data from Landsat (HBASE)

Rabbani, G., Madanian, S., & Mansouri Daneshvar, M. R. (2021). Multi-criteria modeling for land suitability evaluation of the urban greenbelts in Iran. Modeling Earth Systems and Environment, 7, 1291-1307. doi:10.1007/s40808-020-01002-6
Global High Resolution Urban Data from Landsat (HBASE) Documentation - 10.7927/H48W3BCM

Rafique, W., Zheng, D., Barras, J., Joglekar, S., & Kosmas, P. (2019). Predictive analysis of landmine risk. IEEE Access, 7, 107259-107269. doi:10.1109/ACCESS.2019.2929677
Gridded Population of the World (GPW) v3 (population count)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (ASTER GDEM)

Rivkin, L. R., & Johnson, M. T. J. (2022). The impact of urbanization on outcrossing rate and population genetic variation in the native wildflower, Impatiens capensis. Journal of Urban Ecology, 8(1), juac009. doi:10.1093/jue/juac009
Global High Resolution Urban Data from Landsat (GMIS)

Roseman, C. A., & Argrow, B. M. (2020). Weather hazard risk quantification for sUAS safety risk management. Journal of Atmospheric and Oceanic Technology, 37(7), 1251-1268. doi:10.1175/JTECH-D-20-0009.1
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S

Rybnikova, N., Portnov, B. A., Mirkes, E., Zinovyev, A., Brook, A., & Gorban, A. N. (2022). Coloring panchromatic nighttime satellite images: Comparing the performance of several machine learning methods. IEEE Transactions on Geoscience and Remote Sensing, 60(4702715), 1-15. doi:10.1109/TGRS.2021.3076011
Global High Resolution Urban Data from Landsat (HBASE)
POPGRID
REMOTE SENSING (VIIRS NTL)

Santangelo, J. S., Ness, R. W., Cohan, B., Fitzpatrick, C. R., Innes, S. G., Sophie, K., . . . Johnson, M. T. J. (2022). Global urban environmental change drives adaptation in white clover. Science, 375(6586), 1275-1281. doi:10.1126/science.abk0989
Last of the Wild v2 (Global Human Influence Index (Geographic)) - 10.7927/H4BP00QC
Global High Resolution Urban Data from Landsat (GMIS)
REMOTE SENSING (Landsat)

Schmidt, C. (2021). Contemporary and Historic Causes of Biogeographic Gradients in Genetic Diversity. (Ph.D.). University of Manitoba, Winnipeg. Retrieved from http://hdl.handle.net/1993/35662 
Global Roads (Global Roads Open Access Data Set (gROADS), v1) - 10.7927/H4VD6WCT
Last of the Wild v2 (Global Human Footprint (Geographic)) - 10.7927/H4M61H5F
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF

Schneider, J. M., Zabel, F., & Mauser, W. (2022). Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data, 9(1), 527. doi:10.1038/s41597-022-01632-8
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF

Schug, F., Frantz, D., Okujeni, A., & Hostert, P. (2022). Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and -2 data. Remote Sensing Letters, 13(8), 822-832. doi:10.1080/2150704X.2022.2088253
Global High Resolution Urban Data from Landsat (GMIS)
REMOTE SENSING (Sentinel-1)
REMOTE SENSING (Sentinel-2)

Sedano, F., Molini, V., & Azad, M. A. K. (2019). A mapping framework to characterize land use in the Sudan-Sahel region from dense stacks of Landsat data. Remote Sensing, 11(6), 648. doi:10.3390/rs11060648
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Landsat 8)

Shen, P., & Zhao, S. (2021). 1/4 to 1/3 of observed warming trends in China from 1980 to 2015 are attributed to land use changes. Climatic Change, 164(3), 59. doi:10.1007/s10584-021-03045-9
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
NASA REMOTE SENSING (GIMMS NDVI)

Silva, L. C. R. (2022). Expanding the scope of biogeochemical research to accelerate atmospheric carbon capture. Biogeochemistry, 161, 19-40. doi:10.1007/s10533-022-00957-1
Global High Resolution Urban Data from Landsat (GMIS)

Silva, R. C. G., Gronwall, J., van der Kwast, J., Danert, K., & Foppen, J. W. (2020). Estimating domestic self-supply groundwater use in urban continental Africa. Environmental Research Letters, 15(10), 1040b1042. doi:10.1088/1748-9326/ab9af9
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S

Sui, X., & van de Ven, F. (2023). The influence of low impact development (LID) on basin runoff in a half-urbanized catchment: a case study in San Antonio, Texas. Journal of Hydrology, 616, 128793. doi:10.1016/j.jhydrol.2022.128793
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF

Swain, K. C., Singha, C., & Nayak, L. (2020). Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS International Journal of Geo-Information, 9(12), 720. doi:10.3390/ijgi9120720
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (SMAP)
NASA REMOTE SENSING (TRMM)
REMOTE SENSING (Landsat)

Sydor, E. (2019). High-resolution data products help illuminate urbanization's reach.  Retrieved from https://blogs.ei.columbia.edu/2019/02/26/data-impervious-surfaces-urban-extents/
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)

Teillet, C., Pillot, B., Catry, T., Demagistri, L., Lyszczarz, D., Lang, M., . . . Dessay, N. (2021). Fast unsupervised multi-scale characterization of urban landscapes based on earth observation data. Remote Sensing, 13(12), 2398. doi:10.3390/rs13122398
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
REMOTE SENSING (Pleiades-1)
REMOTE SENSING (Sentinel-2 Multispectral Imager (MSI))

Tiepolo, M., & Galligari, A. (2021). Urban expansion-flood damage nexus: Evidence from the Dosso Region, Niger. Land Use Policy, 108, 105547. doi:10.1016/j.landusepol.2021.105547
Global High Resolution Urban Data from Landsat (HBASE)

Van de Walle, J., Brousse, O., Arnalsteen, L., Brimicombe, C., Byarugaba, D., Demuzere, M., . . . van Lipzig, N. P. M. (2022). Lack of vegetation exacerbates exposure to dangerous heat in dense settlements in a tropical African city. Environmental Research Letters, 17(w), 024004. doi:10.1088/1748-9326/ac47c3
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
NASA REMOTE SENSING (MODIS)

Van de Walle, J., Brousse, O., Arnalsteen, L., Byarugaba, D., Ddumba, D. S., Demuzere, M., . . . van Lipzig, N. P. M. (2021). Can local fieldwork help to represent intra-urban variability of canopy parameters relevant for tropical African climate studies? Theoretical and Applied Climatology, 146, 457-474. doi:10.1007/s00704-021-03733-7
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Wang, N., Zhang, X., Yao, S., Wu, J., & Xia, H. (2022). How good are global layers for mapping rural settlements? Evidence from China. Land, 11(8), 1308. doi:10.3390/land11081308
Global Rural-Urban Mapping Project (GRUMP) alpha (collection)
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (MODIS)

Wazarkar, S., & Keshavamurthy, B. N. (2018). A survey on image data analysis through clustering techniques for real world applications. Journal of Visual Communication and Image Representation, 55, 596-626. doi:10.1016/j.jvcir.2018.07.009
Global High Resolution Urban Data from Landsat (Cities from Space)

Williams, F., & Ware, C. (2021). Public realm health in Nigeria: Challenges and opportunities. African Journal of Landscape Architecture, 2(4). Retrieved from https://www.ajlajournal.org/articles/public-realm-health-in-nigeria-challenges-and-opportunities
Global High Resolution Urban Data from Landsat (Cities from Space)

Worden, J., & de Beurs, K. M. (2020). Surface water detection in the Caucasus. International Journal of Applied Earth Observation and Geoinformation, 91, 102159. doi:10.1016/j.jag.2020.102159
Global High Resolution Urban Data from Landsat (GMIS)
REMOTE SENSING (Landsat)

Xu, X., Liu, C., Liu, C., Hui, F., Cheng, X., & Huang, H. (2022). Fine-resolution mapping of the circumpolar Arctic Man-made impervious areas (CAMI) using sentinels, OpenStreetMap and ArcticDEM. Big Earth Data, 6(2), 196-218. doi:10.1080/20964471.2022.2025663
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (Sentinel)

Xu, Z., Jiao, L., Lan, T., Zhou, Z., Cui, H., Li, C., . . . Liu, Y. (2021). Mapping hierarchical urban boundaries for global urban settlements. International Journal of Applied Earth Observation and Geoinformation, 103, 102480. doi:10.1016/j.jag.2021.102480
Global Rural-Urban Mapping Project (GRUMP) v1 (population count)
Global High Resolution Urban Data from Landsat (GMIS)

Yin, J., Dong, J., Hamm, N. A. S., Li, Z., Wang, J., Xing, H., & Fu, P. (2021). Integrating remote sensing and geospatial big data for urban land use mapping: A review. International Journal of Applied Earth Observation and Geoinformation, 103, 102514. doi:10.1016/j.jag.2021.102514
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (AVHRR)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)

Zhang, P., Wolfe, R., & Bounoua, L. (2020). Comparison of MODIS Land Surface Temperature and Air Temperature Over Global in 2015. Paper presented at the IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium.
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Zhang, P., Wu, H., Zhang, J., Sun, Q., Ji, Z., Lin, X., . . . Sun, D. (2023). Understanding archetypal spatial gradient patterns in urban economic, population and air quality nexus: New insights from a geographic-process perspective. Sustainable Cities and Society, 95, 104596. doi:10.1016/j.scs.2023.104596
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Zhang, Z., Paschalis, A., Mijic, A., Meili, N., Manoli, G., van Reeuwijk, M., & Fatichi, S. (2022). A mechanistic assessment of urban heat island intensities and drivers across climates. Urban Climate, 44, 101215. doi:10.1016/j.uclim.2022.101215
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
NASA REMOTE SENSING (MODIS)

Zhong, C., Guo, H., Swan, I., Gao, P., Yao, Q., & Li, H. (2023). Evaluating trends, profits, and risks of global cities in recent urban expansion for advancing sustainable development. Habitat International, 138, 102869. doi:10.1016/j.habitatint.2023.102869
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ
Global Rural-Urban Mapping Project (GRUMP) v1.02 (urban extent polygons) - 10.7927/np6p-qe61
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF