{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T02:01:09Z","timestamp":1773972069968,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,16]],"date-time":"2019-01-16T00:00:00Z","timestamp":1547596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000200","name":"United States Agency for International Development","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/100000200","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Frederick S. Pardee Center for International Futures at the Josef Korbel School of International Studies at the University of Denver","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Uganda is one of the poorest nations in the world. It is important to obtain accurate, timely data on socio-economic characteristics sub-nationally, so as to target poverty reduction strategies to those most in need. Many studies have demonstrated that nighttime lights (NTL) can be used to measure human activities. Nevertheless, the methods developed from these studies (1) suffer from coarse resolutions, (2) fail to capture the nonlinearity and multi-scale variability of geospatial data, and (3) perform poorly for agriculture-dependent regions. This study proposes a new enhanced light intensity model (ELIM) to estimate the gross domestic product (GDP) for sub-national units within Uganda. This model is developed by combining the NTL data from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the population data from the Global Human Settlement Layer (GHSL), and information on agricultural production and market prices across several commodity types. This resulted in a gridded dataset for Uganda\u2019s GDP at sub-national levels, to capture the spatial heterogeneity in the economic activity.<\/jats:p>","DOI":"10.3390\/rs11020163","type":"journal-article","created":{"date-parts":[[2019,1,17]],"date-time":"2019-01-17T03:02:03Z","timestamp":1547694123000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Estimation and Mapping of Sub-National GDP in Uganda Using NPP-VIIRS Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Xuantong","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Geography and the Environment, University of Denver, Denver, CO 80208, USA"}]},{"given":"Mickey","family":"Rafa","sequence":"additional","affiliation":[{"name":"The Frederick S. Pardee Center for International Futures Josef Korbel School of International Studies, University of Denver, 2201 South Gaylord Street, Denver, CO 80208, USA"}]},{"given":"Jonathan D.","family":"Moyer","sequence":"additional","affiliation":[{"name":"The Frederick S. Pardee Center for International Futures Josef Korbel School of International Studies, University of Denver, 2201 South Gaylord Street, Denver, CO 80208, USA"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography and the Environment, University of Denver, Denver, CO 80208, USA"}]},{"given":"Jennifer","family":"Scheer","sequence":"additional","affiliation":[{"name":"USAID\/Uganda Monitoring, Evaluation and Learning Program, Kampala, Uganda"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4961-026X","authenticated-orcid":false,"given":"Paul","family":"Sutton","sequence":"additional","affiliation":[{"name":"Department of Geography and the Environment, University of Denver, Denver, CO 80208, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,16]]},"reference":[{"key":"ref_1","unstructured":"Deb, S. (2015, January 16\u201317). Gap between GDP and HDI: Are the Rich Country Experiences Different from the Poor?. Proceedings of the IARIW-OECD Special Conference, Paris, France."},{"key":"ref_2","unstructured":"(2017). World Population Prospects: The 2017 Revision, Key Findings and Advance Tables, United Nations, Department of Economic and Social Affairs, Population Division. Working Paper No. ESA\/P\/WP\/248."},{"key":"ref_3","unstructured":"Clark, J.I., and Rhind, D.W. (1992). Population data and global environmental change, International Social Science Council and UNESCO."},{"key":"ref_4","unstructured":"Rose, A.N., and Bright, E.A. (2014). The LandScan Global Population Distribution Project: Current State of the Art and Prospective Innovation."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1257\/aer.102.2.994","article-title":"Measuring Economic Growth from Outer Space","volume":"102","author":"Henderson","year":"2012","journal-title":"Am. Econ. Rev. Nashv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0034-4257(80)90043-7","article-title":"Monitoring urban population and energy utilization patterns from satellite Data","volume":"9","author":"Welch","year":"1980","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"201","article-title":"Urbanized area energy-utilization patterns from DMSP data","volume":"46","author":"Welch","year":"1980","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","first-page":"114","article-title":"Development of a 2009 stable lights product using DMSP-OLS data","volume":"30","author":"Baugh","year":"2010","journal-title":"Proc. Asia Pac. Adv. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1175\/BAMS-D-12-00097.1","article-title":"First-light imagery from Suomi NPP VIIRS","volume":"94","author":"Hillger","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_10","unstructured":"(2016). Agriculture in Sub-Saharan Africa: Prospects and challenges for the next decade. OECD-FAO Agricultural Outlook 2016\u20132025, OECD."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bundervoet, T., Maiyo, L., and Sanghi, A. (2015). Bright Lights, Big Cities: Measuring National and Subnational Economic Growth in Africa from Outer Space, with an Application to Kenya and Rwanda, The World Bank.","DOI":"10.1596\/1813-9450-7461"},{"key":"ref_12","unstructured":"Uganda Bureau of Statistics (2010). Uganda Census of Agriculture 2008\/2009, Volume IV: Crop Area and Production Report. Uganda Bureau of Statistics 2010, Uganda Bureau of Statistics."},{"key":"ref_13","unstructured":"Uganda Bureau of Statistics (2016). Uganda Demographic and Health Survey 2016\/17, Uganda Bureau of Statistics."},{"key":"ref_14","first-page":"62","article-title":"Why VIIRS data are superior to DMSP for mapping nighttime lights","volume":"35","author":"Elvidge","year":"2013","journal-title":"Proc. Asia Pac. Adv. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.3390\/rs6021705","article-title":"Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS Data","volume":"6","author":"Shi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Syrris, V., and Julea, A. (2016). A new method for earth observation data analytics based on symbolic machine learning. Remote Sens., 8.","DOI":"10.3390\/rs8050399"},{"key":"ref_17","unstructured":"Uganda Bureau of Statistics (2009). A Summary Report of the National Livestock Census, 2008, Uganda Bureau of Statistics."},{"key":"ref_18","unstructured":"(2012). Food and Agriculture Organization Statistical Databases, Food and Agriculture Organization of the United Nations."},{"key":"ref_19","unstructured":"Cotton Development Organisation (2010). Cotton Development Organisation Annual Report 2009\u20132010, Cotton Development Organisation."},{"key":"ref_20","unstructured":"Uganda Bureau of Statistics (2014). National Population and Housing Census 2014, Subcounty Report\u2014Northern Region, Uganda Bureau of Statistics."},{"key":"ref_21","unstructured":"Uganda Bureau of Statistics (2014). National Population and Housing Census 2014, Main Report, Uganda Bureau of Statistics."},{"key":"ref_22","unstructured":"(2018, August 27). Coffee Producing Areas of Uganda. Available online: https:\/\/www.geogecko.com\/blog\/ugandacoffeemap\/."},{"key":"ref_23","unstructured":"(2018, August 27). FIT Insights Group Limited. Available online: https:\/\/fitinsightsgroup.com\/uganda-2\/."},{"key":"ref_24","unstructured":"Uganda Coffee Development Authority (2015). Coffee Price Trend 1992\u20132015."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.3390\/rs5063057","article-title":"Potential of NPP-VIIRS nighttime light imagery for modeling the regional economy of China","volume":"5","author":"Li","year":"2013","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"147","DOI":"10.2174\/1874923201003010147","article-title":"Shedding Light on the Global Distribution of Economic Activity","volume":"3","author":"Ghosh","year":"2010","journal-title":"Open Geogr. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4443","DOI":"10.1080\/01431160903277464","article-title":"Estimating energy consumption from night-time DMPS\/OLS imagery after correcting for saturation effects","volume":"31","author":"Letu","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1038\/4351152a","article-title":"China\u2019s burning ambition","volume":"435","author":"Aldhous","year":"2005","journal-title":"Nature"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1016\/j.rse.2011.04.032","article-title":"Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP\/OLS nighttime light data","volume":"115","author":"Zhang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"1037","article-title":"Modeling the population of China using DMSP operational linescan system nighttime data","volume":"67","author":"Lo","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4459","DOI":"10.1080\/01431160903261005","article-title":"The Use of Night-time Lights Satellite Imagery as a Measure of Australia\u2019s Regional Electricity Consumption and Population Distribution","volume":"31","author":"Townsend","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3510","DOI":"10.1073\/pnas.0509842103","article-title":"Geography and macroeconomics: New data and new findings","volume":"103","author":"Nordhaus","year":"2006","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"180004","DOI":"10.1038\/sdata.2018.4","article-title":"Gridded global datasets for gross domestic product and Human Development Index over 1990\u20132015","volume":"5","author":"Kummu","year":"2018","journal-title":"Sci. Data"},{"key":"ref_34","unstructured":"Nordhaus, W., Azam, Q., Corderi, D., Hood, K., Makarova, N., Mukhtar, A., Miltner, A., and Weiss, J. (2006). The G-Econ Database on Gridded Output: Methods and Data, Yale University. Available online: http:\/\/gecon.yale.edu\/."},{"key":"ref_35","first-page":"5","article-title":"Estimation of gross domestic product at sub-national scales using nighttime satellite imagery","volume":"8","author":"Sutton","year":"2007","journal-title":"Int. J. Ecol. Econ. Stat."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:26:33Z","timestamp":1760185593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/2\/163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,16]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11020163"],"URL":"https:\/\/doi.org\/10.3390\/rs11020163","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints201811.0520.v1","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,16]]}}}