{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T15:37:29Z","timestamp":1775057849666,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2015,9,22]],"date-time":"2015-09-22T00:00:00Z","timestamp":1442880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Data from the U.S. Defense Meteorological Satellite Program\u2019s Operational Line-scan System are often used to map impervious surface area (ISA) distribution at regional and global scales, but its coarse spatial resolution and data saturation produce high inaccuracy in ISA estimation. Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite\u2019s Day\/Night Band (VIIRS-DNB) with its high spatial resolution and dynamic data range may provide new insights but has not been fully examined in mapping ISA distribution. In this paper, a new variable\u2014Large-scale Impervious Surface Index (LISI)\u2014is proposed to integrate VIIRS-DNB and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data for mapping ISA distribution. A regression model was established, in which LISI was used as an independent variable and the reference ISA from Landsat images was a dependent variable. The results indicated a better estimation performance using LISI than using  a single VIIRS-DNB or MODIS NDVI variable. The LISI-based approach provides accurate spatial patterns from high values in core urban areas to low values in rural areas, with  an overall root mean squared error of 0.11. The LISI-based approach is recommended for fractional ISA estimation in a large area.<\/jats:p>","DOI":"10.3390\/rs70912459","type":"journal-article","created":{"date-parts":[[2015,9,22]],"date-time":"2015-09-22T15:36:54Z","timestamp":1442936214000},"page":"12459-12477","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3615-7128","authenticated-orcid":false,"given":"Wei","family":"Guo","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Dengsheng","family":"Lu","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, School of Environmental & Resource Sciences, Zhejiang A & F University,  Lin\u2019an 311300, China"}]},{"given":"Yanlan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Jixian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]}],"member":"1968","published-online":{"date-parts":[[2015,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1177\/088541202400903563","article-title":"Impervious surfaces and water quality: A review of current literature and its implications for watershed planning","volume":"16","author":"Brabec","year":"2002","journal-title":"J. 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