{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T10:41:05Z","timestamp":1774694465323,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFB2100501"],"award-info":[{"award-number":["2018YFB2100501"]}]},{"name":"Key Research and Development Program of Yunnan province in China","award":["2018IB023"],"award-info":[{"award-number":["2018IB023"]}]},{"name":"National Natural Science Foundation of China","award":["42090012, 41771452, 41771454 and 41890820"],"award-info":[{"award-number":["42090012, 41771452, 41771454 and 41890820"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping impervious surface area (ISA) dynamics at the regional and global scales is an important task that supports the management of the urban environment and urban ecological systems. In this study, we aimed to develop a new method for ISA percentage (ISA%) mapping using Nighttime Light (NTL) and MODIS products. The proposed method consists of three major steps. First, we calculated the Enhanced Vegetation Index (EVI)-adjusted NTL index (EANTLI) and performed intra-annual and inter-annual corrections on the DMSP-OLS data. Second, based on the geographically weighted regression (GWR) model, we built a consistent NTL product from 2000 to 2019 by performing an intercalibration between DMSP-OLS and VIIRS images. Third, we adopted a GA-BP neural network model to monitor ISA% dynamics using NTL imagery, MODIS imagery, and population data. Taking the Guangdong\u2013Hong Kong\u2013Macao Greater Bay as the study area, our results indicate that the ISA% in our study area increased from 7.97% in 2000 to 17.11% in 2019, with a mean absolute error (MAE) of 0.0647, root mean square error (RMSE) of 0.1003, Pearson\u2019s coefficient of 0.9613, and R2 (R-squared) of 0.9239. Specifically, these results demonstrate the effectiveness of the proposed method in mapping ISA and investigating ISA dynamics using temporal features extracted from consistent NTL and MODIS products. The proposed method is feasible when generating ISA% at a large scale at high frequency, given the ease of implementation and the availability of input data sources.<\/jats:p>","DOI":"10.3390\/rs13101900","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T03:28:36Z","timestamp":1620962916000},"page":"1900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"Yun","family":"Tang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Zhenfeng","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-382X","authenticated-orcid":false,"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4998-8796","authenticated-orcid":false,"given":"Bowen","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"ref_1","unstructured":"Nations, U. 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