{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T11:58:04Z","timestamp":1773403084536,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T00:00:00Z","timestamp":1681257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171113"],"award-info":[{"award-number":["42171113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42001367"],"award-info":[{"award-number":["42001367"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Natural Science Foundation","award":["ZR2020QD017"],"award-info":[{"award-number":["ZR2020QD017"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s12145-023-01010-x","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T09:03:03Z","timestamp":1681290183000},"page":"1727-1739","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Impervious surface Mapping and its spatial\u2013temporal evolution analysis in the Yellow River Delta over the last three decades using Google Earth Engine"],"prefix":"10.1007","volume":"16","author":[{"given":"Jiantao","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yexiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Quanlong","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Tongguang","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Pudong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,12]]},"reference":[{"key":"1010_CR1","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.jag.2013.07.002","volume":"26","author":"P Anne","year":"2014","unstructured":"Anne P, Simon R, Andr\u00e9 S (2014) Object-oriented mapping of urban trees using Random Forest classifiers. Int J Appl Earth Obs Geoinf 26:235\u2013245. https:\/\/doi.org\/10.1016\/j.jag.2013.07.002","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"1010_CR2","doi-asserted-by":"publisher","first-page":"100568","DOI":"10.1016\/J.ENVC.2022.100568","volume":"8","author":"KT Ayalew","year":"2022","unstructured":"Ayalew KT, Hailu BT, Suryabhagavan KV (2022) Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia. Environ Challenges 8:100568. https:\/\/doi.org\/10.1016\/J.ENVC.2022.100568","journal-title":"Environ Challenges"},{"issue":"1","key":"1010_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random Forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"issue":"1","key":"1010_CR4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.11834\/jrs.20030107","volume":"7","author":"Y Cha","year":"2003","unstructured":"Cha Y, Ni SX, Yang S (2003) An Effective Approach to Automatically Extract Urban Land-use from TM lmagery. J Remote Sens 7(1):37\u201340. https:\/\/doi.org\/10.11834\/jrs.20030107","journal-title":"J Remote Sens"},{"key":"1010_CR5","doi-asserted-by":"publisher","unstructured":"Chen BA, Feng QL, Niu BW, Yan FQ, Gao BB, Yang JY, Gong JH, Liu JT (2022) Multi-modal fusion of satellite and street-view images for urban village classification based on a dual-branch deep neural network. Int J Appl Earth Obs 109:102794. https:\/\/doi.org\/10.1016\/j.jag.2022.102794","DOI":"10.1016\/j.jag.2022.102794"},{"issue":"2","key":"1010_CR6","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1080\/01944369608975688","volume":"62","author":"LA Chester","year":"1996","unstructured":"Chester LA (1996) Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. J Am Plann Assoc 62(2):243\u2013258. https:\/\/doi.org\/10.1080\/01944369608975688","journal-title":"J Am Plann Assoc"},{"issue":"7","key":"1010_CR7","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1080\/01431160600887722","volume":"28","author":"R Cots-Folch","year":"2007","unstructured":"Cots-Folch R, Aitkenhead M, Martinez-Casasnovas J (2007) Mapping land cover from detailed aerial photography data using textural and neural network analysis. Int J Remote Sens 28(7):1624\u20131642. https:\/\/doi.org\/10.1080\/01431160600887722","journal-title":"Int J Remote Sens"},{"issue":"2","key":"1010_CR8","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1080\/15481603.2013.795307","volume":"50","author":"LR Daniele","year":"2013","unstructured":"Daniele LR, Daniel W (2013) Land cover and impervious surface extraction using parametric and non-parametric algorithms from the open-source software R: an application to sustainable urban planning in Sicily. Gis Remote Sens 50(2):231\u2013250. https:\/\/doi.org\/10.1080\/15481603.2013.795307","journal-title":"Gis Remote Sens"},{"key":"1010_CR9","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","volume":"185","author":"JW Dong","year":"2016","unstructured":"Dong JW, Xiao XM, Michael AM, Geli Z, Qin YW, David T, Chandrashekhar B, Berrien M (2016) Mapping paddy rice planting area in northeastern Asia with Landsat 8 images. phenology-based algorithm and Google Earth Engine. Remote Sens Environ 185:142\u2013154. https:\/\/doi.org\/10.1016\/j.rse.2016.02.016","journal-title":"Remote Sens Environ"},{"issue":"07","key":"1010_CR10","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.11834\/jrs.20210174","volume":"26","author":"P Duan","year":"2022","unstructured":"Duan P, Zhang F, Liu CJ (2022) Extraction of the impervious surface of typical cities in Xinjiang based on Sentinel-2A\/B and spatial difference analysis. J Remote Sens 26(07):1469\u20131482. https:\/\/doi.org\/10.11834\/jrs.20210174","journal-title":"J Remote Sens"},{"issue":"8","key":"1010_CR11","doi-asserted-by":"publisher","first-page":"784","DOI":"10.3390\/rs9080784","volume":"9","author":"S Eckert","year":"2017","unstructured":"Eckert S, Kiteme B, Njuguna E, Zaehringer JG (2017) Agricultural Expansion and Intensification in the Foothills of Mount Kenya: A Landscape Perspective. Remote Sens 9(8):784. https:\/\/doi.org\/10.3390\/rs9080784","journal-title":"Remote Sens"},{"issue":"06","key":"1010_CR12","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.11873\/j.issn.1004-0323.2021.6.1339","volume":"36","author":"BX Fu","year":"2021","unstructured":"Fu BX, Zhang JC, Du WJ, Wang PL, Sun ZC (2021) Effective and Novel Impervious Surface Fine Mapping Method and Its Application on Monitoring Urban Sustainable Development Goals. Remote Sens Technol Appl 36(06):1339\u20131349. https:\/\/doi.org\/10.11873\/j.issn.1004-0323.2021.6.1339","journal-title":"Remote Sens Technol Appl"},{"issue":"08","key":"1010_CR13","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.12082\/dqxxkx.2019.180631","volume":"21","author":"R Geng","year":"2019","unstructured":"Geng R, Fu B, Cai J, Chen X, Lan F, Yu H, Li Q (2019) Object-Based Karst Wetland Vegetation Classification Method Using Unmanned Aerial Vehicle images and Random Forest Algorithm. J. Geo-informatics Sci 21(08):1295\u20131306. https:\/\/doi.org\/10.12082\/dqxxkx.2019.180631","journal-title":"J. Geo-informatics Sci"},{"issue":"01","key":"1010_CR14","doi-asserted-by":"publisher","first-page":"57","DOI":"10.11873\/j.issn.1004-0323.2019.1.0057","volume":"34","author":"X Gu","year":"2019","unstructured":"Gu X, Gao X, Ma H, Shi F, Liu X, Cao X (2019) Comparison of Machine Learning Methods for Land Use\/Land Cover Classification in the Complicated Terrain Regions. Remote Sens Technol Appl 34(01):57\u201367. https:\/\/doi.org\/10.11873\/j.issn.1004-0323.2019.1.0057","journal-title":"Remote Sens Technol Appl"},{"key":"1010_CR15","doi-asserted-by":"publisher","first-page":"73","DOI":"10.13474\/j.cnki.11-2246.2016.0159","volume":"05","author":"R Guo","year":"2016","unstructured":"Guo R, Chi T, Peng L, Liu J, Yang L (2016) Urban land use classification using random forest\u2019s HMS-1 remote sensing data. Bull Surv Mapp 05:73\u201376. https:\/\/doi.org\/10.13474\/j.cnki.11-2246.2016.0159","journal-title":"Bull Surv Mapp"},{"issue":"2","key":"1010_CR16","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1080\/2150704X.2014.882526","volume":"5","author":"MM Hayes","year":"2014","unstructured":"Hayes MM, Miller SN, Murphy MA (2014) High-resolution landcover classification using Random Forest. Remote Sens Lett 5(2):112\u2013121. https:\/\/doi.org\/10.1080\/2150704X.2014.882526","journal-title":"Remote Sens Lett"},{"issue":"5","key":"1010_CR17","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.3390\/RS14051189","volume":"14","author":"S Jan","year":"2022","unstructured":"Jan S, P\u0159emysl \u0160, Josef L, Daniel P, Natalia K (2022) Random Forest Classification of Land Use, Land-Use Change and Forestry (LULUCF) Using Sentinel-2 Data\u2014A Case Study of Czechia. Remote Sens 14(5):1189. https:\/\/doi.org\/10.3390\/RS14051189","journal-title":"Remote Sens"},{"issue":"2","key":"1010_CR18","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1109\/36.134076","volume":"30","author":"YJ Kaufman","year":"1992","unstructured":"Kaufman YJ, Tanre D (1992) Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans on Geosci Remote Sens 30(2):261\u2013270. https:\/\/doi.org\/10.1109\/36.134076","journal-title":"IEEE Trans on Geosci Remote Sens"},{"key":"1010_CR19","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3969\/j.issn.1004-0323.2002.01.002","volume":"01","author":"S Li","year":"2002","unstructured":"Li S, Ding S, Qian L (2002) The Decision Tree Classification and Its Application Research in Land Cover. Remote Sens Technol Appl 01:6\u201311. https:\/\/doi.org\/10.3969\/j.issn.1004-0323.2002.01.002","journal-title":"Remote Sens Technol Appl"},{"issue":"2","key":"1010_CR20","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3390\/RS13020212","volume":"13","author":"F Li","year":"2021","unstructured":"Li F, Li E, Zhang C, Samat A, Liu W, Li C (2021) Estimating Artificial Impervious Surface Percentage in Asia by Fusing Multi-Temporal MODIS and VIIRS Nighttime Light Data. Remote Sens 13(2):212. https:\/\/doi.org\/10.3390\/RS13020212","journal-title":"Remote Sens"},{"issue":"03","key":"1010_CR21","doi-asserted-by":"publisher","first-page":"420","DOI":"10.11834\/jrs.20165239","volume":"20","author":"S Liu","year":"2016","unstructured":"Liu S, Li Q (2016) Composite kernel support vector regression model for hyperspectral image impervious surface extraction. J Remote Sens 20(03):420\u2013430. https:\/\/doi.org\/10.11834\/jrs.20165239","journal-title":"J Remote Sens"},{"issue":"3","key":"1010_CR22","doi-asserted-by":"publisher","first-page":"034515","DOI":"10.1117\/1.JRS.14.034515","volume":"14","author":"J Liu","year":"2020","unstructured":"Liu J, Liu C, Feng Q, Ma Y (2020) Subpixel impervious surface estimation in the Nansi Lake Basin using random forest regression combined with GF-5 hyperspectral data. J Appl Remote Sens 14(3):034515. https:\/\/doi.org\/10.1117\/1.JRS.14.034515","journal-title":"J Appl Remote Sens"},{"key":"1010_CR23","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.isprsjprs.2019.11.021","volume":"159","author":"D Liu","year":"2020","unstructured":"Liu D, Chen N, Zhang X, Wang C, Du W (2020b) Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin. ISPRS J Photogramm Remote Sens 159:337\u2013351. https:\/\/doi.org\/10.1016\/j.isprsjprs.2019.11.021","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"03","key":"1010_CR24","doi-asserted-by":"publisher","first-page":"253","DOI":"10.6046\/zrzyyg.2020310","volume":"33","author":"C Liu","year":"2021","unstructured":"Liu C, Feng Q, Jin D, Shi T, Liu J, Zhu M (2021) Application of random forest and Sentinel-1\/2 in the information extraction of impervious layers in Dongying City. Remote Sens Nat Resour 33(03):253\u2013261. https:\/\/doi.org\/10.6046\/zrzyyg.2020310","journal-title":"Remote Sens Nat Resour"},{"issue":"11","key":"1010_CR25","doi-asserted-by":"publisher","first-page":"4314","DOI":"10.3969\/j.issn.1000-6923.2018.11.042","volume":"38","author":"X Lu","year":"2018","unstructured":"Lu X, Huang Y, Hong J, Zeng D, Yang L (2018) Spatial and temporal variations in wetland landscape patterns in the Yellow River Delta based on Landsat images. China Environ Sci 38(11):4314\u20134324. https:\/\/doi.org\/10.3969\/j.issn.1000-6923.2018.11.042","journal-title":"China Environ Sci"},{"key":"1010_CR26","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.landurbplan.2014.06.009","volume":"130","author":"Q Ma","year":"2014","unstructured":"Ma Q, He C, Wu J, Liu Z, Zhang Q, Sun Z (2014) Quantifying spatiotemporal patterns of urban impervious surfaces in China: An improved assessment using nighttime light data. Landsc Urban Plan 130:36\u201349. https:\/\/doi.org\/10.1016\/j.landurbplan.2014.06.009","journal-title":"Landsc Urban Plan"},{"key":"1010_CR27","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","volume":"114","author":"B Mariana","year":"2016","unstructured":"Mariana B, Lucian D (2016) Random Forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24\u201331. https:\/\/doi.org\/10.1016\/j.isprsjprs.2016.01.011","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1010_CR28","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","volume":"202","author":"G Noel","year":"2017","unstructured":"Noel G, Matt H, Mike D, Simon I, David T, Rebecca M (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18\u201327. https:\/\/doi.org\/10.1016\/j.rse.2017.06.031","journal-title":"Remote Sens Environ"},{"issue":"2","key":"1010_CR29","first-page":"248","volume":"34","author":"H Pei","year":"2018","unstructured":"Pei H, Sun T, Wang X (2018) Object-oriented land use\/cover classification based on texture features of Landsat 8 OLI image. Editorial Office of Trans Chin Soc Agric Eng 34(2):248\u2013255","journal-title":"Editorial Office of Trans Chin Soc Agric Eng"},{"key":"1010_CR30","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.isprsjprs.2020.06.022","volume":"167","author":"AR Phalke","year":"2020","unstructured":"Phalke AR, \u00d6zdo\u011fan M, Thenkabail PS, Erickson T, Gorelick N, Yadav K, Congalton RG (2020) Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine. ISPRS J Photogramm Remote Sens 167:104\u2013122. https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.06.022","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"02","key":"1010_CR31","doi-asserted-by":"publisher","first-page":"200","DOI":"10.3724\/SP.J.1047.2016.00200","volume":"18","author":"W Qiao","year":"2016","unstructured":"Qiao W, Mao G, Wang Y, Chen Y (2016) Research on Urban Expansion and Land Use Change in Nanjing over the Past 32 Years. J Geo-Information Sci 18(02):200\u2013209. https:\/\/doi.org\/10.3724\/SP.J.1047.2016.00200","journal-title":"J Geo-Information Sci"},{"issue":"Jan","key":"1010_CR32","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","volume":"67","author":"VF Rodriguez-Galiano","year":"2011","unstructured":"Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2011) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 67(Jan):93\u2013104. https:\/\/doi.org\/10.1016\/j.isprsjprs.2011.11.002","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1010_CR33","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","volume":"121","author":"VF Rodriguez-Galiano","year":"2012","unstructured":"Rodriguez-Galiano VF, Chica-Olmo M, Abarca-Hernandez F, Atkinson PM, Jeganathan C (2012) Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens Environ 121:93\u2013107. https:\/\/doi.org\/10.1016\/j.rse.2011.12.003","journal-title":"Remote Sens Environ"},{"issue":"11","key":"1010_CR34","doi-asserted-by":"publisher","first-page":"2654","DOI":"10.3390\/RS14112654","volume":"14","author":"A Saeid","year":"2022","unstructured":"Saeid A, Mohsen S, Hamidreza R, Saeid H (2022) Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series. Remote Sens 14(11):2654. https:\/\/doi.org\/10.3390\/RS14112654","journal-title":"Remote Sens"},{"issue":"3","key":"1010_CR35","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1016\/j.asr.2021.03.039","volume":"68","author":"SA SamadiTodar","year":"2021","unstructured":"SamadiTodar SA, Attarchi S, Osati K (2021) Investigation the seasonality effect on impervious surface detection from Sentinel-1 and Sentinel-2 images using Google Earth engine. Adv Space Res 68(3):1356\u20131365. https:\/\/doi.org\/10.1016\/j.asr.2021.03.039","journal-title":"Adv Space Res"},{"issue":"40","key":"1010_CR36","doi-asserted-by":"publisher","first-page":"16083","DOI":"10.1073\/pnas.1211658109","volume":"109","author":"KC Seto","year":"2012","unstructured":"Seto KC, G\u00fcneralp B, Hutyra LR (2012) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc Nat Acad Sci U S A 109(40):16083\u201316088. https:\/\/doi.org\/10.1073\/pnas.1211658109","journal-title":"Proc Nat Acad Sci U S A"},{"issue":"18","key":"1010_CR37","doi-asserted-by":"publisher","first-page":"3666","DOI":"10.3390\/RS13183666","volume":"13","author":"J Shen","year":"2021","unstructured":"Shen J, Shuai Y, Li P, Cao Y, Ma X (2021) Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data. Remote Sens 13(18):3666. https:\/\/doi.org\/10.3390\/RS13183666","journal-title":"Remote Sens"},{"key":"1010_CR38","unstructured":"Song L (2018) Exploring Rainwater Resourcefulness in Binzhou, Shandong Province. China Water Resour 9:23\u201324. CNKI:SUN:SLZG.0.2018-09-010"},{"issue":"C","key":"1010_CR39","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","volume":"164","author":"H Tamiminia","year":"2020","unstructured":"Tamiminia H, Salehi B, Mahdianpari M, Beier CM, Johnson L (2020) Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS J Photogramm Remote Sens 164(C):152\u2013170. https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.04.001","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1010_CR40","doi-asserted-by":"publisher","first-page":"110987","DOI":"10.1016\/j.rse.2018.11.030","volume":"238","author":"X Wang","year":"2020","unstructured":"Wang X, Xiao X, Zou Z, Chen B, Ma J, Dong J, Doughty RB, Zhong Q, Qin Y, Dai S, Li X, Zhao B (2020) Tracking annual changes of coastal tidal flats in China during 1986\u20132016 through analyses of Landsat images with Google Earth Engine. Remote Sens Environ 238:110987. https:\/\/doi.org\/10.1016\/j.rse.2018.11.030","journal-title":"Remote Sens Environ"},{"issue":"22","key":"1010_CR41","doi-asserted-by":"publisher","first-page":"4494","DOI":"10.3390\/RS13224494","volume":"13","author":"S Wang","year":"2021","unstructured":"Wang S, Pu Y, Li S, Li R, Li M (2021) Spatio-Temporal Analysis of Impervious Surface Expansion in the Qinhuai River Basin, China, 1988\u20132017. Remote Sens 13(22):4494\u20134494. https:\/\/doi.org\/10.3390\/RS13224494","journal-title":"Remote Sens"},{"key":"1010_CR42","doi-asserted-by":"publisher","first-page":"103710","DOI":"10.1016\/J.SCS.2022.103710","volume":"79","author":"Y Wang","year":"2022","unstructured":"Wang Y, Li X, Zhang C, He W (2022) Influence of spatiotemporal changes of impervious surface on the urban thermal environment: A case of Huai\u2019an central urban area. Sustain Cities Soc 79:103710. https:\/\/doi.org\/10.1016\/J.SCS.2022.103710","journal-title":"Sustain Cities Soc"},{"issue":"2","key":"1010_CR43","doi-asserted-by":"publisher","first-page":"299","DOI":"10.11834\/jrs.20211317","volume":"26","author":"X Wang","year":"2022","unstructured":"Wang X, Tian J, Li X, Wang L, Gong H, Chen B, Li X, Guo J (2022) Benefits of Google Earth Engine in remote sensing. J Remote Sens 26(2):299\u2013309. https:\/\/doi.org\/10.11834\/jrs.20211317","journal-title":"J Remote Sens"},{"issue":"10","key":"1010_CR44","doi-asserted-by":"publisher","first-page":"5583","DOI":"10.1109\/TGRS.2015.2425658","volume":"53","author":"W Wu","year":"2015","unstructured":"Wu W, Guo H, Li X, Ferro-Famil L, Zhang L (2015) Urban Land Use Information Extraction Using the Ultrahigh-Resolution Chinese Airborne SAR Imagery. IEEE Trans Geosci and Remote Sens 53(10):5583\u20135599. https:\/\/doi.org\/10.1109\/TGRS.2015.2425658","journal-title":"IEEE Trans Geosci and Remote Sens"},{"issue":"5","key":"1010_CR45","doi-asserted-by":"publisher","first-page":"89","DOI":"10.11834\/jrs.20050586","volume":"0","author":"H Xu","year":"2005","unstructured":"Xu H (2005) A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI). J Remote Sens 0(5):89\u2013595. https:\/\/doi.org\/10.11834\/jrs.20050586","journal-title":"J Remote Sens"},{"key":"1010_CR46","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.rse.2013.10.012","volume":"141","author":"L Xu","year":"2013","unstructured":"Xu L, Li J, Brenning A (2013) A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery. Remote Sens Environ 141:14\u201323. https:\/\/doi.org\/10.1016\/j.rse.2013.10.012","journal-title":"Remote Sens Environ"},{"issue":"01","key":"1010_CR47","doi-asserted-by":"publisher","first-page":"97","DOI":"10.11873\/j.issn.10040323.2009.1.97","volume":"24","author":"Z Xue","year":"2009","unstructured":"Xue Z, Yang X, Su F, Sun X (2009) Application Research of Fused Image of CBERS-02and SPOT5Data in Land Use Monitoring of Coastal Zone. Remote Sens Technol Appl 24(01):97\u2013102. https:\/\/doi.org\/10.11873\/j.issn.10040323.2009.1.97","journal-title":"Remote Sens Technol Appl"},{"key":"1010_CR48","doi-asserted-by":"publisher","first-page":"109307","DOI":"10.1016\/J.ECOLIND.2022.109307","volume":"142","author":"L Yang","year":"2022","unstructured":"Yang L, Zhang S, Yin L, Zhang B (2022) Global occupation of wetland by artificial impervious surface area expansion and its impact on ecosystem service value for 2001\u20132018. Ecol Indic 142:109307. https:\/\/doi.org\/10.1016\/J.ECOLIND.2022.109307","journal-title":"Ecol Indic"},{"issue":"3","key":"1010_CR49","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.5194\/essd-12-1625-2020","volume":"12","author":"X Zhang","year":"2020","unstructured":"Zhang X, Liu L, Wu C, Chen X, Gao Y, Xie S, Zhang B (2020) Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform. Earth Syst Sci Data 12(3):1625\u20131648. https:\/\/doi.org\/10.5194\/essd-12-1625-2020","journal-title":"Earth Syst Sci Data"},{"issue":"01","key":"1010_CR50","doi-asserted-by":"publisher","first-page":"56","DOI":"10.13671\/j.hjkxxb.2021.0492","volume":"42","author":"X Zhang","year":"2022","unstructured":"Zhang X, Cao Q, Ji S, Chen H, Zhang T, Liu J (2022) Quantifying the contributions of climate change and human activities to vegetation dynamic changes in the Yellow River Delta. Acta Sci Circumst 42(01):56\u201369. https:\/\/doi.org\/10.13671\/j.hjkxxb.2021.0492","journal-title":"Acta Sci Circumst"},{"issue":"03","key":"1010_CR51","doi-asserted-by":"publisher","first-page":"408","DOI":"10.3724\/SP.J.1047.2013.00408","volume":"15","author":"G Zhao","year":"2013","unstructured":"Zhao G, Ye S, Gao M, Ding X, Yuan H, Wang J (2013) Analysis of Land Use and Shoreline Changes at the Dawenliu Nature Reserve of Yellow River Delta Based on Remote Sensing. J Geo-information Sci 15(03):408\u2013414. https:\/\/doi.org\/10.3724\/SP.J.1047.2013.00408","journal-title":"J Geo-information Sci"},{"key":"1010_CR52","unstructured":"Zhao H, Wang Y (2012) Research on the Factors Affecting the Classification Accuracy of ETM Remote Sensing Image Land Cover\/Use. Remote Sens Technol Appl 27(04):600\u2013608. CNKI:SUN:YGJS.0.2012\u201304\u2013018"},{"key":"1010_CR53","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.jag.2013.01.003","volume":"23","author":"S Zoltan","year":"2013","unstructured":"Zoltan S, Francisco E, Amr HA, Scot S, Leonard P (2013) Analyzing fine-scale wetland composition using high resolution imagery and texture features. Int J Appl Earth Obs and Geoinf 23:204\u2013212. https:\/\/doi.org\/10.1016\/j.jag.2013.01.003","journal-title":"Int J Appl Earth Obs and Geoinf"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01010-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-023-01010-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01010-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T03:38:54Z","timestamp":1684553934000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-023-01010-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,12]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["1010"],"URL":"https:\/\/doi.org\/10.1007\/s12145-023-01010-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2205884\/v1","asserted-by":"object"}]},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,12]]},"assertion":[{"value":"26 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}