{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:00:11Z","timestamp":1773907211091,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"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":["42271112"],"award-info":[{"award-number":["42271112"]}],"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":["ZR2020QD017"],"award-info":[{"award-number":["ZR2020QD017"]}],"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":["ZR2020QD049"],"award-info":[{"award-number":["ZR2020QD049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Natural Science Foundation","award":["42171113"],"award-info":[{"award-number":["42171113"]}]},{"name":"Shandong Natural Science Foundation","award":["42271112"],"award-info":[{"award-number":["42271112"]}]},{"name":"Shandong Natural Science Foundation","award":["ZR2020QD017"],"award-info":[{"award-number":["ZR2020QD017"]}]},{"name":"Shandong Natural Science Foundation","award":["ZR2020QD049"],"award-info":[{"award-number":["ZR2020QD049"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ecological environment of Yellow River Delta High-efficiency Ecological Economic Zone (YRDHEEZ) is adjacent to the Bohai Sea. The unique geographical location makes it highly sensitive to anthropogenic disturbances. As an important land surface biophysical parameter, the impervious surface area (ISA) can characterize the level of urbanization and measure the intensity of human activities, and hence, the timely understanding of ISA dynamic changes is of great significance to protect the ecological safety of the YRDHEEZ. Based on the multi-source and multi-modal Sentinel-1\/2 remotely sensed data provided by Google Earth Engine (GEE) cloud computing platform, this study developed a novel approach for the extraction of time-series ISA in the YRDHEEZ through a combination of random forest algorithm and numerous representative features extracted from Sentinel-1\/2. Subsequently, we revealed the pattern of the ISA spatial-temporal evolution in this region over the past five years. The results demonstrated that the proposed method has good performance with an average overall accuracy of 94.84% and an average kappa coefficient of 0.9393, which verified the feasibility of the proposed method for large-scale ISA mapping with 10 m. Spatial-temporal evolution analysis revealed that the ISA of the YRDHEEZ decreased from 5211.39 km2 in 2018 to 5147.02 km2 in 2022 with an average rate of \u221216.09 km2\/year in the last 5 years, suggesting that the ISA of YRDHEEZ has decreased while its overall pattern was not significantly changed over time. The presented workflow can provide a reference for large-scale ISA mapping and its evolution analysis, especially in regions on estuarine deltas.<\/jats:p>","DOI":"10.3390\/rs15010136","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:53:11Z","timestamp":1672109591000},"page":"136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1\/2 on the GEE"],"prefix":"10.3390","volume":"15","author":[{"given":"Jiantao","family":"Liu","sequence":"first","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}]},{"given":"Yexiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0448-3463","authenticated-orcid":false,"given":"Xiaoqian","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/01944369608975688","article-title":"Impervious surfaces Coverage: The Emergence of a Key Environmental Indicator","volume":"62","author":"Arnold","year":"1996","journal-title":"J. Am. Plan. Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2006.09.003","article-title":"Comparison of Impervious surfaces area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery","volume":"106","author":"Yuan","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jia, Y., Tang, L., and Wang, L. (2017). Influence of Ecological Factors on Estimation of Impervious surfaces Area Using Landsat 8 Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9070751"},{"key":"ref_4","first-page":"182","article-title":"Temporal and spatial correlation between Impervious surfaces and surface runoff: A case study of the main urban area of Hangzhou city","volume":"24","author":"Yao","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_5","first-page":"100","article-title":"The Importance of Imperviousness","volume":"1","author":"Schueler","year":"1994","journal-title":"Watershed Prot. Tech."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.12.027","article-title":"Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover","volume":"175","author":"Song","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"135828","DOI":"10.1016\/j.scitotenv.2019.135828","article-title":"A comparative analysis of urban Impervious surfaces and green space and their dynamics among 318 different size cities in China in the past 25 years","volume":"706","author":"Dou","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0034-4257(02)00136-0","article-title":"Estimating Impervious surfaces distribution by spectral mixture analysis","volume":"84","author":"Wu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1080\/17538947.2013.866173","article-title":"Methods to extract Impervious surfaces areas from satellite images","volume":"7","author":"Lu","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1080\/10106049.2016.1273401","article-title":"Development of a modified bare soil and urban index for Landsat 8 satellite data","volume":"33","author":"Piyoosh","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1007\/s10661-021-09321-6","article-title":"Fusion of Sentinel-1 and Sentinel-2 data in mapping the Impervious surfaces at city scale","volume":"193","author":"Shrestha","year":"2021","journal-title":"Environ. Monit. Assess."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"34515","DOI":"10.1117\/1.JRS.14.034515","article-title":"Subpixel Impervious surfaces estimation in the Nansi Lake Basin using random forest regression combined with GF-5 hyperspectral data","volume":"14","author":"Liu","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_13","first-page":"420","article-title":"Composite kernel support vector regression model for hyperspectral image Impervious surfaces extraction","volume":"20","author":"Liu","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2016.01.003","article-title":"Annual dynamics of Impervious surfaces in the Pearl River Delta, China, from 1988 to 2013, using time series Landsat imagery","volume":"113","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","first-page":"253","article-title":"Application of random forest and Sentinel-1\/2 in the information extraction of impervious layers in Dongying City","volume":"33","author":"Liu","year":"2021","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_16","first-page":"1920","article-title":"Extraction and spatial analysis of Impervious surfaces in the Bohai Bay region based on OLI imagery","volume":"37","author":"Zhai","year":"2015","journal-title":"Resour. Sci."},{"key":"ref_17","first-page":"1469","article-title":"Extraction of the Impervious surfaces of typical cities in Xinjiang based on Sentinel-2A\/B and spatial difference analysis","volume":"26","author":"Duan","year":"2022","journal-title":"J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1080\/15481603.2017.1282414","article-title":"Semi-automatic mapping of anthropogenic Impervious surfaces in an urban\/suburban area using Landsat 8 satellite data","volume":"54","author":"Piyoosh","year":"2017","journal-title":"GIsci. Remote Sens."},{"key":"ref_19","first-page":"148","article-title":"Development status and future prospects of multi-source remote sensing image fusion","volume":"25","author":"Li","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.05.010","article-title":"Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery","volume":"130","author":"Mandianpari","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.isprsjprs.2021.12.008","article-title":"Hierarchical fusion of optical and dual-polarized SAR on Impervious surfaces mapping at city scale","volume":"184","author":"Sun","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, F., Li, E., Saibati, A., Zhang, L., Liu, W., and Hu, J. (2020). Estimation of large-scale Impervious surfaces percentage by fusion of multi-source time series remote sensing data. J. Remote Sens., 24.","DOI":"10.11834\/jrs.20209450"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, X., Yang, K., Wang, J., Wang, Z., Wang, L., and Su, F. (2022). Improving long-term Impervious surfaces percentage mapping in mountainous areas based on multi-source remote sensing data. Geocarto Int., 1\u201323.","DOI":"10.1080\/10106049.2022.2076908"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.5194\/essd-12-1625-2020","article-title":"Development of a global 30\u2009m Impervious surfaces map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform","volume":"12","author":"Zhang","year":"2020","journal-title":"Earth Syst. Sci Data."},{"key":"ref_25","first-page":"176","article-title":"Estimating Urban Impervious surfaces Percentage with ERS-1\/2 InSAR Data","volume":"12","author":"Jiang","year":"2008","journal-title":"J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.isprsjprs.2018.03.007","article-title":"A new scheme for urban Impervious surfaces classification from SAR images","volume":"139","author":"Zhang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111757","DOI":"10.1016\/j.rse.2020.111757","article-title":"Incorporating synthetic aperture radar and optical images to investigate the annual dynamics of anthropogenic Impervious surfaces at large scale","volume":"242","author":"Lin","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.landurbplan.2016.03.009","article-title":"Mapping urban Impervious surfaces with dual-polarimetric SAR data: An improved method","volume":"151","author":"Zhang","year":"2016","journal-title":"Landsc. Urban Plan."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, X., Tian, J., Li, X., Wang, L., Gong, H., Chen, B., Li, X., and Guo, J. (2022). Benefits of Google Earth Engine in remote sensing. J. Remote Sens., 26.","DOI":"10.11834\/jrs.20211317"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112002","DOI":"10.1016\/j.rse.2020.112002","article-title":"A summary of the special issue on remote sensing of land change science with Google earth engine","volume":"248","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, H., Wei, Y., Liu, C., Li, X., and Fang, H. (2019). A Scheme for the Long-Term Monitoring of Impervious\u2212Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11161891"},{"key":"ref_34","first-page":"4314","article-title":"Spatial and temporal variations in wetland landscape patterns in the Yellow River Delta based on Landsat images","volume":"38","author":"Lu","year":"2018","journal-title":"China Environ. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.jenvman.2017.07.049","article-title":"Measuring conflicts in the management of anthropized ecosystems: Evidence from a choice experiment in a human-created Mediterranean wetland","volume":"203","author":"Perni","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_36","first-page":"204","article-title":"Distribution of Micro-plastics in the Soil Covered by Different Vegetation in Yellow River Delta Wetland","volume":"42","author":"Yue","year":"2021","journal-title":"Environ. Sci."},{"key":"ref_37","first-page":"1696","article-title":"The time-space evolution characteristics of the vulnerability of land ecosystems and influencing factors: A case study of the Yellow River Delta Efficiency Eco-economic Zone","volume":"39","author":"Zhang","year":"2019","journal-title":"China Environ. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shao, Z., Fu, H., Fu, P., and Yin, L. (2016). Mapping Urban Impervious surfaces by Fusing Optical and SAR Data at the Decision Level. Remote Sens., 8.","DOI":"10.3390\/rs8110945"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2410","DOI":"10.1109\/JSTARS.2022.3157755","article-title":"Integrating Zhuhai-1 Hyperspectral Imagery With Sentinel-2 Multispectral Imagery to Improve High-Resolution Impervious surfaces Area Mapping","volume":"15","author":"Feng","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"589","article-title":"A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI)","volume":"5","author":"Xu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_42","first-page":"37","article-title":"An Effective Approach to Automatically Extract Urban Land-use from TM imagery","volume":"1","author":"Cha","year":"2003","journal-title":"J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Dong, X., Meng, Z., Wang, Y., Zhang, Y., Sun, H., and Wang, Q. (2021). Monitoring Spatiotemporal Changes of Impervious surfaces in Beijing City Using Random Forest Algorithm and Textural Features. Remote Sens., 13.","DOI":"10.3390\/rs13010153"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1080\/10106049.2019.1566406","article-title":"Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method","volume":"35","author":"Bramhe","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_45","first-page":"204","article-title":"Analyzing fine-scale wetland composition using high resolution imagery and texture features","volume":"23","author":"Szantoi","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_47","first-page":"127","article-title":"Urban land use classification based on remote sensing and multi-source geographic data","volume":"34","author":"Wu","year":"2022","journal-title":"Remote Sens. Nat. Resour."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"111199","DOI":"10.1016\/j.rse.2019.05.018","article-title":"Key issues in rigorous accuracy assessment of land cover products","volume":"231","author":"Stehman","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111630","DOI":"10.1016\/j.rse.2019.111630","article-title":"Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification","volume":"239","author":"Foody","year":"2020","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:52:04Z","timestamp":1760147524000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,26]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010136"],"URL":"https:\/\/doi.org\/10.3390\/rs15010136","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,26]]}}}