{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T16:52:05Z","timestamp":1768668725324,"version":"3.49.0"},"reference-count":72,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["821016"],"award-info":[{"award-number":["821016"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National key research and development program of China","award":["2019YFA0607201"],"award-info":[{"award-number":["2019YFA0607201"]}]},{"name":"Science and Technology Commission of Shanghai","award":["19DZ1203405"],"award-info":[{"award-number":["19DZ1203405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Urbanisation processes inherently influence land cover (LC) and have dramatic impacts on the amount, distribution and quality of vegetation cover. The latter are the source of ecosystem services (ES) on which humans depend. However, the temporal and thematical dimensions are not documented in a comparable manner across Europe and China. Three cities in China and three cities in Europe were selected as case study areas to gain a picture of spatial urban dynamics at intercontinental scale. First, we analysed available global and continental thematic LC products as a data pool for sample selection and referencing our own mapping model. With the help of the Google Earth Engine (GEE) platform and earth observation data, an automatic LC mapping method tailored for more detailed ES features was proposed. To do so, differentiated LC categories were quantified. In order to obtain a balance between efficiency and high classification accuracy, we developed an optimal classification model by evaluating the importance of a large number of spectral, texture-based indices and topographical information. The overall classification accuracies range between 73% and 95% for different time slots and cities. To capture ES related LC categories in great detail, deciduous and coniferous forests, cropland, grassland and bare land were effectively identified. To understand inner urban options for potential new ES, dense and dispersed built-up areas were differentiated with good results. In addition, this study focuses on the differences in the characteristics of urban expansion witnessed in China and Europe. Our results reveal that urbanisation has been more intense in the three Chinese cities than in the three European cities, with an 84% increase in the entire built-up area over the last two decades. However, our results also show the results of China\u2019s ecological restoration policies, with a total of 963 km2 of new green and blue LC created in the last two decades. We proved that our automatic mapping can be effectively applied to future studies, and the monitoring results will be useful for consecutive ES analyses aimed at achieving more environmentally friendly cities.<\/jats:p>","DOI":"10.3390\/rs13091744","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T05:10:55Z","timestamp":1619759455000},"page":"1744","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Integrated Mapping of Spatial Urban Dynamics\u2014A European-Chinese Exploration. Part 1\u2014Methodology for Automatic Land Cover Classification Tailored towards Spatial Allocation of Ecosystem Services Features"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4740-1202","authenticated-orcid":false,"given":"Ellen","family":"Banzhaf","sequence":"first","affiliation":[{"name":"UFZ-Helmholtz Centre for Environmental Research, Department Urban and Environmental Sociology, Permoserstr. 15, 04318 Leipzig, Germany"}]},{"given":"Wanben","family":"Wu","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Shanghai Institute of EcoChongming (SIEC), Fudan University, Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1787-608X","authenticated-orcid":false,"given":"Xiangyu","family":"Luo","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3474-6367","authenticated-orcid":false,"given":"Julius","family":"Knopp","sequence":"additional","affiliation":[{"name":"UFZ-Helmholtz Centre for Environmental Research, Department Urban and Environmental Sociology, Permoserstr. 15, 04318 Leipzig, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2020, January 26). World Urbanization Prospects: The 2018 Revision. Key Facts. Available online: https:\/\/population.un.org\/wup\/Publications\/Files\/WUP2018-KeyFacts.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Baker, J.L. (2012). Climate Change, Disaster Risk, and the Urban Poor: Cities Building Resilience for a Changing World, World Bank.","DOI":"10.1596\/978-0-8213-8845-7"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jha, A.K., Miner, T.W., and Stanton-Geddes, Z. (2013). Building Urban Resilience: Principles, Tools, and Practice, World Bank.","DOI":"10.1596\/978-0-8213-8865-5"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Heinrichs, D., Krellenberg, K., Hansj\u00fcrgens, B., and Mart\u00ednez, F. (2012). Risk Habitat Megacity, Springer Science & Business Media.","DOI":"10.1007\/978-3-642-11544-8"},{"key":"ref_5","unstructured":"UN SDGs (2021, February 02). Transforming Our World: The 2030 Agenda for Sustainable Development. Resolution Adopted by the UN General Assembly. 25 September 2015. Available online: https:\/\/sustainabledevelopment.un.org\/post2015\/transformingourworld."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Weng, Q., Quattrochi, D.A., and Gamba, P. (2018). The Global Urban Footprint. Urban Remote Sensing, Taylor & Francis. [2nd ed.].","DOI":"10.1201\/9781315166612"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.progress.2011.04.001","article-title":"The dimensions of global urban expansion: Estimates and projections for all countries, 2000\u20132050","volume":"75","author":"Angel","year":"2011","journal-title":"Progr. Plan."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111510","DOI":"10.1016\/j.rse.2019.111510","article-title":"Annual maps of global artificial impervious area (GAIA) between 1985 and 2018","volume":"236","author":"Gong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1038\/s41893-020-0521-x","article-title":"High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015","volume":"3","author":"Liu","year":"2020","journal-title":"Nat. Sustain."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/513030a","article-title":"Satellites: Make Earth observations open access","volume":"513","author":"Wulder","year":"2014","journal-title":"Nature"},{"key":"ref_12","unstructured":"Mar\u00e7al, A. (2006). Accuracy assessment of the Portuguese CORINE Land Cover map. Global Developments in Environmental Earth Observation from Space, Millpress."},{"key":"ref_13","unstructured":"B\u00fcttner, G., and Maucha, G. (2016). The Thematic Accuracy of Corine Land Cover 2000 Assessment Using LUCAS (Land Use\/Cover Area Frame Statistical Survey), European Environment Agency. Available online: https:\/\/land.copernicus.eu\/user-corner\/technical-library\/technical_report_7_2006.pdf."},{"key":"ref_14","first-page":"1","article-title":"The content and accuracy of the CORINE Land Cover dataset for Norway","volume":"96","author":"Strand","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","unstructured":"European Commission (2020, January 26). Mapping Human Presence on Earth. The Global Human Settlements Layer (GHSL). Available online: https:\/\/ec.europa.eu\/jrc\/sites\/jrcsh\/files\/jrc-ghsl-infographics-key_messages.pdf."},{"key":"ref_16","unstructured":"European Commission (2020, January 26). Urban Atlas 2018 Mapping Guide. v6.7. Available online: https:\/\/land.copernicus.eu\/user-corner\/technical-library\/urban-atlas-mapping-guide."},{"key":"ref_17","unstructured":"Copernicus (2020, January 26). Urban Atlas 2012 validation report. GMES Initial Operations\/Copernicus Land Monitoring Services\u2014Validation of Products, Report Issue 1.2. Available online: https:\/\/land.copernicus.eu\/user-corner\/technical-library\/ua-2012-validation-report."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1080\/17538947.2016.1151956","article-title":"Assessing the suitability of GlobeLand30 for mapping land cover in Germany","volume":"9","author":"Arsanjani","year":"2016","journal-title":"Int. J. Digit. Earth."},{"key":"ref_19","unstructured":"Ballin, M., Barcaroli, G., Masselli, M., and Scarn\u00f3, M. (2018). Redesign Sample for Land Use\/Cover Area Frame Survey (LUCAS) 2018, Publications Office of the European Union."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1007\/s11430-018-9255-3","article-title":"GlobeLand30: Operational global land cover mapping and big-data analysis","volume":"61","author":"Chen","year":"2018","journal-title":"Sci. China Earth Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5325","DOI":"10.3390\/rs6065325","article-title":"A Circa 2010 Thirty Meter Resolution Forest Map for China","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_22","unstructured":"Xu, X., Pang, Z., and Yu, X. (2014). Spatial-Temporal Pattern Analysis of Land Use\/Cover Change: Methods and Application, Scientific and Technical Documentation Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2015.07.017","article-title":"A Linear Dirichlet Mixture Model for decomposing scenes: Application to analyzing urban functional zonings","volume":"169","author":"Zhang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.isprsjprs.2017.09.007","article-title":"Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data","volume":"132","author":"Zhang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1080\/15481603.2020.1724707","article-title":"Large-scale urban functional zone mapping by integrating remote sensing images and open social data","volume":"57","author":"Du","year":"2020","journal-title":"GIsci Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2015.08.030","article-title":"Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products","volume":"169","author":"Claverie","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0734-189X(84)90197-X","article-title":"Segmentation of a high-resolution urban scene using texture operators","volume":"25","author":"Conners","year":"1984","journal-title":"Comput. Gr. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2003.11.008","article-title":"Satellite-Based Modeling of Gross Primary Production in an Evergreen Needleleaf Forest","volume":"89","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index NDWI to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","unstructured":"Alasta, A.F. (2011, January 23\u201324). Using Remote Sensing data to identify iron deposits in central western Libya. Proceedings of the International Conference on Emerging Trends in Computer and Image Processing ICETCIP\u20192011, Bangkok, Thailand."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Singh, V.P., Singh, P., and Haritashya, U.K. (2011). Normalized-Difference Snow Index (NDSI). Encyclopedia of Snow, Ice, and Glaciers, Encyclopedia of Earth Sciences; Springer.","DOI":"10.1007\/978-90-481-2642-2"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.rse.2016.07.016","article-title":"Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+ imagery","volume":"184","author":"Coulter","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_40","unstructured":"Bannari, A., Asalhi, H., and Teillet, P.M. (2002, January 24\u201328). Transformed difference vegetation index (TDVI) for vegetation cover mapping. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium in Toronto, Toronto, ON, Canada."},{"key":"ref_41","first-page":"1","article-title":"Development of new indices for extraction of built-up area & bare soil from Landsat data","volume":"1","author":"Waqar","year":"2012","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"10017","DOI":"10.3390\/rs70810017","article-title":"Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest","volume":"7","author":"Karlson","year":"2015","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5031","DOI":"10.1080\/01431160210121764","article-title":"Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas","volume":"23","author":"Price","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","first-page":"220","article-title":"Continuous field mapping of Mediterranean wetlands using sub-pixel spectral signatures and multi-temporal Landsat data","volume":"28","author":"Reschke","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","unstructured":"Jarvis, A., Rubiano, J., Nelson, A., Farrow, A., and Mulligan, M. (2004). Practical Use of SRTM Data in the Tropics: Comparisons with Digital Elevation Models Generated from Cartographic Data, International Centre for Tropical, Agriculture (CIAT)."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1016\/j.rse.2007.08.025","article-title":"Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments","volume":"112","author":"Sesnie","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_48","unstructured":"Simonetti, E., Simonetti, D., and Preatoni, D. (2014). Phenology-Based Land Cover Classification Using Landsat 8 Time Series, Publications Office of the European Union."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112105","DOI":"10.1016\/j.rse.2020.112105","article-title":"Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection","volume":"251","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F.A. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"25:1","DOI":"10.1186\/1471-2105-8-25","article-title":"Bias in random forest variable importance measures: Illustrations, sources and a solution","volume":"8","author":"Strobl","year":"2007","journal-title":"BMC Bioinform."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1080\/15481603.2019.1690780","article-title":"Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","volume":"57","author":"Gumma","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8489","DOI":"10.3390\/rs70708489","article-title":"On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping","volume":"7","author":"Millard","year":"2015","journal-title":"Remote Sens."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1111\/j.1467-9671.2010.01229.x","article-title":"Modeling the potential distribution of pine forests susceptible to sirex noctilio infestations in Mpumalanga, South Africa","volume":"14","author":"Ismail","year":"2010","journal-title":"Trans. GIS"},{"key":"ref_58","first-page":"18","article-title":"Classification and regression by random forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R. News"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cageo.2017.02.012","article-title":"Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery","volume":"103","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"623","DOI":"10.2747\/1548-1603.49.5.623","article-title":"An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA","volume":"49","author":"Ghimire","year":"2012","journal-title":"GISci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier\u2014The Role of Image Composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_64","first-page":"234","article-title":"Urban land cover dynamics and their impact on ecosystem services in Kigali, Rwanda using multi-temporal Landsat data","volume":"13","author":"Mugiraneza","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_65","unstructured":"(2021, February 02). The State Council of the People\u2032s Republic of China, 2017. Create a New Situation for Ecological Civilization Construction, Available online: http:\/\/www.gov.cn\/xinwen\/2017-08\/02\/content_5215591.html."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Kadhim, N., Mourshed, M., and Bray, M. (2016). Advances in remote sensing applications for urban sustainability. Euro-Mediterr. J. Environ. Integr., 1.","DOI":"10.1007\/s41207-016-0007-4"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40808-016-0265-9","article-title":"Dynamics of surface urban biophysical compositions and its impact on land surface thermal field. Model","volume":"2","author":"Ishola","year":"2016","journal-title":"Earth Syst. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Demuzere, M., Bechtel, B., Middel, A., and Mills, G. (2019). Mapping Europe into local climate zones. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0214474"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2017.05.024","article-title":"Using the 500m MODIS land cover product to derive a consistent continental scale 30m Landsat land cover classification","volume":"197","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"6335","DOI":"10.1126\/science.abe8628","article-title":"Using satellite imagery to understand and promote sustainable development","volume":"371","author":"Burke","year":"2021","journal-title":"Science"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.rse.2018.11.012","article-title":"Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States","volume":"221","author":"Chastain","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Jones, S., Soto-Berelov, M., Haywood, A., and Hislop, S. (2020). Landsat time-series for estimating forest aboveground biomass and Its dynamics across space and time: A review. Remote Sens., 12.","DOI":"10.3390\/rs12010098"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1744\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:55:56Z","timestamp":1760162156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/9\/1744"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,30]]},"references-count":72,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13091744"],"URL":"https:\/\/doi.org\/10.3390\/rs13091744","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,30]]}}}