{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T11:29:46Z","timestamp":1742642986990,"version":"3.37.3"},"reference-count":93,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,3]],"date-time":"2021-04-03T00:00:00Z","timestamp":1617408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fujian educational research projects of young and middle-aged teachers","award":["JAT200453"]},{"name":"High level talents research project of Xiamen University of Technology","award":["4010520004"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41907611"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously.<\/jats:p>","DOI":"10.3390\/rs13071383","type":"journal-article","created":{"date-parts":[[2021,4,4]],"date-time":"2021-04-04T02:03:36Z","timestamp":1617501816000},"page":"1383","source":"Crossref","is-referenced-by-count":8,"title":["Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7849-2023","authenticated-orcid":false,"given":"Feng","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"},{"name":"Big Data Institute of Digital Natural Disaster Monitoring in Fujian, Xiamen University of Technology, Xiamen 361024, China"},{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, Xiamen 361005, China"}]},{"given":"Chenxing","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Center for Eco-Environmental Sciences, State Key Laboratory of Urban and Regional Ecology, Chinese Academy of Sciences, Beijing 100085, China"}]},{"given":"Yuansheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}]},{"given":"Zhenshi","family":"Yi","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}]},{"given":"Qiancong","family":"Fan","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University, Xiamen 361005, China"}]},{"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Yuejun","family":"Song","sequence":"additional","affiliation":[{"name":"Jiangxi Key Laboratory of Soil Erosion and Prevention, Jiangxi Institute of Soil and Water Conservation, Nanchang 330029, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free access to Landsat imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.11.032","article-title":"The global Landsat archive: Status, consolidation, and direction","volume":"185","author":"Wulder","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.rse.2019.02.016","article-title":"Benefits of the free and open Landsat data policy","volume":"224","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"unstructured":"(2021, March 10). Landsat Missions, Available online: https:\/\/www.usgs.gov\/core-science-systems\/nli\/landsat.","key":"ref_6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8967","DOI":"10.1109\/TGRS.2020.2992609","article-title":"Characterization of MSS Channel Reflectance and Derived Spectral Indices for Building Consistent Landsat 1-5 Data Record","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rse.2016.02.052","article-title":"An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations","volume":"185","author":"Holden","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"29092","DOI":"10.1080\/19443994.2016.1188734","article-title":"Landuse and NDVI change analysis of Sperchios river basin (Greece) with different spatial resolution sensor data by Landsat\/MSS\/TM and OLI","volume":"57","author":"Markogianni","year":"2016","journal-title":"Desalin. Water Treat."},{"doi-asserted-by":"crossref","unstructured":"Mohajane, M., Essahlaoui, A., Oudija, F., El Hafyani, M., El Hmaidi, A., El Ouali, A., Randazzo, G., and Teodoro, C.A. (2018). Land use\/land cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments, 5.","key":"ref_10","DOI":"10.3390\/environments5120131"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"310","DOI":"10.3390\/rs6010310","article-title":"Cross-comparison of vegetation indices derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jes.2017.02.009","article-title":"Challenges to quantitative applications of Landsat observations for the urban thermal environment","volume":"59","author":"Chen","year":"2017","journal-title":"J. Environ. Sci."},{"doi-asserted-by":"crossref","unstructured":"Chen, F., Lou, S., Fan, Q., Wang, C., Claverie, M., Wang, C., and Li, J. (2019). Normalized difference vegetation index continuity of the Landsat 4-5 MSS and TM: Investigations based on simulation. Remote Sens., 11.","key":"ref_14","DOI":"10.3390\/rs11141681"},{"doi-asserted-by":"crossref","unstructured":"Mancino, G., Ferrara, A., Padula, A., and Nol\u00e8, A. (2020). Cross-comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) derived vegetation indices in a Mediterranean environment. Remote Sens., 12.","key":"ref_15","DOI":"10.3390\/rs12020291"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/0034-4257(87)90053-8","article-title":"An assessment of Landsat MSS and TM data for urban and near-urban land-cover digital classification","volume":"21","author":"Haack","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1109\/TGRS.1987.289823","article-title":"Comparison of Landsat MSS and TM data for urban land-use classification","volume":"GE-25","author":"Khorram","year":"1987","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/S0034-4257(01)00254-1","article-title":"Landsat-7 ETM+ as an observatory for land cover: Initial radiometric and geometric comparisons with Landsat-5 Thematic Mapper","volume":"78","author":"Masek","year":"2001","journal-title":"Remote Sens. Environ."},{"unstructured":"Blonski, S., Glasser, G., Russell, J., Ryan, R., Terrie, G., and Zanoni, V. (2021, March 31). Synthesis of multispectral bands from hyperspectral data: Validation based on images acquired by AVIRIS, Hyperion, ALI, and ETM+, Available online: https:\/\/ntrs.nasa.gov\/search.jsp?R=20040010531.","key":"ref_19"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"813","DOI":"10.14358\/PERS.70.7.813","article-title":"A comparison of AVIRIS and Landsat for land use classification at the urban fringe","volume":"70","author":"Platt","year":"2004","journal-title":"Photogramm. Eng. Rem. S."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"unstructured":"Baumgardner, M.F., Biehl, L.L., and Landgrebe, D.A. (2021, March 31). 220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3. Available online: https:\/\/purr.purdue.edu\/publications\/1947\/1.","key":"ref_22"},{"unstructured":"(2021, March 10). Information on 220 Channel AVIRIS Data Set. Available online: https:\/\/engineering.purdue.edu\/~biehl\/MultiSpec\/aviris_documentation.html.","key":"ref_23"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/A:1024048429145","article-title":"The solar spectral irradiance from 200 to 2400 nm as measured by SOLSPEC Spectrometer from the ATLAS 123 and EURECA missions","volume":"214","author":"Thuillier","year":"2003","journal-title":"Sol. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summery of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A calibration and products validation status. Remote Sens., 9.","key":"ref_26","DOI":"10.3390\/rs9060584"},{"unstructured":"USGS (U.S. Geological Survey) (2021, March 31). Landsat 8 (L8) Data Users Handbook (LSDS-1574) (Version 5.0), Available online: https:\/\/prd-wret.s3.us-west-2.amazonaws.com\/assets\/palladium\/production\/atoms\/files\/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf.","key":"ref_27"},{"unstructured":"Berk, A., Anderson, G.P., Acharya, P.K., and Shettle, E.P. (2008). MODTRAN\u00ae5.2.0.0 User\u2019s Manual, Hanscom Air Force Base.","key":"ref_28"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.isprsjprs.2016.01.007","article-title":"Effect of emissivity uncertainty on surface temperature retrieval over urban areas: Investigations based on spectral libraries","volume":"114","author":"Chen","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combination for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"unstructured":"(2021, March 31). Landsat Surface Reflectance-Derived Spectral Indices, Available online: https:\/\/www.usgs.gov\/core-science-systems\/nli\/landsat\/landsat-surface-reflectance-derived-spectral-indices?qt-science_support_page_related_con=0#qt-science_support_page_related_con.","key":"ref_33"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"330","DOI":"10.3390\/rs1030330","article-title":"Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement","volume":"1","author":"Manandhar","year":"2009","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4573","DOI":"10.1080\/01431161.2014.930206","article-title":"Meta-discoveries form a synthesis of satellite-based land-cover mapping research","volume":"35","author":"Yu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An assessment of support vector machines for land cover classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support vector machines for classification in remote sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","first-page":"352","article-title":"A kernel function analysis for support vector machines for land cover classification","volume":"11","author":"Kavzoglu","year":"2009","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)-Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1006\/jbin.2001.1004","article-title":"A comparison of machine learning methods for the diagnosis of pigmented skin lesions","volume":"34","author":"Dreiseitl","year":"2001","journal-title":"J. Biomed. Inform."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3781","DOI":"10.1080\/01431160500166433","article-title":"Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images","volume":"26","author":"Maselli","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_45","first-page":"27","article-title":"Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms","volume":"12","author":"Otukei","year":"2012","journal-title":"Int. J. Appl. Earth Obs."},{"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","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 Recogn. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a Random Forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1080\/01431160701294661","article-title":"Multispectral land use classification using neural networks and support vector machines: One or the other, or both?","volume":"29","author":"Dixon","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.3390\/rs5073212","article-title":"Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of Random Forest classification of wetlands in Northern Minnesota","volume":"5","author":"Corcoran","year":"2013","journal-title":"Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"964","DOI":"10.3390\/rs6020964","article-title":"Comparison of classification algorithms and training sample sizes in urban land classification with Landsat Thematic Mapper imagery","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.rse.2005.08.011","article-title":"On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification","volume":"98","author":"McVicar","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"doi-asserted-by":"crossref","unstructured":"Joachims, T. (2005, January 7\u201311). A Support Vector Method for Multivariate Performance Measures. Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.","key":"ref_57","DOI":"10.1145\/1102351.1102399"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1080\/01431160500275762","article-title":"Comparing accuracy assessments to infer superiority of image classification methods","volume":"27","author":"Jia","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1080\/1747423X.2015.1130756","article-title":"The utility of earth observation technologies in understanding impacts of land reform in the eastern region of Zimbabwe","volume":"11","author":"Sibanda","year":"2016","journal-title":"J. Land Use Sci."},{"doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2013). Remote Sensing Digital Image Analysis: An Introduction, Springer. [5th ed.].","key":"ref_61","DOI":"10.1007\/978-3-642-30062-2"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5347","DOI":"10.3390\/rs70505347","article-title":"Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA","volume":"7","author":"Hao","year":"2015","journal-title":"Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat land cover classification methods: A review. Remote Sens., 9.","key":"ref_63","DOI":"10.3390\/rs9090967"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/S0034-4257(99)00055-3","article-title":"Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products","volume":"70","author":"Thomlinson","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2006.02.010","article-title":"Use of impervious surface in urban land-use classification","volume":"102","author":"Lu","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.1109\/TGRS.2003.818464","article-title":"Revised Landsat-5 TM radiometric calibration procedures, and post-calibration dynamic ranges","volume":"41","author":"Chander","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1109\/LGRS.2018.2806223","article-title":"Multisource earth observation data for land-cover classification using Random Forest","volume":"15","author":"Xu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/07038992.2015.1089401","article-title":"Large area mapping of annual land cover dynamics using multitemporal change detection and classification of Landsat time series data","volume":"41","author":"Franklin","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2018.02.064","article-title":"Improved mapping of forest type using spectral-temporal Landsat features","volume":"210","author":"Pasquarella","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1080\/01431160600784259","article-title":"Land cover classification using multi-temporal MERIS vegetation indices","volume":"28","author":"Dash","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1080\/2150704X.2013.828180","article-title":"A comparison of object-based and contextual pixel-based classifications using high and medium spatial resolution images","volume":"4","author":"Cai","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2014.03.007","article-title":"Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution","volume":"103","author":"Wang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Dannenberg, M.P., Hakkenberg, C.R., and Song, C.H. (2016). Consistent classification of Landsat time series with an improved Automatic Adaptive Signature Generalization Algorithm. Remote Sens., 8.","key":"ref_75","DOI":"10.3390\/rs8080691"},{"key":"ref_76","first-page":"27","article-title":"A comparison of the performance of pixel based and object based classifications over images with various spatial resolutions","volume":"2","author":"Gao","year":"2008","journal-title":"Online J. Earth Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_79","first-page":"245","article-title":"Comparison of advanced pixel based (ANN and SVM) and object-oriented classification approaches using Landsat-7 ETM+ data","volume":"2","author":"Pakhalea","year":"2010","journal-title":"Int. J. Eng. Technol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1080\/01431160903571791","article-title":"Comparison of pixel- and object-based classification in land cover change mapping","volume":"32","author":"Robertson","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1080\/01431161.2012.724540","article-title":"Does spatial resolution matter? A multiscale comparison of object-based and pixel-based methods for detecting change associated with gas well drilling operations","volume":"34","author":"Baker","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_82","first-page":"259","article-title":"Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping","volume":"35","author":"Poursanidis","year":"2015","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.isprsjprs.2014.09.005","article-title":"Use of Landsat and Corona data for mapping forest cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil","volume":"103","author":"Song","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.3390\/rs5052411","article-title":"Land use\/land cover change analysis using object-based classification approach in Munessa-Shashemene landscape of the Ethiopian Highlands","volume":"5","author":"Kindu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30 m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1109\/TGRS.2006.890414","article-title":"One-class classification for mapping a specific land-cover class: SVDD classification of Fenland","volume":"45","author":"Boyd","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Karakehayov, Z. (2012). Making Use of the Landsat 7 SLC-off ETM+ Image through Different Recovering Approaches. Data Acquisition Applications, IntechOpen Limited.","key":"ref_89","DOI":"10.5772\/2596"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2017.10.031","article-title":"Evaluating land surface albedo estimation from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach","volume":"204","author":"He","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"112181","DOI":"10.1016\/j.rse.2020.112181","article-title":"Improving Landsat multispectral scanner (MSS) geolocation by least-squares-adjustment based time-series co-registration","volume":"252","author":"Yan","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2019.09.009","article-title":"Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images","volume":"157","author":"Yoo","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1383\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T06:38:02Z","timestamp":1736231882000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,3]]},"references-count":93,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071383"],"URL":"https:\/\/doi.org\/10.3390\/rs13071383","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,4,3]]}}}