{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:16:24Z","timestamp":1762377384778,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fujian Natural Science Foundation, China","award":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"],"award-info":[{"award-number":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"]}]},{"name":"High-Level Talents Research Project of Xiamen University of Technology","award":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"],"award-info":[{"award-number":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"]}]},{"name":"Fujian Educational Research Projects of Young and Middle-Aged Teachers","award":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"],"award-info":[{"award-number":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"]}]},{"name":"National Natural Science Foundation of China","award":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"],"award-info":[{"award-number":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"]}]},{"name":"Water Conservancy Science and Technology Project of Jiangxi Province","award":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"],"award-info":[{"award-number":["2021J011190","4010520004","JAT200453","42101250","202224ZDKT11","202123YBKT16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multispectral images accessible free of charge have increased significantly from the acquisitions by the wide-field-of-view (WFV) sensors onboard Gaofen-1\/-6 (GF-1\/-6), the Operational Land Imager (OLI) onboard Landsat 8 (L8), and the Multi-Spectral Instrument (MSI) onboard Sentinel-2 (S2). These images with medium spatial resolutions are beneficial for land-cover mapping to monitor local to global surface dynamics. Comparative analyses of the four sensors in classification were made under different scenarios with five classifiers, mainly based on the simulated multispectral reflectance from well-processed hyperspectral data. With channel reflectance, differences in classification between the L8 OLI and the S2 MSI were generally dependent on the classifier considered, although the two sensors performed similarly. Meanwhile, without channels over the shortwave infrared region, the GF-1\/-6 WFVs showed inferior performances. With channel reflectance, the support vector machine (SVM) with Gaussian kernel generally outperformed other classifiers. With the SVM, on average, the GF-1\/-6 WFVs and the L8 OLI had great increases (more than 15%) in overall accuracy relative to using the maximum likelihood classifier (MLC), whereas the overall accuracy improvement was about 13% for the S2 MSI. Both SVM and random forest (RF) had greater overall accuracy, which partially solved the problems of imperfect channel settings. However, under the scenario with a small number of training samples, for the GF-1\/-6 WFVs, the MLC showed approximate or even better performance compared to RF. Since several factors possibly influence a classifier\u2019s performance, attention should be paid to a comparison and selection of methods. These findings were based on the simulated multispectral reflectance with focusing on spectral channel (i.e., number of channels, spectral range of the channel, and spectral response function), whereas spatial resolution and radiometric quantization were not considered. Furthermore, a limitation of this paper was largely associated with the limited spatial coverage. More case studies should be carried out with real images over areas with different geographical and environmental backgrounds. To improve the comparability in classification among different sensors, further investigations are definitely required.<\/jats:p>","DOI":"10.3390\/rs15092373","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Comparison of Simulated Multispectral Reflectance among Four Sensors in Land Cover Classification"],"prefix":"10.3390","volume":"15","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"}]},{"given":"Wenhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}]},{"given":"Yuejun","family":"Song","sequence":"additional","affiliation":[{"name":"Jiangxi Key Laboratory of Soil Erosion and Prevention, Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2473-491X","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Chenxing","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"ref_1","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. 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