{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T14:36:05Z","timestamp":1770474965453,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,6]],"date-time":"2021-11-06T00:00:00Z","timestamp":1636156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Regional or continental-scale land cover mapping requires various amounts of months of multi-temporal satellite data to pick phenological variation in vegetation, enhancing differentiability among surface cover types and improving accuracy. However, little has been addressed about the number of months\/multi-temporal images needed to obtain the best result and the impact of using different amounts of these data on the accuracy of individual classes. This work aimed to analyze these effects by utilizing the various amounts of months of time series FengYun-3C (FY-3C) data within one year for land cover mapping of parts of Africa using a random forest classifier. The study area covers roughly one-third of Africa, including eastern, central, and northern parts of the continent. One-year FY-3C ten-day composite images consisting of eleven-band each with 1-km spatial resolution were divided into seven input datasets that comprise stacked images of 1-month, 3-month, 6-month, consecutive 9-month, 12-month, selected images from 12 months using band\/feature importance, and selected 9-month. Comparisons of these datasets on independent test samples revealed that overall accuracy, kappa coefficient, and the accuracy of the individual classes generally increase significantly with increasing the number of data\/months. However, the highest accuracy and kappa coefficient, 0.86 and 0.83, were obtained by processing selected 9-month imageries. The second maximum accuracy and kappa (0.85 and 0.82,) were found by manipulating 12-month scenes which are the same as the results obtained by applying feature reduction. Although 4% and 5% higher accuracy were achieved by manipulating 3-month and 6-month data relative to 1-month imageries, no variation of accuracy was observed between six- and nine-months of consecutive data, both yielding equal accuracy and kappa value (0.84 and 0.81) indicating redundancy of information. Overall, the high accuracy results show the feasibility of FY-3C data for land cover mapping of Africa.<\/jats:p>","DOI":"10.3390\/rs13214461","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T20:42:54Z","timestamp":1636317774000},"page":"4461","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Tesfaye","family":"Adugna","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"},{"name":"College of Applied Sciences, Addis Ababa Science and Technology University (AASTU), Addis Ababa P.O. Box 16417, Ethiopia"}]},{"given":"Wenbo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}]},{"given":"Jinlong","family":"Fan","sequence":"additional","affiliation":[{"name":"China Meteorological Administration, National Satellite Meteorological Center, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,6]]},"reference":[{"key":"ref_1","first-page":"207","article-title":"Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale","volume":"13","author":"Roujean","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1080\/014311600210092","article-title":"Land Cover Mapping of Large Areas from Satellites: Status and Research Priorities","volume":"21","author":"Cihlar","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"27909","DOI":"10.1029\/1999JD900243","article-title":"Land cover from multiple thematic mapper scenes using a new enhancement-classification methodology","volume":"104","author":"Beaubien","year":"1999","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_4","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."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2003.11.016","article-title":"Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data. Remote","volume":"90","author":"Latifovic","year":"2004","journal-title":"Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0034-4257(02)00078-0","article-title":"Global land cover mapping from MODIS: Algorithms and early results","volume":"83","author":"Friedl","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_7","unstructured":"Smets, B., Buchhorn, M., Lesiv, M., and Tsendbazar, N.-E. (2017). Copernicus Global Land Operations \u201cVegetation and Energy\u201d, Copernicus."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/0034-4257(95)00210-3","article-title":"Land cover classification with AVHRR multichannel composites in northern environments","volume":"58","author":"Cihlar","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"37","DOI":"10.14358\/PERS.81.1.37","article-title":"Optimal Land Cover Mapping and Change Analysis in Northeastern Oregon Using Landsat Imagery","volume":"81","author":"Campbell","year":"2015","journal-title":"Photogramm. Eng. Remote. Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3289","DOI":"10.1080\/014311697217099","article-title":"The IGBP-DIS global 1km land cover data set, DISCover: First results","volume":"18","author":"Loveland","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"703","DOI":"10.11728\/cjss2014.05.703","article-title":"FY-3 meteorological satellites and the applications","volume":"34","author":"Tang","year":"2014","journal-title":"China J. Space Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1007\/s13351-019-9063-4","article-title":"Capability of Fengyun-3D Satellite in Earth System Observation","volume":"33","author":"Yang","year":"2019","journal-title":"J. Meteorol. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4866","DOI":"10.1109\/TGRS.2018.2841827","article-title":"Prelaunch Calibration and Radiometric Performance of the Advanced MERSI II on FengYun-3D. IEEE Trans. Geosci","volume":"56","author":"Xu","year":"2018","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4846","DOI":"10.1109\/TGRS.2012.2197826","article-title":"Overview of FY-3 Payload and Ground Application System","volume":"50","author":"Yang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1007\/s13351-020-0027-5","article-title":"Vegetation Products Derived from Fengyun-3D Medium Resolution Spectral Imager-II","volume":"34","author":"Han","year":"2020","journal-title":"J. Meteorol. Res."},{"key":"ref_16","unstructured":"(2021, April 29). USGS, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","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_20","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."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/S0034-4257(97)00049-7","article-title":"Decision tree classification of land cover from remotely sensed data","volume":"61","author":"Friedl","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1080\/014311600210218","article-title":"A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products","volume":"21","author":"Hansen","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","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_27","first-page":"S27","article-title":"Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms","volume":"12S","author":"Otukei","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"RandomForests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_29","unstructured":"Tso, B., and Mather, P. (2009). Classification Methods for Remotely Sensed Data, CRC Press."},{"key":"ref_30","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_31","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kulkarni, V.Y., and Sinha, P.K. (2012, January 18\u201320). Pruning of random forest classifiers: A survey and future directions. Proceedings of the 2012 International Conference on Data Science & Engineering (ICDSE), Kerala, India.","DOI":"10.1109\/ICDSE.2012.6282329"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"5166","DOI":"10.1080\/01431161.2013.788261","article-title":"Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests","volume":"34","author":"Guan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","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_36","first-page":"18","article-title":"Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery","volume":"18","author":"Noi","year":"2018","journal-title":"Sensors"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/10106048709354084","article-title":"Introductory digital image processing: A remote sensing perspective","volume":"2","author":"Jensen","year":"1987","journal-title":"Geocarto Int."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9655","DOI":"10.3390\/rs70809655","article-title":"An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms","volume":"7","author":"Colditz","year":"2015","journal-title":"Remote Sens."},{"key":"ref_39","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, The Guilford Press. [5th ed.]."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4461\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:26:51Z","timestamp":1760167611000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4461"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,6]]},"references-count":39,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214461"],"URL":"https:\/\/doi.org\/10.3390\/rs13214461","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,6]]}}}