{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T11:35:49Z","timestamp":1760441749274,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005269","name":"Anhui Provincial Department of Education","doi-asserted-by":"publisher","award":["KJ2018A0326"],"award-info":[{"award-number":["KJ2018A0326"]}],"id":[{"id":"10.13039\/501100005269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Compositing is a fundamental pre-processing for remote sensing images. Landsat series optical satellite images are influenced by cloud coverage, acquisition time, sensor types, and seasons, which make it difficult to obtain continuous cloud-free observations. It limits the potential use and analysis of time series images. Therefore, global change researchers urgently need to \u2018composite\u2019 multi-sensor and multi-temporal images. Many previous studies have used isolated pixel-based algorithms to composite Landsat images; however, this study is different and develops a batch pixel-based algorithm for composing continuous cloud-free Landsat images. The algorithm chooses the best scene as the reference image using the user-specified image ID or related parameters. Further, it accepts all valid pixels in the reference image as the main part of the result and develops a priority coefficient model. Development of this model is based on the criteria of five factors including cloud coverage, acquisition time, acquisition year, observation seasons, and sensor types to select substitutions for the missing pixels in batches and to merge them into the final composition. This proposed batch pixel-based algorithm may provide reasonable compositing results on the basis of the experimental test results of all Landsat 8 images in 2019 and the visualization results of 12 locations in 2020. In comparison with the isolated pixel-based algorithms, our algorithm eliminates band dispersion, requires fewer images, and enhances the composition\u2019s pixel concentration considerably. The algorithm provides a complete and practical framework for time series image processing for Landsat series satellites, and has the potential to be applied to other optical satellite images as well.<\/jats:p>","DOI":"10.3390\/rs14174252","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4252","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9759-0702","authenticated-orcid":false,"given":"Jianzhou","family":"Li","sequence":"first","affiliation":[{"name":"School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China"},{"name":"Engineering Technology Research Center of Resources Environment and GIS, Anhui Province, Wuhu 241003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0004-5025","authenticated-orcid":false,"given":"Jinji","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Geography and Tourism, Anhui Normal University, Wuhu 241003, China"},{"name":"Engineering Technology Research Center of Resources Environment and GIS, Anhui Province, Wuhu 241003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5871-0502","authenticated-orcid":false,"given":"Xiaojiao","family":"Ye","sequence":"additional","affiliation":[{"name":"Collection and Editing Department of Library, Wannan Medical College, Wuhu 241003, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,29]]},"reference":[{"key":"ref_1","unstructured":"USGS (2022, April 06). Landsat Missions, Available online: https:\/\/www.usgs.gov\/landsat-missions\/landsat-9."},{"key":"ref_2","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."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pontes-Lopes, A., Dalagnol, R., Dutra, A.C., de Jesus Silva, C.V., de Alencastro Gra\u00e7a, P.M.L., and de Oliveira e Cruz de Arag\u00e3o, L.E. (2022). Quantifying post-fire changes in the aboveground biomass of an Amazonian forest based on field and remote sensing data. Remote Sens., 14.","DOI":"10.3390\/rs14071545"},{"key":"ref_4","first-page":"e00366","article-title":"Climate change and its effects on vegetation phenology across ecoregions of Ethiopia","volume":"13","author":"Workie","year":"2018","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_5","first-page":"102285","article-title":"All models of satellite-derived phenology are wrong, but some are useful: A case study from northern Australia","volume":"97","author":"Younes","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ecolmodel.2015.07.017","article-title":"A review of methods, data, and models to assess changes in the value of ecosystem services from land degradation and restoration","volume":"319","author":"Grace","year":"2016","journal-title":"Ecol. Model."},{"key":"ref_7","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_8","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_9","doi-asserted-by":"crossref","unstructured":"Hemati, M., Hasanlou, M., Mahdianpari, M., and Mohammadimanesh, F. (2021). A systematic review of Landsat data for change detection applications: 50 years of monitoring the earth. Remote Sens., 13.","DOI":"10.3390\/rs13152869"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A review of large area monitoring of land cover change using Landsat data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/07038992.2014.945827","article-title":"Pixel-based image compositing for large-area dense time series applications and science","volume":"40","author":"White","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111968","DOI":"10.1016\/j.rse.2020.111968","article-title":"Landsat 9: Empowering open science and applications through continuity","volume":"248","author":"Masek","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-Century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41597-021-00867-1","article-title":"A map of the extent and year of detection of oil palm plantations in Indonesia, Malaysia and Thailand","volume":"8","author":"Danylo","year":"2021","journal-title":"Sci. Data"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/nature20584","article-title":"High-resolution mapping of global surface water and its long-term changes","volume":"540","author":"Pekel","year":"2016","journal-title":"Nature"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.5194\/nhess-21-1495-2021","article-title":"HazMapper: A global open-source natural hazard mapping application in Google Earth Engine","volume":"21","author":"Scheip","year":"2021","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101157","DOI":"10.1016\/j.ecoinf.2020.101157","article-title":"Forecasting environmental factors and zooplankton of Bakreswar reservoir in India using time series model","volume":"60","author":"Banerjee","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, C., Webb, J.A., and Stewardson, M.J. (2022). Modelling Impacts of Environmental Water on Vegetation of a Semi-Arid Floodplain\u2013Lakes System Using 30-Year Landsat Data. Remote Sens., 14.","DOI":"10.3390\/rs14030708"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1139\/cjfr-2020-0353","article-title":"Trends in wildfire burn severity across Canada, 1985 to 2015","volume":"51","author":"Guindon","year":"2021","journal-title":"Can. J. For. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1080\/01431168608948945","article-title":"Characteristics of maximum-value composite images from temporal AVHRR data","volume":"7","author":"Holben","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4167","DOI":"10.1016\/j.rse.2008.06.010","article-title":"Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America","volume":"112","author":"Luo","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1016\/j.rse.2008.03.009","article-title":"Multi-temporal MODIS\u2013Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2009.08.011","article-title":"Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States","volume":"114","author":"Roy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2010.10.001","article-title":"Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia","volume":"115","author":"Potapov","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"A pixel-based landsat compositing algorithm for large area land cover mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"Google Earth Engine (2022, June 02). API Reference. Available online: https:\/\/developers.google.com\/earth-engine\/apidocs."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, C., Wu, Z., Qin, Q., and Ye, X. (2022). A framework of generating land surface reflectance of China early Landsat MSS images by visibility data and its evaluation. Remote Sens., 14.","DOI":"10.3390\/rs14081802"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4\u20138 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_31","unstructured":"FAO (2021, December 20). Methods & Standards. Available online: http:\/\/www.fao.org\/ag\/agn\/nutrition\/Indicatorsfiles\/Agriculture.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1080\/01431169308904400","article-title":"On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces","volume":"14","author":"Owe","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","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":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"209","DOI":"10.2166\/wst.1998.0470","article-title":"Monitoring of a water basin area in Istanbul using remote sensing data","volume":"38","author":"Goksel","year":"1998","journal-title":"Water Sci. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1109\/TGRS.2011.2164087","article-title":"Development of the landsat data continuity mission cloud-cover assessment algorithms","volume":"50","author":"Scaramuzza","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106694","DOI":"10.1016\/j.agwat.2020.106694","article-title":"On-farm reservoir monitoring using Landsat inundation datasets","volume":"246","author":"Perin","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sirin, A., and Medvedeva, M. (2022). Remote sensing mapping of peat-fire-burnt areas: Identification among other wildfires. Remote Sens., 14.","DOI":"10.3390\/rs14010194"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2020.02.008","article-title":"Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning","volume":"162","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2877","DOI":"10.1080\/01431161.2019.1697006","article-title":"Cloud and cloud shadow masking for Sentinel-2 using multitemporal images in global area","volume":"41","author":"Candra","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e2020JG005942","DOI":"10.1029\/2020JG005942","article-title":"Warming as a Driver of Vegetation Loss in the Sonoran Desert of California","volume":"126","author":"Hantson","year":"2021","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_41","first-page":"102386","article-title":"Sub-annual tropical forest disturbance monitoring using harmonized Landsat and Sentinel-2 data","volume":"102","author":"Chen","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Guan, X., Huang, C., and Zhang, R. (2021). Integrating MODIS and Landsat data for land cover classification by multilevel decision rule. Land, 10.","DOI":"10.3390\/land10020208"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, S., Wang, J., Li, D., Ran, Z., and Yang, B. (2021). Evaluation of Landsat 8-like land surface temperature by fusing Landsat 8 and MODIS land surface temperature product. Processes, 9.","DOI":"10.3390\/pr9122262"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4252\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:19:25Z","timestamp":1760141965000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,29]]},"references-count":43,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174252"],"URL":"https:\/\/doi.org\/10.3390\/rs14174252","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,8,29]]}}}