{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T07:56:20Z","timestamp":1781769380736,"version":"3.54.5"},"reference-count":33,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"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>Masking of clouds, cloud shadow, water and snow\/ice in optical satellite imagery is an important step in automated processing chains. We compare the performance of the masking provided by Fmask (\u201cFunction of mask\u201d implemented in FORCE), ATCOR (\u201cAtmospheric Correction\u201d) and Sen2Cor (\u201cSentinel-2 Correction\u201d) on a set of 20 Sentinel-2 scenes distributed over the globe covering a wide variety of environments and climates. All three methods use rules based on physical properties (Top of Atmosphere Reflectance, TOA) to separate clear pixels from potential cloud pixels, but they use different rules and class-specific thresholds. The methods can yield different results because of different definitions of the dilation buffer size for the classes cloud, cloud shadow and snow. Classification results are compared to the assessment of an expert human interpreter using at least 50 polygons per class randomly selected for each image. The class assignment of the human interpreter is considered as reference or \u201ctruth\u201d. The interpreter carefully assigned a class label based on the visual assessment of the true color and infrared false color images and additionally on the bottom of atmosphere (BOA) reflectance spectra. The most important part of the comparison is done for the difference area of the three classifications considered. This is the part of the classification images where the results of Fmask, ATCOR and Sen2Cor disagree. Results on difference area have the advantage to show more clearly the strengths and weaknesses of a classification than results on the complete image. The overall accuracy of Fmask, ATCOR, and Sen2Cor for difference areas of the selected scenes is 45%, 56%, and 62%, respectively. User and producer accuracies are strongly class- and scene-dependent, typically varying between 30% and 90%. Comparison of the difference area is complemented by looking for the results in the area where all three classifications give the same result. Overall accuracy for that \u201csame area\u201d is 97% resulting in the complete classification in overall accuracy of 89%, 91% and 92% for Fmask, ATCOR and Sen2Cor respectively.<\/jats:p>","DOI":"10.3390\/rs13010137","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T08:35:19Z","timestamp":1609749319000},"page":"137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Comparison of Masking Algorithms for Sentinel-2 Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8329-8564","authenticated-orcid":false,"given":"Viktoria","family":"Zekoll","sequence":"first","affiliation":[{"name":"DLR, German Aerospace Center, D-82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magdalena","family":"Main-Knorn","sequence":"additional","affiliation":[{"name":"DLR, German Aerospace Center, D-12489 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Alonso","sequence":"additional","affiliation":[{"name":"DLR, German Aerospace Center, D-82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7394-6137","authenticated-orcid":false,"given":"Jerome","family":"Louis","sequence":"additional","affiliation":[{"name":"Telespazio France, 31023 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9292-3931","authenticated-orcid":false,"given":"David","family":"Frantz","sequence":"additional","affiliation":[{"name":"Earth Observation Lab, Geography Department, Humboldt-Universit\u00e4t zu Berlin, D-10099 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8499-4780","authenticated-orcid":false,"given":"Rudolf","family":"Richter","sequence":"additional","affiliation":[{"name":"DLR, German Aerospace Center, D-82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4626-9393","authenticated-orcid":false,"given":"Bringfried","family":"Pflug","sequence":"additional","affiliation":[{"name":"DLR, German Aerospace Center, D-12489 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. 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