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Despite their importance, they are hard to survey due to their location, extent, and particularly, their cloud coverage. Clouds hinder the spatial and radiometric correction of satellite imagery and also diminishing the useful area on each image, making it difficult to monitor land change. For this reason, our purpose is to identify the cloud detection algorithm best suited for the Amazon rainforest on Sentinel\u20132 images. To achieve this, we tested four cloud detection algorithms on Sentinel\u20132 images spread in five areas of the Amazonia. Using more than eight thousand validation points, we compared four cloud detection methods: Fmask 4, MAJA, Sen2Cor, and s2cloudless. Our results point out that FMask 4 has the best overall accuracy on images of the Amazon region (90%), followed by Sen2Cor\u2019s (79%), MAJA (69%), and S2cloudless (52%). We note the choice of method depends on the intended use. Since MAJA reduces the number of false positives by design, users that aim to improve the producer\u2019s accuracy should consider its use.<\/jats:p>","DOI":"10.3390\/rs12081284","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"1284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Comparison of Cloud Cover Detection Algorithms on Sentinel\u20132 Images of the Amazon Tropical Forest"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7966-2880","authenticated-orcid":false,"given":"Alber Hamersson","family":"Sanchez","sequence":"first","affiliation":[{"name":"Earth System Science Center, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9855-2046","authenticated-orcid":false,"given":"Michelle Cristina A.","family":"Picoli","sequence":"additional","affiliation":[{"name":"Image Processing Division, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3681-487X","authenticated-orcid":false,"given":"Gilberto","family":"Camara","sequence":"additional","affiliation":[{"name":"Image Processing Division, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8675-4046","authenticated-orcid":false,"given":"Pedro R.","family":"Andrade","sequence":"additional","affiliation":[{"name":"Earth System Science Center, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1498-6830","authenticated-orcid":false,"given":"Michel Eustaquio D.","family":"Chaves","sequence":"additional","affiliation":[{"name":"Remote Sensing Division, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Lechler","sequence":"additional","affiliation":[{"name":"Institute for Geoinformatics, University of M\u00fcnster, 48149 M\u00fcnster, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6513-2192","authenticated-orcid":false,"given":"Anderson R.","family":"Soares","sequence":"additional","affiliation":[{"name":"Image Processing Division, National Institute for Space Research\u2014INPE, S\u00e3o Jos\u00e9 dos Campos 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0082-9498","authenticated-orcid":false,"given":"Rennan F. 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Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s10584-014-1058-7","article-title":"Amazon Forest Biomass Density Maps: Tackling the Uncertainty in Carbon Emission Estimates","volume":"124","author":"Ometto","year":"2014","journal-title":"Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16732","DOI":"10.1073\/pnas.0910275107","article-title":"Tropical Forests Were the Primary Sources of New Agricultural Land in the 1980s and 1990s","volume":"107","author":"Gibbs","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","unstructured":"INPE (2019). Amazon Deforestation Monitoring Project (PRODES), National Institute for Space Research. Technical Report."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1126\/science.1248525","article-title":"Slowing Amazon Deforestation through Public Policy and Interventions in Beef and Soy Supply Chains","volume":"344","author":"Nepstad","year":"2014","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"074021","DOI":"10.1088\/1748-9326\/aaccbb","article-title":"Future Environmental and Agricultural Impacts of Brazil\u2019s Forest Code","volume":"13","author":"Soterroni","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_7","unstructured":"Shimabukuro, Y.E., Santos, J.R., Formaggio, A.R., Duarte, V., and Rudorff, B.F.T. (2012). The Brazilian Amazon Monitoring Program: PRODES and DETER Projects. Global Forest Monitoring From Earth Observation, Taylor and Francis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1017\/S1355770X15000078","article-title":"Deforestation Slowdown in the Brazilian Amazon: Prices or Policies?","volume":"20","author":"Assuncao","year":"2015","journal-title":"Environ. Dev. Econ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1126\/science.aaa0181","article-title":"Brazil\u2019s Soy Moratorium","volume":"347","author":"Gibbs","year":"2015","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1590\/1809-4392201505504","article-title":"High Spatial Resolution Land Use and Land Cover Mapping of the Brazilian Legal Amazon in 2008 Using Landsat-5\/TM and MODIS Data","volume":"46","author":"Almeida","year":"2016","journal-title":"Acta Amaz."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5493","DOI":"10.3390\/rs5115493","article-title":"Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon","volume":"5","author":"Souza","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e1601047","DOI":"10.1126\/sciadv.1601047","article-title":"Types and Rates of Forest Disturbance in Brazilian Legal Amazon, 2000\u20132013","volume":"3","author":"Tyukavina","year":"2017","journal-title":"Sci. Adv."},{"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":"328","DOI":"10.1016\/j.isprsjprs.2018.08.007","article-title":"Big Earth Observation Time Series Analysis for Monitoring Brazilian Agriculture","volume":"145","author":"Picoli","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","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. Environ."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2012.11.009","article-title":"Classifying Multiyear Agricultural Land Use Data from Mato Grosso Using Time-Series MODIS Vegetation Index Data","volume":"130","author":"Brown","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"1","article-title":"Land Use Intensity Trajectories on Amazonian Pastures Derived from Landsat Time Series","volume":"41","author":"Rufin","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.rse.2017.10.009","article-title":"Mapping Pasture Management in the Brazilian Amazon from Dense Landsat Time Series","volume":"205","author":"Jakimow","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016). SENTINEL-2 Sen2Cor: L2A Processor for Users. Proceedings Living Planet Symposium, ESA."},{"key":"ref_23","unstructured":"Hagolle, O., Huc, M., Auer, S., Richter, R., and Richter, R. (2019, November 29). MAJA Algorithm Theoretical Basis Document. Available online: https:\/\/zenodo.org\/record\/1209633#.XpdnZvnQ-Cg."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.rse.2018.04.046","article-title":"Improvement of the Fmask Algorithm for Sentinel-2 Images: Separating Clouds from Bright Surfaces Based on Parallax Effects","volume":"215","author":"Frantz","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_27","unstructured":"Zupanc, A. (2019, November 29). Improving Cloud Detection with Machine Learning. Available online: https:\/\/medium.com\/sentinel-hub\/improving-cloud-detection-with-machine-learning-c09dc5d7cf13."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2807","DOI":"10.1029\/2000GL012585","article-title":"Cloud Condensation Nuclei in the Amazon Basin: \u201cMarine\u201d Conditions over a Continent?","volume":"28","author":"Roberts","year":"2001","journal-title":"Geophys. Res. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1126\/science.1191056","article-title":"Rainforest Aerosols as Biogenic Nuclei of Clouds and Precipitation in the Amazon","volume":"329","author":"Poschl","year":"2010","journal-title":"Science"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Artaxo, P., Rizzo, L.V., Paix\u00e3o, M., De Lucca, S., Oliveira, P.H., Lara, L.L., Wiedemann, K.T., Andreae, M.O., Holben, B., and Schafer, J. (2009). Aerosol Particles in Amazonia: Their Composition, Role in the Radiation Balance, Cloud Formation, and Nutrient Cycles. Amazonia and Global Change, American Geophysical Union (AGU).","DOI":"10.1029\/2008GM000778"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1080\/01431160010006926","article-title":"Cloud Cover in Landsat Observations of the Brazilian Amazon","volume":"22","author":"Asner","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10037","DOI":"10.5194\/acp-17-10037-2017","article-title":"Sensitivities of Amazonian Clouds to Aerosols and Updraft Speed","volume":"17","author":"Cecchini","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/S0034-4257(03)00095-6","article-title":"The Impact of Deforestation on Cloud Cover over the Amazon Arc of Deforestation","volume":"86","author":"Durieux","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3670","DOI":"10.1073\/pnas.0810156106","article-title":"Impact of Deforestation in the Amazon Basin on Cloud Climatology","volume":"106","author":"Wang","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1080\/01431161.2019.1667548","article-title":"Satellite Data Cloud Detection Using Deep Learning Supported by Hyperspectral Data","volume":"41","author":"Sun","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.05.024","article-title":"An Automatic Method for Screening Clouds and Cloud Shadows in Optical Satellite Image Time Series in Cloudy Regions","volume":"214","author":"Zhu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/nature10717","article-title":"The Amazon Basin in Transition","volume":"481","author":"Davidson","year":"2012","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wolanin, A., Camps-Valls, G., Gomez-Chova, L., Mateo-Garcia, G., Tol, C., Zhang, Y., and Guanter, L. (2019). Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 Using Machine Learning Methods Trained with Radiative Transfer Simulations. Remote. Sens. Environ.","DOI":"10.1016\/j.rse.2019.03.002"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Thepaut, 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.","DOI":"10.3390\/rs9060584"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud Detection Algorithm Comparison and Validation for Operational Landsat Data Products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.rse.2018.11.007","article-title":"Near Real-Time Agriculture Monitoring at National Scale at Parcel Resolution: Performance Assessment of the Sen2-Agri Automated System in Various Cropping Systems around the World","volume":"221","author":"Defourny","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_42","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_43","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 Landsat 4-7, 8, and Sentinel 2 Images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_44","unstructured":"Mueller-Wilm, U. (2019). Sen2Cor 2.8 Software Release Note, ESA (European Space Agency) Report. Technical Report."},{"key":"ref_45","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017). Lightgbm: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2668","DOI":"10.3390\/rs70302668","article-title":"A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VEN\u03bcS and Sentinel-2 Images","volume":"7","author":"Hagolle","year":"2015","journal-title":"Remote Sens."},{"key":"ref_47","unstructured":"Qiu, S., Zhu, Z., and He, B. (2019, December 01). Fmask 4.0 Handbook. Available online: https:\/\/drive.google.com\/drive\/folders\/1oVefP9G-TD2vhoCaaKCxQjvAnUlrwB19."},{"key":"ref_48","unstructured":"Israel, G.D. (1992). Determining Sample Size, University of Florida. Technical Report."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chinchor, N. (1992). MUC-4 Evaluation Metrics. Fourth Message Uunderstanding Conference (MUC-4), Association for Computational Linguistics.","DOI":"10.3115\/1072064.1072067"},{"key":"ref_50","first-page":"397","article-title":"Accuracy Assessment: A User\u2019s Perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111446","DOI":"10.1016\/j.rse.2019.111446","article-title":"Cloud Detection Algorithm for Multi-Modal Satellite Imagery Using Convolutional Neural-Networks (CNN)","volume":"237","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1284\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:31:29Z","timestamp":1760362289000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,18]]},"references-count":52,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12081284"],"URL":"https:\/\/doi.org\/10.3390\/rs12081284","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,18]]}}}