{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T21:08:17Z","timestamp":1784840897928,"version":"3.55.0"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T00:00:00Z","timestamp":1530835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["TEC2016-77741-R"],"award-info":[{"award-number":["TEC2016-77741-R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["TIN2015-64210-R"],"award-info":[{"award-number":["TIN2015-64210-R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006785","name":"Google","doi-asserted-by":"publisher","award":["Google Earth Engine Award: Cloud detection in the Cloud"],"award-info":[{"award-number":["Google Earth Engine Award: Cloud detection in the Cloud"]}],"id":[{"id":"10.13039\/100006785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["Consolidator Grant SEDAL (project id 647423)"],"award-info":[{"award-number":["Consolidator Grant SEDAL (project id 647423)"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The exploitation of Earth observation satellite images acquired by optical instruments requires an automatic and accurate cloud detection. Multitemporal approaches to cloud detection are usually more powerful than their single scene counterparts since the presence of clouds varies greatly from one acquisition to another whereas surface can be assumed stationary in a broad sense. However, two practical limitations usually hamper their operational use: the access to the complete satellite image archive and the required computational power. This work presents a cloud detection and removal methodology implemented in the Google Earth Engine (GEE) cloud computing platform in order to meet these requirements. The proposed methodology is tested for the Landsat-8 mission over a large collection of manually labeled cloud masks from the Biome dataset. The quantitative results show state-of-the-art performance compared with mono-temporal standard approaches, such as FMask and ACCA algorithms, yielding improvements between 4\u20135% in classification accuracy and 3\u201310% in commission errors. The algorithm implementation within the Google Earth Engine and the generated cloud masks for all test images are released for interested readers.<\/jats:p>","DOI":"10.3390\/rs10071079","type":"journal-article","created":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T10:55:44Z","timestamp":1530874544000},"page":"1079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":132,"title":["Multitemporal Cloud Masking in the Google Earth Engine"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0569-393X","authenticated-orcid":false,"given":"Gonzalo","family":"Mateo-Garc\u00eda","sequence":"first","affiliation":[{"name":"Image Processing Laboratory, University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3924-1269","authenticated-orcid":false,"given":"Luis","family":"G\u00f3mez-Chova","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0876-8340","authenticated-orcid":false,"given":"Julia","family":"Amor\u00f3s-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3014-3921","authenticated-orcid":false,"given":"Jordi","family":"Mu\u00f1oz-Mar\u00ed","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1683-2138","authenticated-orcid":false,"given":"Gustau","family":"Camps-Valls","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, University of Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4105","DOI":"10.1109\/TGRS.2007.905312","article-title":"Cloud-Screening Algorithm for ENVISAT\/MERIS Multispectral Images","volume":"45","author":"Calpe","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere and land","volume":"120","author":"Malenovsky","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.14358\/PERS.72.10.1179","article-title":"Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm","volume":"72","author":"Irish","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.rse.2016.11.016","article-title":"A Cloud masking algorithm for the XBAER aerosol retrieval using MERIS data","volume":"197","author":"Mei","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.3390\/rs6064907","article-title":"Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing","volume":"6","author":"Hughes","year":"2014","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hollstein, A., Segl, K., Guanter, L., Brell, M., and Enesco, M. (2016). Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images. Remote Sens., 8.","DOI":"10.3390\/rs8080666"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Iannone, R.Q., Niro, F., Goryl, P., Dransfeld, S., Hoersch, B., Stelzer, K., Kirches, G., Paperin, M., Brockmann, C., and G\u00f3mez-Chova, L. (2017, January 27\u201329). Proba-V cloud detection Round Robin: Validation results and recommendations. Proceedings of the 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Brugge, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035219"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.rse.2011.09.022","article-title":"Landsat: Building a strong future","volume":"122","author":"Loveland","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_12","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_13","first-page":"453","article-title":"Automated Detection and Removal of Clouds and Their Shadows from Landsat TM Images","volume":"82","author":"Wang","year":"1999","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1016\/j.rse.2010.03.002","article-title":"A multi-temporal method for cloud detection, applied to FORMOSAT-2, VEN\u03bcS, LANDSAT and SENTINEL-2 images","volume":"114","author":"Hagolle","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1080\/01431161.2012.720045","article-title":"Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA","volume":"34","author":"Jin","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1109\/LGRS.2015.2390673","article-title":"Enhancing the Detectability of Clouds and Their Shadows in Multitemporal Dryland Landsat Imagery: Extending Fmask","volume":"12","author":"Frantz","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"015005","DOI":"10.1117\/1.JRS.11.015005","article-title":"Cloud masking and removal in remote sensing image time series","volume":"11","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Candra, D.S., Phinn, S., and Scarth, P. (2017, January 7\u201310). Cloud and cloud shadow removal of landsat 8 images using Multitemporal Cloud Removal method. Proceedings of the 2017 6th International Conference on Agro-Geoinformatics, Fairfax, VA, USA.","DOI":"10.1109\/Agro-Geoinformatics.2017.8047007"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2015.2443494","article-title":"The Time Variable in Data Fusion: A Change Detection Perspective","volume":"3","author":"Bovolo","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TGRS.2005.861929","article-title":"Contextual reconstruction of cloud-contaminated multitemporal multispectral images","volume":"44","author":"Melgani","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TGRS.2012.2197682","article-title":"Cloud Removal From Multitemporal Satellite Images Using Information Cloning","volume":"51","author":"Lin","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"095053","DOI":"10.1117\/1.JRS.9.095053","article-title":"Thin cloud removal from remote sensing images using multidirectional dual tree complex wavelet transform and transfer least square support vector regression","volume":"9","author":"Hu","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TGRS.2016.2580576","article-title":"Spatially and Temporally Weighted Regression: A Novel Method to Produce Continuous Cloud-Free Landsat Imagery","volume":"55","author":"Chen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_27","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_28","unstructured":"Dean, J., and Ghemawat, S. (2004, January 6\u20138). MapReduce: Simplified Data Processing on Large Clusters. Proceedings of the Sixth Symposium on Operating System Design and Implementation (OSDI\u201904), San Francisco, CA, USA."},{"key":"ref_29","unstructured":"U.S. Geological Survey (2016). L7 Irish Cloud Validation Masks, Data Release."},{"key":"ref_30","unstructured":"U.S. Geological Survey (2016). L8 SPARCS Cloud Validation Masks, Data Release."},{"key":"ref_31","unstructured":"U.S. Geological Survey (2016). L8 Biome Cloud Validation Masks, Data Release."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Meng, F., Yang, X., Zhou, C., and Li, Z. (2017). A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery. Sensors, 17.","DOI":"10.3390\/s17092130"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/LGRS.2010.2095409","article-title":"Implementation on Landsat Data of a Simple Cloud-Mask Algorithm Developed for MODIS Land Bands","volume":"8","author":"Oreopoulos","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and van der Maaten, L. (arXiv, 2018). Exploring the Limits of Weakly Supervised Pretraining, arXiv.","DOI":"10.1007\/978-3-030-01216-8_12"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1079\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:11:36Z","timestamp":1760195496000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,6]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["rs10071079"],"URL":"https:\/\/doi.org\/10.3390\/rs10071079","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,6]]}}}