{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:51:08Z","timestamp":1780102268490,"version":"3.54.0"},"reference-count":58,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,5,25]],"date-time":"2017-05-25T00:00:00Z","timestamp":1495670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Oceanic and Atmospheric Administration (NOAA)","award":["NA10NOS4780146"],"award-info":[{"award-number":["NA10NOS4780146"]}]},{"DOI":"10.13039\/100000180","name":"Department of Homeland Security","doi-asserted-by":"publisher","award":["2015-ST-061-ND0001-01"],"award-info":[{"award-number":["2015-ST-061-ND0001-01"]}],"id":[{"id":"10.13039\/100000180","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing derived Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Studies that are heavily dependent on optical sensors are subject to data loss due to cloud coverage. Specifically, cloud contamination is a hindrance to long-term environmental assessment when using information from satellite imagery retrieved from visible and infrared spectral ranges. Landsat has an ongoing high-resolution NDVI record starting from 1984. Unfortunately, this long time series NDVI data suffers from the cloud contamination issue. Though both simple and complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques have limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum using a Random Forest (RF) trained and tested with multi-parameter hydrologic data. The RF-based OCPR model is compared with a linear regression model to demonstrate the capability of OCPR. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance. The RF-based OCPR method achieves a root mean squared error of 0.016 between predicted and observed NDVI reflectance values. The linear regression model achieves a root mean squared error of 0.126. Our findings suggest that the RF-based OCPR method is effective to repair cloudy pixels and provides continuous and quantitatively reliable imagery for long-term environmental analysis.<\/jats:p>","DOI":"10.3390\/rs9060527","type":"journal-article","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T04:35:42Z","timestamp":1496118942000},"page":"527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Optical Cloud Pixel Recovery via Machine Learning"],"prefix":"10.3390","volume":"9","author":[{"given":"Subrina","family":"Tahsin","sequence":"first","affiliation":[{"name":"Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0264-5868","authenticated-orcid":false,"given":"Stephen","family":"Medeiros","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milad","family":"Hooshyar","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2172-6321","authenticated-orcid":false,"given":"Arvind","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1080\/01431168508948281","article-title":"Analysis of the phenology of global vegetation using meteorological satellite data","volume":"6","author":"Justice","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/386698a0","article-title":"Increased plant growth in the northern high latitudes from 1981 to 1991","volume":"386","author":"Myneni","year":"1997","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1080\/01431168508948283","article-title":"Canopy reflectance, photosynthesis and transpiration","volume":"6","author":"Sellers","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","first-page":"529","article-title":"Normalized Difference Vegetation Index (NDVI as the basis for local forest management. Example of the municipality of Topola, Serbia","volume":"24","year":"2015","journal-title":"Pol. J. Environ. Stud."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.geomorph.2015.10.003","article-title":"NDVI patterns as indicator of morphodynamic activity in the middle Parana River floodplain","volume":"253","author":"Marchetti","year":"2016","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0034-4257(96)00137-X","article-title":"Multitemporal, multichannel AVHRR data sets for land biosphere studies\u2014Artifacts and corrections","volume":"60","author":"Cihlar","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/0034-4257(91)90005-Q","article-title":"Vegetation indices from AVHRR: An update and future prospects","volume":"35","author":"Gutman","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.3390\/rs5062973","article-title":"Removal of optically thick clouds from multi-spectral satellite images using multi-frequency SAR data","volume":"5","author":"Eckardt","year":"2013","journal-title":"Remote Sens."},{"key":"ref_10","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_11","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_12","doi-asserted-by":"crossref","first-page":"3998","DOI":"10.1109\/TGRS.2012.2227329","article-title":"Missing-area reconstruction in multispectral images under a compressive sensing perspective","volume":"51","author":"Lorenzi","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","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_14","first-page":"482","article-title":"Land Change Science: Observing, Monitoring, and Understanding Trajectories of Change on the Earth\u2019s Surface","volume":"6","author":"Gutman","year":"2004","journal-title":"Remote Sens. Digit. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.artmed.2010.05.002","article-title":"Missing data imputation using statistical and machine learning methods in a real breast cancer problem","volume":"50","author":"Jerez","year":"2010","journal-title":"Artif. Intell. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1515\/ijb-2013-0038","article-title":"Estimation of a Predictor\u2019s Importance by Random Forests When There Is Missing Data: RISK Prediction in Liver Surgery using Laboratory Data","volume":"10","author":"Hapfelmeier","year":"2014","journal-title":"Int. J. Biostat."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.neucom.2015.03.108","article-title":"Extreme learning machine for missing data using multiple imputations","volume":"174","author":"Sovilj","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1061\/JYCEAJ.0005466","article-title":"Reciprocal-Distance Estimate of Point Rainfall","volume":"106","author":"Simanton","year":"1980","journal-title":"J. Hydraul. Div."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0022-1694(96)03285-4","article-title":"Patching rainfall data using regression methods. 1 Best subset selection, EM and pseudo-EM methods: Theory","volume":"198","author":"Makhuvha","year":"1997","journal-title":"J. Hydrol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1080\/01431169208904212","article-title":"The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series","volume":"13","author":"Viovy","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/0034-4257(95)00190-5","article-title":"Identification of contaminated pixels in AVHRR composite images for studies of land biosphere","volume":"56","author":"Cihlar","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1080\/014311600209814","article-title":"Reconstructing cloudfree NDVI composites using Fourier analysis of time series","volume":"21","author":"Roerink","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function-fitting to time-series of satellite sensor data","volume":"40","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"Swets, D.L., Reed, B.C., Rowland, J.D., and Marko, S.E. (1999, January 17\u201321). A weighted least-squares approach to temporal smoothing of NDVI. Proceedings of the ASPRS Annual Conference, from Image to Information, Portland, OR, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/36.739119","article-title":"A cloud-removal algorithm for SSM\/I data","volume":"37","author":"Long","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/TGRS.2012.2237408","article-title":"Patch-based information reconstruction of cloud-contaminated multitemporal images","volume":"52","author":"Lin","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"Lynch, S.D. (2003). Development of a RASTER Database of Annual, Monthly and Daily Rainfall for Southern Africa, Water Research Commission. WRC Report No. 1156\/1\/04."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MGRS.2015.2441912","article-title":"Missing Information Reconstruction of Remote Sensing Data: A Technical Review","volume":"3","author":"Shen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7086","DOI":"10.1109\/TGRS.2014.2307354","article-title":"Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning","volume":"52","author":"Li","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.03.009","article-title":"Sparse-based reconstruction of missing information in remote sensing images from spectral\/temporal complementary information","volume":"106","author":"Li","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"171","DOI":"10.4314\/wsa.v31i2.5199","article-title":"Infilling streamflow data using feed-forward back-propagation (BP) artificial neural networks: Application of standard BP and pseudo Mac Laurin power series BP techniques","volume":"31","author":"Ilunga","year":"2005","journal-title":"Water SA"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.rse.2014.10.003","article-title":"Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013","volume":"156","author":"Han","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2014.04.010","article-title":"Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data","volume":"149","author":"Comber","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1080\/13658816.2014.885527","article-title":"Predictive modelling of gold potential with the integration of multisource information based on random forest: A case study on the Rodalquilar area, Southern Spain","volume":"28","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.jaridenv.2015.08.015","article-title":"Influence of rainfall variability on the vegetation dynamics over Northeastern Brazil","volume":"124","author":"Barbosa","year":"2016","journal-title":"J. Arid Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.jaridenv.2014.09.010","article-title":"Riparian vegetation NDVI dynamics and its relationship with climate, surface water and groundwater","volume":"113","author":"Fu","year":"2015","journal-title":"J. Arid Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3827","DOI":"10.1080\/01431160010007033","article-title":"Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains","volume":"22","author":"Wang","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s10666-011-9297-8","article-title":"Vegetation NDVI linked to temperature and precipitation in the upper catchments of Yellow River","volume":"17","author":"Hao","year":"2012","journal-title":"Environ. Model. Assess."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1109\/TGRS.2011.2166965","article-title":"A changing-weight filter method for reconstructing a high-quality NDVI time series to preserve the integrity of vegetation phenology","volume":"50","author":"Zhu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1016\/S0895-4356(96)00002-9","article-title":"Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes","volume":"49","author":"Tu","year":"1996","journal-title":"J. Clin. Epidemiol."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.ecss.2014.11.008","article-title":"Hydrodynamic modeling and analysis of sea-level rise impacts on salinity for oyster growth in Apalachicola Bay, Florida","volume":"156","author":"Huang","year":"2015","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_47","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Taylor & Francis."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1021\/ci060164k","article-title":"Random forest models to predict aqueous solubility","volume":"47","author":"Palmer","year":"2007","journal-title":"J. Chem. Inf. Model."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Svetnik, V., Liaw, A., Tong, C., and Wang, T. (2004, January 9\u201311). Application of Breiman\u2019s random forest to modeling structure-activity relationships of pharmaceutical molecules. Proceedings of the International Workshop on Multiple Classifier Systems, Cagliari, Italy.","DOI":"10.1007\/978-3-540-25966-4_33"},{"key":"ref_50","first-page":"2015","article-title":"Consistency of random forests and other averaging classifiers","volume":"9","author":"Biau","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_51","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_52","first-page":"154","article-title":"How many trees in a random forest?","volume":"Volume 7376","author":"Oshiro","year":"2012","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.14358\/PERS.72.10.1155","article-title":"Historical record of landsat global coverage: Mission operations, NSLRSDA, and international cooperator stations","volume":"72","author":"Goward","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"7529","DOI":"10.1002\/2016GL069594","article-title":"Resilience of coastal wetlands to extreme hydrologic events in Apalachicola Bay","volume":"43","author":"Tahsin","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_55","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_56","unstructured":"Warmerdam, F. (2016, March 26). GDAL\u2014Geospatial Data Abstraction Library. Available online: http:\/\/gdal.org\/1.11\/."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1089\/neu.2014.3399","article-title":"Development of a Database for Translational Spinal Cord Injury Research","volume":"31","author":"Nielson","year":"2014","journal-title":"J. Neurotrauma"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/LRA.2016.2519949","article-title":"Rigidity-Based Surface Recognition for a Domestic Legged Robot","volume":"1","author":"Kertesz","year":"2016","journal-title":"IEEE Robot. Autom. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/527\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:37:00Z","timestamp":1760207820000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,25]]},"references-count":58,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["rs9060527"],"URL":"https:\/\/doi.org\/10.3390\/rs9060527","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,5,25]]}}}