Reconstruction of Cloud-free Sentinel-2 Image Time-series Using an Extended Spatiotemporal Image Fusion Approach

被引:5
作者
Zhou, Fuqun [1 ]
Zhong, Detang [1 ]
Peiman, Rihana [1 ]
机构
[1] Nat Resources Canada, Canada Ctr Mapping & Earth Observat, Canada Ctr Remote Sensing, 6th Floor,560 Rochester St, Ottawa, ON K1S 5K2, Canada
关键词
Sentinel-2; MODIS; image fusion; clouds and cloud shadow; image time-series; SURFACE REFLECTANCE; LANDSAT;
D O I
10.3390/rs12162595
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Time-series for medium spatial resolution satellite imagery are a valuable resource for environmental assessment and monitoring at regional and local scales. Sentinel-2 satellites from the European Space Agency (ESA) feature a multispectral instrument (MSI) with 13 spectral bands and spatial resolutions from 10 m to 60 m, offering a revisit range from 5 days at the equator to a daily approach of the poles. Since their launch, the Sentinel-2 MSI image time-series from satellites have been used widely in various environmental studies. However, the values of Sentinel-2 image time-series have not been fully realized and their usage is impeded by cloud contamination on images, especially in cloudy regions. To increase cloud-free image availability and usage of the time-series, this study attempted to reconstruct a Sentinel-2 cloud-free image time-series using an extended spatiotemporal image fusion approach. First, a spatiotemporal image fusion model was applied to predict synthetic Sentinel-2 images when clear-sky images were not available. Second, the cloudy and cloud shadow pixels of the cloud contaminated images were identified based on analysis of the differences of the synthetic and observation image pairs. Third, the cloudy and cloud shadow pixels were replaced by the corresponding pixels of its synthetic image. Lastly, the pixels from the synthetic image were radiometrically calibrated to the observation image via a normalization process. With these processes, we can reconstruct a full length cloud-free Sentinel-2 MSI image time-series to maximize the values of observation information by keeping observed cloud-free pixels and calibrating the synthetized images by using the observed cloud-free pixels as references for better quality.
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页数:22
相关论文
共 30 条
[1]   A simple and effective method for filling gaps in Landsat ETM plus SLC-off images [J].
Chen, Jin ;
Zhu, Xiaolin ;
Vogelmann, James E. ;
Gao, Feng ;
Jin, Suming .
REMOTE SENSING OF ENVIRONMENT, 2011, 115 (04) :1053-1064
[2]   Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model [J].
Cheng, Qing ;
Shen, Huanfeng ;
Zhang, Liangpei ;
Yuan, Qiangqiang ;
Zeng, Chao .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2014, 92 :54-68
[3]   Evaluation of Vegetation Biophysical Variables Time Series Derived from Synthetic Sentinel-2 Images [J].
Djamai, Najib ;
Zhong, Detang ;
Fernandes, Richard ;
Zhou, Fuqun .
REMOTE SENSING, 2019, 11 (13)
[4]   Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services [J].
Drusch, M. ;
Del Bello, U. ;
Carlier, S. ;
Colin, O. ;
Fernandez, V. ;
Gascon, F. ;
Hoersch, B. ;
Isola, C. ;
Laberinti, P. ;
Martimort, P. ;
Meygret, A. ;
Spoto, F. ;
Sy, O. ;
Marchese, F. ;
Bargellini, P. .
REMOTE SENSING OF ENVIRONMENT, 2012, 120 :25-36
[5]   Prediction of plant diversity in grasslands using Sentinel-1 and-2 satellite image time series [J].
Fauvel, Mathieu ;
Lopes, Mailys ;
Dubo, Titouan ;
Rivers-Moore, Justine ;
Frison, Pierre-Louis ;
Gross, Nicolas ;
Ouin, Annie .
REMOTE SENSING OF ENVIRONMENT, 2020, 237
[6]   On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance [J].
Gao, Feng ;
Masek, Jeff ;
Schwaller, Matt ;
Hall, Forrest .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (08) :2207-2218
[7]  
Granska E., 2019, REMOTE SENS, V11, P1197, DOI [10.3390/rs11101197, DOI 10.3390/RS11101197]
[8]   A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS [J].
Hilker, Thomas ;
Wulder, Michael A. ;
Coops, Nicholas C. ;
Linke, Julia ;
McDermid, Greg ;
Masek, Jeffrey G. ;
Gao, Feng ;
White, Joanne C. .
REMOTE SENSING OF ENVIRONMENT, 2009, 113 (08) :1613-1627
[9]   Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes [J].
Huang, Bo ;
Zhang, Hankui .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2014, 35 (16) :6213-6233
[10]   High resolution wheat yield mapping using Sentinel-2 [J].
Hunt, Merryn L. ;
Blackburn, George Alan ;
Carrasco, Luis ;
Redhead, John W. ;
Rowland, Clare S. .
REMOTE SENSING OF ENVIRONMENT, 2019, 233