A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images

Over lands, the cloud detection on remote sensing images is not an easy task, because of the frequent difficulty to distinguish clouds from the underlying landscape, even at a high resolution. Up to now, most high resolution images have been distributed without an associated cloud mask. This situation should change in the near future, thanks to two new satellite missions that will provide optical images combining 3 features: high spatial resolution, high revisit frequency and constant viewing angles. The VENµS (French and Israeli cooperation) mission should be launched in 2012 and the European SENTINEL-2 mission in 2013. Fortunately, two existing satellite missions, FORMOSAT-2 and LANDSAT, enable to simulate the future data of these sensors. Multi-temporal imagery at constant viewing angles provides a new way to discriminate clouded and unclouded pixels, using the relative stability of the earth surface reflectances compared to the quick variations of the reflectance of pixels affected by clouds. In this study, we have used time series of images from FORMOSAT-2 and LANDSAT to develop and test a Multi-Temporal Cloud Detection (MTCD) method. This algorithm combines a detection of a sudden increase of reflectance in the blue wavelength on a pixel by pixel basis, and a test of the linear correlation of pixel neighborhoods taken from couples of images acquired successively. MTCD cloud masks are compared with cloud cover assessments obtained from FORMOSAT-2 and LANDSAT data catalogs. The results show that the MTCD method provides a better discrimination of clouded and unclouded pixels than the usual methods based on thresholds applied to reflectances or reflectance ratios. This method will be used within VENµS level 2 processing and will be proposed for SENTINEL-2 level 2 processing.

[1]  R. Saunders,et al.  An improved method for detecting clear sky and cloudy radiances from AVHRR data , 1988 .

[2]  Sylvie Le Hégarat-Mascle,et al.  Use of Markov Random Fields for automatic cloud/shadow detection on high resolution optical images , 2009 .

[3]  E. Ben-Dor.  A precaution regarding cirrus cloud detection from airborne imaging spectrometer data using the 1.38 μm water vapor band , 1994 .

[4]  F. Maignan,et al.  Bidirectional reflectance of Earth targets: evaluation of analytical models using a large set of spaceborne measurements with emphasis on the Hot Spot , 2004 .

[5]  Michael Berger,et al.  Sentinel-2 optical high resolution mission for GMES operational services , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[6]  Christophe Latry,et al.  Cloud detection with SVM technique , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[7]  S. Sader,et al.  Detection of forest harvest type using multiple dates of Landsat TM imagery , 2002 .

[8]  Bin Wang,et al.  Automated Detection and Removal of Clouds and Their Shadows from Landsat TM Images , 1999 .

[9]  Olivier Hagolle,et al.  Automatic Registration of Optical Images, a Stake for Future Missions: Application to Ortho-Rectification, Time Series and Mosaic Products , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[10]  Stéphane Colzy,et al.  Cloud Detection from the Spaceborne POLDER Instrument and Validation against Surface Synoptic Observations , 1999 .

[11]  Stephen E. Reichenbach,et al.  Automated cloud cover assessment for Landsat TM images , 1996, Optics & Photonics.

[12]  W. Menzel,et al.  Discriminating clear sky from clouds with MODIS , 1998 .

[13]  Richard R. Irish,et al.  Landsat 7 automatic cloud cover assessment , 2000, SPIE Defense + Commercial Sensing.

[14]  J. Roujean,et al.  A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .

[15]  Yujie Wang,et al.  An automatic cloud mask algorithm based on time series of MODIS measurements , 2008 .

[16]  Xavier Briottet,et al.  Inter-calibration using desertic sites as a reference target , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[17]  S. Goward,et al.  Characterization of the Landsat-7 ETM Automated Cloud-Cover Assessment (ACCA) Algorithm , 2006 .

[18]  Gérard Dedieu,et al.  Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to Formosat-2 images , 2008 .

[19]  J. Mustard,et al.  Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .

[20]  Philippe Marguinaud,et al.  OPERATIONAL CLOUD MASKING FOR THE OSI SAF GLOBAL METOP/AVHRR SST PRODUCT , 2007 .

[21]  J. Dozier.  Spectral Signature of Alpine Snow Cover from the Landsat Thematic Mapper , 1989 .