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Project Documents


Project Documents

The following documents are provided within this project.

  • Project baseline for PV-CDRR: providing accurate information and implementation from the project including the work logic, the approaches and methods, the work plan and the list of deliverables.
  • Round Robin Protocols: providing the guidelines and rules for participating to the Round Robin excercise and a short description of the adopted QA metrics. Delivery of this document to the algorithm providers is expected for 30 April 2016.
  • Validation and Test Dataset Description: providing detailed information on the validation dataset definition, including statistical distribution of the different pixel classes, geographical distribution of the selected pixels and covered surface type, season and environmental conditions. Delivery of this document to the algorithm providers is expected for 30 June 2016.
  • Final Report: providing results of the inter-comparison exercise and discussion on advantages and drawbacks of each algorithm. Delivery of this document is expected for 30 January 2017. A peer-reviewed paper will be also prepared summarising the outcomes of this study. 

Cloud Detection Algorithm Theoretical Baseline Document (ATBD)

The information provided in this section is applicable to the different Cloud Detection Algorithms developed within the framework of the Proba-V Cloud Detection Round Robin (PV-CDRR). The description of each adopted methodologies is available in the following dedicated documentation:


PV-CDRR Workshop documents

Outreach Publications


Relevant scientific papers, which were used for defining the Round Robin baseline objectives and relevant QA metrics are listed here below.

  • Ackerman, S. A., Holz, R. E., Frey, R., Eloranta, E. W., Maddux, B. C., & McGill, M. (2008). Cloud detection with MODIS. Part II: validation. Journal of Atmospheric and Oceanic Technology, 25(7), 1073-1086.
  • Ackerman, Steve, et al., Discriminating clear-sky from cloud with MODIS algorithm theoretical basis document (MOD35)." MODIS Cloud Mask Team, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin. 2010.
  • Breon, F. M., & Colzy, S. (1999). Cloud detection from the spaceborne POLDER instrument and validation against surface synoptic observations. Journal of Applied Meteorology, 38(6), 777-785.
  • Brockmann C., Paperin M., Danne O., Kirches, G., Bontemps, S., Stelzer, K., Ruescas, Cloud Screening and Pixel Characterisation: IdePix Approach and Validation Using PixBox, Sentinel-3 OLCI/SLSTR and MERIS/(A)ATSR workshop, which will be hosted in ESA-ESRIN, Frascati, Italy, from 15 to 19 October 2012.
  • Christodoulou, C., Michaelides, S. C., & Pattichis, C. S. (2003). Multifeature texture analysis for the classification of clouds in satellite imagery. Geoscience and Remote Sensing, IEEE Transactions on, 41(11), 2662-2668.
  • Hagolle, O., et al. Quality assessment and improvement of temporally composited products of remotely sensed imagery by combination of VEGETATION 1 and 2 images. Remote Sensing of Environment 94.2 (2005): 172-186.
  • Hagolle, O., Huc, M., Pascual, D. V., & Dedieu, G. (2010). A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images. Remote Sensing of Environment, 114(8), 1747-1755.
  • Hollstein, A., Fischer, J., Carbajal Henken, C., & Preusker, R. (2014). Bayesian cloud detection for MERIS, AATSR, and their combination. Atmospheric Measurement Techniques Discussions, 7(11), 11045-11085.
  • Jang, J. D., Viau, A. A., Anctil, F., & Bartholomé, E. (2006). Neural network application for cloud detection in SPOT VEGETATION images. International Journal of Remote Sensing, 27(4), 719-736.
  • Lisens, G., P. Kempeneers, F. Fierens, and J. Van Rensbergen. Development of Cloud, Snow, and Shadow Masking Algorithms for VEGETATION Imagery. Proceedings of Geoscience and Remote Sensing Symposium, IGARSS 2000, Honolulu, HI 2: 834–836.
  • Sedano, F., Kempeneers, P., Strobl, P., Kucera, J., Vogt, P., Seebach, L., & San-Miguel-Ayanz, J. (2011). A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5), 588-596.
  • Wolters, E.L.A., Swinnen, E., I. Benhadj, Dierckx, W., PROBA-V cloud detection evaluation and proposed modification, QWG Technical Note, 17/7/2015
  • Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2009; p. 193.
  • Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement. Vol. 20, No. 1, pp. 37–40.
  • Hayes, A. F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1, 77-89.
  • Scott, W. (1955). "Reliability of content analysis: The case of nominal scale coding." Public Opinion Quarterly, 19(3), 321-325.
  • CMS/Météo-France, 2005, Validation report for PGE01-02-03 of SAF/NWC/MSG. Météo France / Centre de Météorologie Spatiale Report SAF/NWC/IOP/MFL/SCI/VAL/01, version 1.0.
  • Mackie, S., Embury, O., Old, C., Merchant, C. J., & Francis, P. (2010). Generalized Bayesian cloud detection for satellite imagery. Part 1: Technique and validation for night-time imagery over land and sea. International Journal of Remote Sensing, 31(10), 2573-2594.
  • Dierckx, Wouter, et al. "PROBA-V mission for global vegetation monitoring: standard products and image quality." International Journal of Remote Sensing 35.7 (2014):2589-2614.
  • Vancutsem, J. F. Pekel, P. Bogaert & P. Defourny (2007) Mean Compositing, an alternative strategy for producing temporal syntheses. Concepts and performance assessment for SPOT VEGETATION time series, International Journal of Remote Sensing, 28:22, 5123-5141.