A Parallax Shift Effect Correction Based on Cloud Height for Geostationary Satellites and Radar Observations - Publication - MOST Wiedzy


A Parallax Shift Effect Correction Based on Cloud Height for Geostationary Satellites and Radar Observations


The effect of cloud parallax shift occurs in satellite imaging, particularly for high angles of satellite observations. This study demonstrates new methods of parallax effect correction for clouds observed by geostationary satellites. The analytical method that could be found in literature, namely the Vicente et al./Koenig method, is presented at the beginning. It approximates a cloud position using an ellipsoid with semi-axes increased by the cloud height. The error values of this method reach up to 50 meters. The second method, which is proposed by the author, is an augmented version of the Vicente et al./Koenig approach. With this augmentation, the error can be reduced to centimeters. The third method, also proposed by the author, incorporates geodetic coordinates. It is described as a set of equations that are solved with the numerical method, and its error can be driven to near zero by adjusting the count of iterations. A sample numerical solution procedure with application of the Newton method is presented. Also, a simulation experiment that evaluates the proposed methods is described in the paper. The results of an experiment are described and contrasted with current technology. Currently, operating geostationary Earth Observation (EO) satellite resolutions vary from 0.5 km up to 8 km. The pixel sizes of these satellites are much greater than for maximal error of the least precise method presented in this paper. Therefore, the chosen method will be important when the resolution of geostationary EO satellites reaches 50 m. To validate the parallax correction, procedure data from on-ground radars and the Meteosat Second Generation (MSG) satellite, which describes stormy events, was compared before and after correction. Comparison was performed by correlating the logarithm of the cloud optical thickness (COT) with radar reflectance in dBZ (radar reflectance – Z in logarithmic form).


  • 2


  • 2

    Web of Science

  • 2



artykuły w czasopismach
Published in:
Remote Sensing no. 12, pages 1 - 20,
ISSN: 2072-4292
Publication year:
Bibliographic description:
Bieliński T.: A Parallax Shift Effect Correction Based on Cloud Height for Geostationary Satellites and Radar Observations// Remote Sensing -Vol. 12,iss. 3 (2020), s.1-20
Digital Object Identifier (open in new tab) 10.3390/rs12030365
Bibliography: test
  1. Kaminski, L.; Kulawiak, M.; Cizmowski, W.; Chybicki, A.; Stepnowski, A.; Orlowski, A. Web-based GIS dedicated for marine environment surveillance and monitoring. In Proceedings of the OCEANS 2009- EUROPE; 2009; pp. 1-7. open in new tab
  2. Manzione, R.L.; Castrignano, A. A geostatistical approach for multi-source data fusion to predict water table depth. Sci. Total Environ. 2019, 696, UNSP 133763. open in new tab
  3. Mishra, M.; Dugesar, V.; Prudhviraju, K.N.; Patel, S.B.; Mohan, K. Precision mapping of boundaries of flood plain river basins using high-resolution satellite imagery: A case study of the Varuna river basin in Uttar Pradesh, India. J. Earth Syst. Sci. 2019, 128, 105. open in new tab
  4. Berezowski, T.; Wassen, M.; Szatylowicz, J.; Chormanski, J.; Ignar, S.; Batelaan, O.; Okruszko, T. Wetlands in flux: looking for the drivers in a central European case. Wetl. Ecol. Manag. 2018, 26, 849-863. open in new tab
  5. Stateczny, A.; Bodus-Olkowska, I. Sensor data fusion techniques for environment modelling. In Proceedings of the 2015 16th International Radar Symposium (IRS); 2015; pp. 1123-1128. open in new tab
  6. Kazimierski, W.; Stateczny, A. Fusion of data from AIS and tracking radar for the needs of ECDIS. In Proceedings of the 2013 Signal Processing Symposium (SPS); 2013; pp. 1-6. open in new tab
  7. Roebeling, R.A.; Holleman, I. SEVIRI rainfall retrieval and validation using weather radar observations. Journal of Geophysical Research: Atmospheres 2009, 114. open in new tab
  8. Vicente, G.A.; Scofield, R.A.; Menzel, W.P. The Operational GOES Infrared Rainfall Estimation Technique. Bull. Amer. Meteor. Soc. 1998, 79, 1883-1898. open in new tab
  9. Zhao, J.; Chen, X.; Zhang, J.; Zhao, H.; Song, Y. Higher temporal evapotranspiration estimation with improved SEBS model from geostationary meteorological satellite data. Sci Rep 2019, 9, 14981. open in new tab
  10. Henken, C.C.; Schmeits, M.J.; Deneke, H.; Roebeling, R.A. Using MSG-SEVIRI Cloud Physical Properties and Weather Radar Observations for the Detection of Cb/TCu Clouds. J. Appl. Meteor. Climatol. 2011, 50, 1587-1600. open in new tab
  11. Miller, S.D.; Rogers, M.A.; Haynes, J.M.; Sengupta, M.; Heidinger, A.K. Short-term solar irradiance forecasting via satellite/model coupling. Solar Energy 2018, 168, 102-117. open in new tab
  12. Li, S.; Sun, D.; Yu, Y. Automatic cloud-shadow removal from flood/standing water maps using MSG/SEVIRI imagery. International Journal of Remote Sensing 2013, 34, 5487-5502. open in new tab
  13. Wang, C.; Luo, Z.J.; Huang, X. Parallax correction in collocating CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations: Method and application to convection study. Journal of Geophysical Research: Atmospheres 2011, 116. open in new tab
  14. Guo, Q.; Feng, X.; Yang, C.; Chen, B. Improved Spatial Collocation and Parallax Correction Approaches for Calibration Accuracy Validation of Thermal Emissive Band on Geostationary Platform. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 2647-2663. open in new tab
  15. Chen, J.; Yang, J.-G.; An, W.; Chen, Z.-J. An Attitude Jitter Correction Method for Multispectral Parallax Imagery Based on Compressive Sensing. IEEE Geosci. Remote Sens. Lett. 2017, 14, 1903-1907. open in new tab
  16. Frantz, D.; Haß, E.; Uhl, A.; Stoffels, J.; Hill, J. Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment 2018, 215, 471-481. open in new tab
  17. Roebeling, R.A.; Feijt, A.J. Validation of cloud liquid water path retrievals from SEVIRI on METEOSAT-8 using CLOUDNET observations. In Proceedings of the EUMETSAT Meteorological Satellite Conference; open in new tab
  18. Citeseer: Helsinki, Finland, 2006; pp. 12-16.
  19. Roebeling, R.A.; Deneke, H.M.; Feijt, A.J. Validation of Cloud Liquid Water Path Retrievals from SEVIRI Using One Year of CloudNET Observations. J. Appl. Meteor. Climatol. 2008, 47, 206-222. open in new tab
  20. Greuell, W.; Roebeling, R.A. Toward a Standard Procedure for Validation of Satellite-Derived Cloud Liquid Water Path: A Study with SEVIRI Data. J. Appl. Meteor. Climatol. 2009, 48, 1575-1590. open in new tab
  21. Schutgens, N. a. J.; Greuell, W.; Roebeling, R. Effect of inhomogeneity on the validation of SEVIRI LWP. In Current Problems in Atmospheric Radiation (irs 2008); open in new tab
  22. Nakajima, T., Yamasoe, M.A., Eds.; Amer Inst Physics: Melville, 2009; Vol. 1100, pp. 424-+ ISBN 978-0-7354-0635-3.
  23. Vicente, G.A.; Davenport, J.C.; Scofield, R.A. The role of orographic and parallax corrections on real time high resolution satellite rainfall rate distribution. International Journal of Remote Sensing 2002, 23, 221-230. open in new tab
  24. Koenig, M. Description of the parallax correction functionality Available online: https://cwg.eumetsat.int/parallax-corrections/ (accessed on Jan 17, 2020).
  25. Wolfgang, T. Geodesy, an introduction; open in new tab
  26. De Gruyter, Berlin, 1980; ISBN 3-11-007232-7.
  27. Czarnecki, K. Geodezja współczesna;
  28. Wyd. 3 (1 w WN PWN)-1 dodruk.; Wydawnictwo Naukowe PWN: Warszawa, 2015; ISBN 978-83-01-18380-6. open in new tab
  29. Meteorological Products Extraction Facility Algorithm Specification Document Available online: https://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_TEN_SPE_0 4022_MSG_MPEF&RevisionSelectionMethod=LatestReleased&Rendition=Web (accessed on Nov 28, 2019). open in new tab
  30. Solve system of nonlinear equations - MATLAB fsolve Available online: https://www.mathworks.com/help/optim/ug/fsolve.html (accessed on Oct 23, 2019). open in new tab
  31. Wolf, R. Coordination Group for Meteorological Satellites LRIT/HRIT Global Specification Available online: https://www.cgms-info.org/documents/pdf_cgms_03.pdf (accessed on Nov 28, 2019). open in new tab
  32. PROJ contributors PROJ coordinate transformation software library; Open Source Geospatial Foundation, 2019; open in new tab
  33. Marshall, J.S.; Gunn, K.L.S. Measurement of snow parameters by radar. J. Meteor. 1952, 9, 322-327. open in new tab
  34. Roebeling, R.A.; Feijt, A.J.; Stammes, P. Cloud property retrievals for climate monitoring: Implications of differences between Spinning Enhanced Visible and Infrared Imager (SEVIRI) on METEOSAT-8 and Advanced Very High Resolution Radiometer (AVHRR) on NOAA-17. Journal of Geophysical Research: Atmospheres 2006, 111. 31. Optimal Cloud Analysis: Product Guide Available online: http://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_DMT_770106 &RevisionSelectionMethod=LatestReleased&Rendition=Web (accessed on Jan 8, 2020). open in new tab
  35. MSG Level 1.5 Image Data Format Description Available online: http://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_TEN_05105_ MSG_IMG_DATA&RevisionSelectionMethod=LatestReleased&Rendition=Web (accessed on Nov 28, 2019). open in new tab
  36. GOES N Databook Available online: https://goes.gsfc.nasa.gov/text/GOES-N_Databook/databook.pdf (accessed on Nov 29, 2019).
Sources of funding:
  • Działalność statusowa
Verified by:
Gdańsk University of Technology

seen 115 times

Recommended for you

Meta Tags