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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 3, pp. 240-251

Nighttime cloud classification by VIIRS satellite data

A.V. Skorokhodov 1 
1 Zuev Institute of Atmospheric Optics SB RAS, Tomsk, Russia
Accepted: 13.04.2020
DOI: 10.21046/2070-7401-2020-17-3-240-251
The analysis results of VIIRS satellite data obtained at nighttime for cloud classification are presented. We used visible light spectrum images of the day/night band of this sensor. This made it possible to use the results of texture analysis for images to describe clouds. The boundary conditions for the applicability of day/night images to solve the problem of cloud type recognition are determined. The nighttime cloud classification is presented taking into account the capabilities of satellite device. An algorithm for cloud type recognition based on the use of a probabilistic neural network is proposed. The software is based on the technology of parallel computing by general-purpose graphic processors. The search results of informative features for clouds based on the GRAD-II truncated enumeration method are presented. The nighttime cloud classification results based on the test sample and expert evaluation are discussed. We used satellite images obtained under various sensing conditions. The classification results of individual cloud types at nighttime by VIIRS and daytime by MODIS are discussed.
Keywords: cloud classification, nighttime, satellite data, texture features, cloud properties, day/night images, VIIRS
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References:

  1. Astafurov V. G., Skorokhodov A. V., Segmentatsiya sputnikovykh snimkov po teksturnym priznakam na osnove neirosetevykh tekhnologii (Segmentation of satellite images by textural parameters based on Neural Network Technologies), Issledovanie Zemli iz kosmosa, 2011, No. 6, pp. 10–20.
  2. Astafurov V. G., Skorokhodov A. V., Formirovanie sistemy informativnykh klassifikatsionnykh kharakteristik pri reshenii zadachi klassifikatsii oblachnosti po sputnikovym dannym MODIS (Formation of a set of informative classification features for solving cloud classification problem using MODIS satellite data), Trudy SPIIRAN, 2017, Vol. 53, No. 4, pp. 118–139.
  3. Bespalov D. P., Devyatkin A. M., Dovgalyuk Yu. A., Kondratyuk V. I., Kuleshov Yu. V., Svetlova T. P., Suvorov S. S., Timofeev V. I., Atlas oblakov (Cloud Atlas), Saint Petersburg: D’ART, 2011, 248 p.
  4. Volkova E. V., Otsenki parametrov oblachnogo pokrova, osadkov i opasnykh yavlenii pogody po dannym radiometra AVHRR s MISZ serii NOAA kruglosutochno v avtomaticheskom rezhime (Automatic estimation of cloud cover and precipitation parameters obtained by AVHRR NOAA for day and night conditions), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol. 10, No. 3, pp. 66–74.
  5. Zhizhin M. N., Elvidzh K. D., Poida A. A., Mul’tispektral’noe distantsionnoe zondirovanie nochnoi poverkhnosti Zemli (Multispectral nighttime remote sensing of the Earth), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 3, pp. 9–26.
  6. Kravtsov G. A., Vychisleniya na klassifikatsiyakh. Otsenka klassifikatorov (Classification calculations. Classifier Assessment), Elektronnoe modelirovanie, 2016, Vol. 38, No. 6, pp. 15–24.
  7. Kod dlya operativnoi peredachi dannykh prizemnykh meteorologicheskikh nablyudenii s seti stantsii Rosgidrometa (Land station surface synoptic code FM 12-IX SYNOP), Moscow, 2013, 79 p.
  8. Oblaka i oblachnaya atmosfera (Clouds and cloudy atmosphere), Mazin I. P., Khrgian A.Kh. (eds.), Leningrad: Gidrometeoizdat, 1989, 647 p.
  9. Ackerman S. A., Frey R., Heidinger A., Li Y., Walther A., Platnik S., Meyer K. G., Wind G., Amarasinghe N., Wang C., Marchant B., Holz R. E., Dutcher S., Hubanks P., EOS MODIS and SNPP VIIRS Cloud Properties: User Guide for the Climate Data Record Continuity Level-2 Cloud Top and Optical Properties Product (CLDPROP), Greenbelt: Goddard Space Flight Center, 2019, 65 p.
  10. Astafurov V. G., Kuriyanovich K. V., Skorokhodov A. V., A statistical model for describing the texture of cloud cover images from satellite data, Russian Meteorology and Hydrology, 2017, Vol. 42(4), pp. 248–257.
  11. Bankert R. L., Mitrescu C., Miller S. W., Wade R. H., Comparison of GOES cloud classification algorithms employing explicit and implicit physics, J. Applied Meteorology and Climatology, 2009, Vol. 48, pp. 1411–1421.
  12. Bony S., Stevens B., Frierson D. M. W., Jakob C., Kageyama M., Pincus R., Shepherd T. G., Sherwood S. C., Siebesma A. P., Sobel A. H., Watanabe M., Webb M. J., Clouds, circulation and climate sensitivity, Nature Geoscience, 2015, Vol. 8, pp. 261–268.
  13. Bretherton C. S., Blossey P. N., Khairoutdinov M., An energy-balance analysis of deep convective self-aggregation above uniform SST, J. Atmospheric Sciences, 2005, Vol. 62, pp. 4273–4292.
  14. Chernokulsky A. V., Esau I., Bulygina O. N., Davy R., Mokhov I. I., Outten S., Semenov V. A., Climatology and interannual variability of cloudiness in the Atlantic Arctic from surface observations since the late nineteenth century, J. Climate, 2017, Vol. 30, pp. 2103–2020.
  15. Davis G., History of the NOAA satellite program, J. Applied Remote Sensing, 2007, Vol. 1, pp. 012504–012504.
  16. Evan A. T., Heidinger A. K., Vimont D. J., Arguments against a physical long-term trend in global ISCCP cloud amounts, Geophysical Research Letters, 2007, Vol. 34, L04701.
  17. Fowler L. D., Randall D. A., Liquid and Ice Cloud Microphysics in the CSU General Circulation Model. Part III: Sensitivity to Modeling Assumptions, J. Climate, 1996, Vol. 9, pp. 561–586.
  18. Liu Y., Xia J., Shi C. X., Hong Y., An improved cloud classification algorithm for China’s FY-2C multi-channel images using artificial neural network, Sensors, 2009, Vol. 9, pp. 5558–5579.
  19. Menzel W. P., Remote sensing applications with meteorological satellites, Madison: University of Wisconsin, 2006, 307 p.
  20. Pressel K. G., Kaul C. M., Schneider T., Possible climate transitions from breakup of stratocumulus decks under greenhouse warming, Nature Geoscience, 2019, Vol. 12, pp. 163–167.
  21. Skorokhodov A. V., Astafurov V. G., Evsutkin T. V., Application of statistical models of image texture and physical parameters of clouds for their classification on MODIS satellite images, Izvestiya, Atmospheric and Oceanic Physics, 2019, Vol. 55(9), pp. 1053–1064.
  22. Xiong X., Wenny B. N., Angal A., Barnes W., Salomonson V., Summary of Terra and Aqua MODIS long-term performance, Proc. IGARSS, 2011, pp. 4006–4009.
  23. Young A. H., Knapp K. R., Inamdar A., Hankins W., Rossow W. B., The International Satellite Cloud Climatology Project H-Series climate data record product, Earth System Science Data, 2018, Vol. 10, pp. 583–593.