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A Cloud Detection Algorithm for Tiangong-2 Remote Sensing Data Over the Tibetan Plateau

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Book cover Proceedings of the Tiangong-2 Remote Sensing Application Conference

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 541))

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Abstract

Cloud plays an essential role in energy budget and climate change. As the “Third Pole” of the earth, accurate cloud monitoring over the Tibetan Plateau is indispensable. The Tiangong-2 Space Laboratory provides multispectral remote sensing data with relatively high spatial resolution and is useful to get an accurate spatial distribution of cloud. We propose the single band and multiband threshold methods for cloud detection using Tiangong-2 data. To generate appropriate thresholds, the spectral reflectance of the thick cloud, the thin cloud, and land objects were analyzed based on the data from visible to shortwave infrared bands. It was found that the cloud can be accurately distinguished from snow and other land objects including water, ice, bare soil, plant and so on. Compared with Tiangong-2, the geostationary satellite Himawari-8 significantly overestimated the cloud cover over the Tibetan Plateau. The False Alarm Rate (FAR) and the Cloud Hit Rate (CHR) were 29.12% and 94.77%, respectively. Within the incorrectly classified pixels of Himawari-8 data, the cloud types were mainly altocumulus and cumulus. The main reason is probably the difference in classification method and spatial resolution.

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References

  1. Baker, M.: Cloud microphysics and climate. Science 276, 1072–1078 (1997)

    Article  Google Scholar 

  2. Cheng, G., Wu, T.: Responses of permafrost to climate change and their environmental significance, Qinghai–Tibet Plateau. J. Geophys. Res. Earth Surf. 112 (2007)

    Google Scholar 

  3. Ni, J.: A simulation of biomes on the Tibetan plateau and their responses to global climate change. Mt. Res. Dev. 20, 80–89 (2000)

    Article  Google Scholar 

  4. Wang, Z., Li, Z., Xu, M., Yu, G.: River Morphodynamics and Stream Ecology of the Qinghai-Tibet Plateau. CRC Press (2016)

    Google Scholar 

  5. Dim, J., Murakami, H., Nakajima, T., Nordell, B., Heidinger, A., Takamura, T.: The recent state of the climate: driving components of cloud-type variability. J. Geophys. Res. Atmos. 116 (2011)

    Google Scholar 

  6. Favre, A., Päckert, M., Pauls, S.U., Jähnig, S.C., Uhl, D., Michalak, I., Muellner-Riehl, A.N.: The role of the uplift of the Qinghai–Tibetan plateau for the evolution of Tibetan biotas. Biol. Rev. 90, 236–253 (2015)

    Article  Google Scholar 

  7. Yang, K., Wu, H., Qin, J., Lin, C., Tang, W., Chen, Y.: Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: a review. Global Planet Change 112, 79–91 (2014)

    Article  Google Scholar 

  8. Zhang, R., Jiang, D., Zhang, Z., Yu, E.: The impact of regional uplift of the Tibetan Plateau on the Asian monsoon climate. Palaeogeogr. Palaeoclimatol. Palaeoecol. 417, 137–150 (2015)

    Article  Google Scholar 

  9. Kang, S., Xu, Y., You, Q., Flügel, W.-A., Pepin, N., Yao, T.: Review of climate and cryospheric change in the Tibetan Plateau. Environ. Res. Lett. 5, 015101 (2010)

    Article  Google Scholar 

  10. Kutzbach, J., Prell, W., Ruddiman, W.F.: Sensitivity of Eurasian climate to surface uplift of the Tibetan plateau. J. Geol. 101, 177–190 (1993)

    Article  Google Scholar 

  11. Frey, R.A., Ackerman, S.A., Liu, Y., Strabala, K.I., Zhang, H., Key, J.R., Wang, X.: Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5. J. Atmos. Ocean. Technol. 25, 1057–1072 (2008)

    Article  Google Scholar 

  12. Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T.: An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. J. Meteorol. Soc. Japan. Ser. II, 94, 151–183 (2016)

    Google Scholar 

  13. Ishida, H., & Nakajima, T.Y.: Development of an unbiased cloud detection algorithm for a spaceborne multispectral imager. J. Geophys. Res. Atmos. 114 (2009)

    Google Scholar 

  14. Rossow, W.B., Schiffer, R.A.: ISCCP cloud data products. Bull. Am. Meteor. Soc. 72, 2–20 (1991)

    Article  Google Scholar 

  15. Wang, Y., Zhao, C.: Can MODIS cloud fraction fully represent the diurnal and seasonal variations at DOE ARM SGP and manus sites? J. Geophys. Res. Atmos. 122, 329–343 (2017)

    Article  Google Scholar 

  16. Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Joseph Hughes, M., Laue, B.: Cloud detection algorithm comparison and validation for operational landsat data products. Remote Sens. Environ. 194, 379–390 (2017)

    Article  Google Scholar 

  17. Lin, C.-H., Lin, B.-Y., Lee, K.-Y., Chen, Y.-C.: Radiometric normalization and cloud detection of optical satellite images using invariant pixels. ISPRS J. Photogramm. Remote Sens. 106, 107–117 (2015)

    Article  Google Scholar 

  18. Shang, H., Letu, H., Nakajima, T.Y., Wang, Z., Ma, R., Wang, T., Lei, Y., Ji, D., Li, S., Shi, J.: Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data. Sci. Rep. 8, 1105 (2018)

    Article  Google Scholar 

  19. Chauvin, R., Nou, J., Thil, S., Traoré, A., Grieu, S.: Cloud detection methodology based on a sky-imaging system. Energy Procedia 69, 1970–1980 (2015)

    Article  Google Scholar 

  20. Dürr, B., Philipona, R.: Automatic cloud amount detection by surface longwave downward radiation measurements. J. Geophys. Res. Atmos. 109 (2004)

    Google Scholar 

  21. Sun, L., Mi, X., Wei, J., Wang, J., Tian, X., Yu, H., Gan, P.: A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths. ISPRS J. Photogramm. Remote Sens. 124, 70–88 (2017)

    Article  Google Scholar 

  22. Sun, L., Wei, J., Wang, J., Mi, X., Guo, Y., Lv, Y., Yang, Y., Gan, P., Zhou, X., Jia, C., Tian, X.: A universal dynamic threshold cloud detection algorithm (UDTCDA) supported by a prior surface reflectance database. J. Geophys. Res. Atmos. 121, 7172–7196 (2016)

    Article  Google Scholar 

  23. Hall, D.K., Riggs, G.A., Salomonson, V.V.: Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sens. Environ. 54, 127–140 (1995)

    Article  Google Scholar 

  24. Roy, D.P., Kovalskyy, V., Zhang, H., Vermote, E.F., Yan, L., Kumar, S., Egorov, A.: Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016)

    Article  Google Scholar 

  25. Hutchison, K.D., Iisager, B.D., Hauss, B.: The use of global synthetic data for pre-launch tuning of the VIIRS cloud mask algorithm. Int. J. Remote Sens. 33, 1400–1423 (2012)

    Article  Google Scholar 

  26. Mace, G.G., Jakob, C., Moran, K.P.: Validation of hydrometeor occurrence predicted by the ECMWF model using millimeter wave radar data. Geophys. Res. Lett. 25, 1645–1648 (1998)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the key program of NSFC (Grant No. 413311771). Thanks to the China Manned Space Engineering for providing space science and application data products of Tiangong-2. The CLP data used in this paper was supplied by the P-Tree System, Japan Aerospace Exploration Agency (JAXA).

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Correspondence to Yanan Liu .

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Chu, Q., Liu, Y., Yan, G. (2019). A Cloud Detection Algorithm for Tiangong-2 Remote Sensing Data Over the Tibetan Plateau. In: Gu, Y., Gao, M., Zhao, G. (eds) Proceedings of the Tiangong-2 Remote Sensing Application Conference. Lecture Notes in Electrical Engineering, vol 541. Springer, Singapore. https://doi.org/10.1007/978-981-13-3501-3_8

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  • DOI: https://doi.org/10.1007/978-981-13-3501-3_8

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