سابِق|تال

Remote sensing inversion of sea ice concentration by a middle-resolution imaging spectrometer

  • role： First author第一作者
  • Affiliation：

    Ocean University of China, Qingdao 266100, China

  • Email：kaiqishi_0920@163.com
  • Introduction：史凯琦，1993年生，男，硕士研究生，研究方向为海洋光学遥感。E-mail：kaiqishi_0920@163.com
SHI Kaiqi1,  
  • Affiliation：

    National Satellite Ocean Application Service, Beijing 100081, China

ZOU Bin3,  
  • role： Corresponding author通信作者
  • Affiliation：

    Ocean University of China, Qingdao 266100, China

    Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

  • Email：chenshuguo@ouc.edu.cn
  • Introduction：陈树果，1984年生，男，副教授，研究方向为海洋光学与水色遥感。E-mail:chenshuguo@ouc.edu.cn
CHEN Shuoguo12*,  
  • Affiliation：

    Ocean University of China, Qingdao 266100, China

XUE Cheng1,  
  • Affiliation：

    Key Laboratory of Space Ocean Remote Sensing and Applications, Beijing 100081, China

SHI Lijian4,  
  • Affiliation：

    Ocean University of China, Qingdao 266100, China

    Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

ZHANG Tinglu12

ملخص

Sea ice concentration, which refers to the percentage of sea ice in an area, is an important parameter describing the characteristics of sea ice. Remote sensing monitoring of sea ice is crucial to understand the role of polar regions in the global climate system and global warming. Sea ice information is of great importance in ship transportation, weather forecasting, and global climate forecast. At present, passive microwave radiometers are the main means of monitoring sea ice concentration; however, because microwaves present wavelength limitations, conventional sea ice concentration products cannot be used in practical applications in small areas. Visible-light-infrared remote sensing can retrieve sea ice concentrations, and its advantages over other sensing methods include high spatial resolution.This work focuses on the development of a sea ice concentration algorithm suitable for medium-resolution images. Few medium-resolution sea ice concentration products based on visible-light remote sensing are available, and only NOAA has released relevant operational products. However, the accuracy of its algorithm for low-concentration sea ice inversion is low. This paper proposes an improved algorithm based on the existing algorithm to determine the ice node via the nearest-pixel method. MODIS data are used as a data source to calculate the sea ice concentration, and Landsat 8 OLI data with a spatial resolution of 30 m are used for comparative verification.Results show that the improved algorithm can improve the inversion accuracy of low-concentration sea ice. The Liu algorithm has the disadvantage of overestimation. In the case of sea ice concentrations of 0% — 50%, the average deviation of the Liu algorithm is 13%, and its standard deviation is 38%. By comparison, the average deviation of the improved algorithm is 5%, and its standard deviation is 32%. In the case of sea ice concentrations of 0% — 100%, the average deviation of the Liu algorithm is 4%, and its standard deviation is 32%. By comparison, the average deviation of the improved algorithm is -3%, and its standard deviation is 28%. In the case of sea ice concentrations of 0% —50%, the accuracy of the improved algorithm is better than that of the Liu algorithm. When the sea ice concentration is close to 100%, the results of the two algorithms are highly similar. Overall, the improvement effect of the proposed algorithm is related to the actual sea ice concentration, and the improved algorithm is more accurate than the Liu algorithm for low-concentration sea ice regions, such as ice-water transitions and broken ice coverage.

مفهوم

remote sensing;sea ice;ice water recognition;MODIS;nearest pixel;SIC algorithm

References

  1. 1.
    Aldenhoff W, Berg A and Eriksson L E B. 2016. Sea ice concentration estimation from Sentinel-1 synthetic aperture radar images over the Fram Strait//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China: IEEE: 7675-7677
  2. 2.
    Baldwin D, Tschudi M, Pacifici F and Liu Y H. 2017. Validation of Suomi-NPP VIIRS sea ice concentration with very high-resolution satellite and airborne camera imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 122-138
  3. 3.
    Cavalieri D J, Markus T, Hall D K, Ivanoff A and Glick E. 2010. Assessment of AMSR-E Antarctic winter sea-ice concentrations using Aqua MODIS. IEEE Transactions on Geoscience and Remote Sensing, 48(9): 3331-3339
  4. 4.
    Drüe C and Heinemann G. 2004. High-resolution maps of the sea-ice concentration from MODIS satellite data. Geophysical Research Letters, 31(20): L20403
  5. 5.
    Guo Y Y and Jiao M L. 2010. Using MODIS data to retrieve distribution of sea ice in Bohai Sea. Journal of Huaihai Institute of Technology (Natural Science Edition), 19(1): 84-87
  6. 6.
    Hall D K, Riggs G A and Salomonson V V. 2001. Algorithm theoretical basis document (ATBD) for the MODIS snow and sea ice-mapping algorithms[EB/OL]. (2001-09-01).
  7. 7.
    Han S Q, Li Z F and Sun Z G. 2005. Observational study of MODIS data on sea ice of China’s Bohai Sea. Scientia Meteorologica Sinica, 25(6): 624-628
  8. 8.
    Knight E J and Kvaran G. 2014. Landsat-8 operational land imager design, characterization and performance. Remote Sensing, 6(11): 10286-10305
  9. 9.
    Laxon S W, Giles K A, Ridout A L, Wingham D J, Willatt R, Cullen R, Kwok R, Schweiger A, Zhang J L, Haas C, Hendricks S, Krishfield R, Kurtz N, Farrell S and Davidson M. 2013. CryoSat-2 estimates of Arctic sea ice thickness and volume. Geophysical Research Letters, 40(4): 732-737
  10. 10.
    Liu Y H, Key J and Mahoney R. 2016. Sea and freshwater ice concentration from VIIRS on Suomi NPP and the future JPSS satellites. Remote Sensing, 8(6): 523
  11. 11.
    Perovich D K. 1996. The Optical Properties of Sea Ice, CRREL Monogr. No. 96-1. U.S. Army Cold Regions Research and Engineering Laboratory: 25
  12. 12.
    Riggs G A and Hall D K. 2015. MODIS sea ice products user guide to collection 6[EB/OL]. (2015-03-17).
  13. 13.
    Riggs G A, Hall D K and Ackerman S A. 1999. Sea ice extent and classification mapping with the moderate resolution imaging spectroradiometer airborne simulator. Remote Sensing of Environment, 68(2): 152-163
  14. 14.
    Salomonson V V, Barnes W and Masuoka E J. 2006. Introduction to MODIS and an overview of associated activities//Qu J J, Gao W, Kafatos M, Murphy R E and Salomonson V V, eds. Earth Science Satellite Remote Sensing. Berlin, Heidelberg: Springer: 12-32
  15. 15.
    Su J, Hao G H, Ye X X and Wang W B. 2013. The experiment and validation of sea ice concentration AMSR-E retrieval algorithm in polar region. Journal of Remote Sensing, 17(3): 495-513
  16. 16.
    Wang X Y, Guan L and Li L L. 2018. Comparison and validation of sea ice concentration from FY-3B/MWRI and Aqua/AMSR-E observations. Journal of Remote Sensing, 22(5): 723-736
  17. 17.
    Wu K Q, Xu Y and Hao Y M. 2005. Application in sea ice remote sensing of MODIS data. Marine Forecasts, 22(S1): 44-49
  18. 18.
    Wu L T, Wu H D, Sun L T, Zhang Y F, Liu Y and Wei X Q. 2006. Retrieval of sea ice in the Bohai sea from MODIS data. Periodical of Ocean University of China, 36(2): 173-179
  19. 19.
    Ye X X, Su J, Wang Y, Hao G H and Hou J Q. 2011. Assessment of AMSR-E sea ice concentration in ice margin zone using MODIS data//2011 International Conference on Remote Sensing, Environment and Transportation Engineering. Nanjing, China: IEEE: 3869-3873
  20. 20.
    Zhang N and Zhang Q H. 2014. Retrieval of the sea ice area from MODIS data by CART decision tree method. Marine Science Bulletin, 33(3): 321-327
  21. 21.
    Zhang S G, Zhao J P, Li M, Liu S X and Zhang S W. 2018. An improved dual-polarized ratio algorithm for sea ice concentration retrieval from passive microwave satellite data and inter-comparison with ASI, ABA and NT2. Journal of Oceanology and Limnology, 36(5): 1494-1508
  22. 22.
    Zhang X, Zhou C X, E D C and An J C. 2014. Monitoring of Antarctic sea ice based on the multichannel MODIS data. Geomatics and Information Science of Wuhan University, 39(10): 1194-1198
  23. 23.
    Zhao J C, Zhou X, Sun X Y, Cheng J J, Hu B and Li C H. 2017. The inter comparison and assessment of satellite sea-ice concentration datasets from the arctic. Journal of Remote Sensing, 21(3): 351-364
  24. 24.
    Zhao X, Su H Y, Shi Z Y and Pang X P. 2015. Intercomparison of multi-sources sea ice concentration data in Antarctic. Geomatics and Information Science of Wuhan University, 40(11): 1460-1466
  25. 25.
    Zou B, Lin M S, Shi L J, Zou Y R, Jia Y J and Zeng T. 2018. Application of remote sensing technology in ocean disaster. City and Disaster Reduction, 123(6): 61-65

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website