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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

résumé

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.

mots-clés

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

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