A Blended Sea Ice Concentration Product from AMSR2 and VIIRS

被引:5
|
作者
Dworak, Richard [1 ]
Liu, Yinghui [2 ]
Key, Jeffrey [2 ]
Meier, Walter N. [3 ]
机构
[1] Univ Wisconsin, Cooperat Inst Meteorol Satellite Studies, 1225 West Dayton St, Madison, WI 53706 USA
[2] NOAA NESDIS, Ctr Satellite Applicat & Res, 1225 West Dayton St, Madison, WI 53706 USA
[3] Univ Colorado, Natl Snow & Ice Data Ctr, CIRES, 449 UCB, Boulder, CO 80309 USA
关键词
Arctic; sea ice concentration; melting ice; high spatial resolution; blending technique; best-linear unbiased estimator; thermal infrared; visible; NDSI; passive microwave; uncertainties; VIIRS; AMSR2; Sentinel; Synthetic Aperture Radar; SATELLITE; VALIDATION; RETRIEVAL; SURFACE; ENHANCEMENT; ALGORITHMS; ATMOSPHERE; TRENDS; CLOUD;
D O I
10.3390/rs13152982
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.
引用
收藏
页数:19
相关论文
共 50 条
  • [41] Daily Sea Ice Concentration Product over Polar Regions Based on Brightness Temperature Data from the HY-2B SMR Sensor
    Wu, Suhui
    Shi, Lijian
    Zou, Bin
    Zeng, Tao
    Dong, Zhaoqing
    Lu, Dunwang
    REMOTE SENSING, 2023, 15 (06)
  • [42] Assessment of AMSR-E sea ice concentration products at Ice edges in antarctic
    Su H.
    Pang X.
    Zhao X.
    Zhao, Xi (xi.zhao@whu.edu.cn), 2016, Editorial Board of Medical Journal of Wuhan University (41): : 559 - 564
  • [43] GCOM-W AMSR2 Soil Moisture Product Validation Using Core Validation Sites
    Bindlish, Rajat
    Cosh, Michael H.
    Jackson, Thomas J.
    Koike, Toshio
    Fujii, Hideyuki
    Chan, Steven K.
    Asanuma, Jun
    Berg, Aaron
    Bosch, D. David
    Caldwell, Todd
    Collins, Chandra Holifield
    McNairn, Heather
    Martinez-Fernandez, Jose
    Prueger, John
    Rowlandson, Tracy
    Seyfried, Mark
    Starks, Patrick
    Thibeault, Marc
    Van der Velde, R.
    Walker, Jeffrey P.
    Coopersmith, Evan J.
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2018, 11 (01) : 209 - 219
  • [44] Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites
    Liu, Yinghui
    Key, Jeffrey
    Mahoney, Robert
    REMOTE SENSING, 2016, 8 (06)
  • [45] SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures
    Meissner, Thomas
    Manaster, Andrew
    REMOTE SENSING, 2021, 13 (24)
  • [46] A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations
    Du, Jinyang
    Kimball, John S.
    Jones, Lucas A.
    Kim, Youngwook
    Glassy, Joseph
    Watts, Jennifer D.
    EARTH SYSTEM SCIENCE DATA, 2017, 9 (02) : 791 - 808
  • [47] Arctic Thin Ice Detection Using AMSR2 and FY-3C MWRI Radiometer Data
    Makynen, Marko
    Simila, Markku
    REMOTE SENSING, 2024, 16 (09)
  • [48] Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven years (2012-2018)
    Kaihotsu, Ichirow
    Asanuma, Jun
    Aida, Kentaro
    Oyunbaatar, Dambaravjaa
    SN APPLIED SCIENCES, 2019, 1 (11):
  • [49] Thin Ice Detection in the Barents and Kara Seas Using AMSR2 High-Frequency Radiometer Data
    Makynen, Marko
    Simila, Markku
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (10): : 7418 - 7437
  • [50] Sea ice concentration, ice temperature, and snow depth using AMSR-E data
    Comiso, JC
    Cavalieri, DJ
    Markus, T
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41 (02): : 243 - 252