Retrievals of Arctic sea ice melt pond depth and underlying ice thickness using optical data

被引:0
|
作者
ZHANG Hang [1 ]
YU Miao [1 ]
LU Peng [1 ]
ZHOU Jiaru [1 ]
LI Zhijun [1 ]
机构
[1] State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
D O I
10.13679/j.advps.2021.0021
中图分类号
P715.7 [遥测技术设备]; P731.15 [海冰];
学科分类号
摘要
Melt pond is a distinctive characteristic of the summer Arctic,which affects energy balance in the Arctic system.The Delta-Eddington model (BL) and Two-str Eam r Adiative transfer model (TEA) are employed to retrieving pond depth Hand underlying ice thickness Haccording to the ratio X of the melt-pond albedo in two bands.Results showed that whenλ=359 nm andλ=605 nm,the Pearson’s correlation coefficient r between X and His 0.99 for the BL model.The result of TEA model was similar to the BL model.The retrievals of Hfor the two models agreed well with field observations.For H,the highest r(0.99) was obtained whenλ=447 nm andλ=470 nm for the BL model,λ=447 nm andλ=451 nm for the TEA model.Furthermore,the BL model was more suitable for the retrieval of thick ice (0<H<3.5 m,R~2=0.632),while the TEA model is on the contrary (H<1 m,R~2=0.842).The present results provide a potential method for the remote sensing on melt pond and ice in the Arctic summer.
引用
收藏
页码:105 / 117
页数:13
相关论文
共 50 条
  • [21] Snow depth on Arctic sea ice
    Warren, SG
    Rigor, IG
    Untersteiner, N
    Radionov, VF
    Bryazgin, NN
    Aleksandrov, YI
    Colony, R
    JOURNAL OF CLIMATE, 1999, 12 (06) : 1814 - 1829
  • [22] A First Assessment of IceBridge Snow and Ice Thickness Data Over Arctic Sea Ice
    Farrell, Sinead Louise
    Kurtz, Nathan
    Connor, Laurence N.
    Elder, Bruce C.
    Leuschen, Carlton
    Markus, Thorsten
    McAdoo, David C.
    Panzer, Ben
    Richter-Menge, Jacqueline
    Sonntag, John G.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2012, 50 (06): : 2098 - 2111
  • [23] Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge
    Kwok, Ron
    Kurtz, Nathan T.
    Brucker, Ludovic
    Ivanoff, Alvaro
    Newman, Thomas
    Farrell, Sinead L.
    King, Joshua
    Howell, Stephen
    Webster, Melinda A.
    Paden, John
    Leuschen, Carl
    MacGregor, Joseph A.
    Richter-Menge, Jacqueline
    Harbeck, Jeremy
    Tschudi, Mark
    CRYOSPHERE, 2017, 11 (06): : 2571 - 2593
  • [24] Sensitivity of Arctic sea ice to melt pond processes and atmospheric forcing: A model study
    Sterlin, Jean
    Fichefet, Thierry
    Massonnet, Francois
    Lecomte, Olivier
    Vancoppenolle, Martin
    OCEAN MODELLING, 2021, 167
  • [25] Diurnal Melt Detection on Arctic Sea Ice Using Tandem QuikSCAT and SeaWinds Data
    Hicks, B. R.
    Long, D. G.
    2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, 2006, : 4112 - 4114
  • [26] The refreezing of melt ponds on Arctic sea ice
    Flocco, Daniela
    Feltham, Daniel L.
    Bailey, Eleanor
    Schroeder, David
    JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2015, 120 (02) : 647 - 659
  • [27] The color of melt ponds on Arctic sea ice
    Lu, Peng
    Lepparanta, Matti
    Cheng, Bin
    Li, Zhijun
    Istomina, Larysa
    Heygster, Georg
    CRYOSPHERE, 2018, 12 (04): : 1331 - 1345
  • [28] Observations of melt ponds on Arctic sea ice
    Fetterer, F
    Untersteiner, N
    JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 1998, 103 (C11): : 24821 - 24835
  • [29] Assessment of contemporary satellite sea ice thickness products for Arctic sea ice
    Sallila, Heidi
    Farrell, Sinead Louise
    McCurry, Joshua
    Rinne, Eero
    CRYOSPHERE, 2019, 13 (04): : 1187 - 1213
  • [30] Impacts of Sea Ice Thickness Initialization on Seasonal Arctic Sea Ice Predictions
    Dirkson, Arlan
    Merryfield, William J.
    Monahan, Adam
    JOURNAL OF CLIMATE, 2017, 30 (03) : 1001 - 1017