Antarctic Blue Ice Classification Using Sentinel-1/2: An Application in the Lambert Glacier Basin

被引:1
|
作者
Zhou, Yimeng [1 ,2 ,3 ]
Zheng, Lei [1 ,2 ,3 ]
Hui, Fengming [1 ,2 ,3 ]
Xu, Rui [4 ]
Cheng, Xiao [1 ,2 ,3 ]
机构
[1] Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Guangzhou 519082, Peoples R China
[2] Sun Yat Sen Univ, Southern Marine Sci & Engn Guangdong Lab, Zhuhai 519082, Peoples R China
[3] Sun Yat Sen Univ, Key Lab Comprehens Observat Polar Environm, Minist Educ, Zhuhai 519082, Peoples R China
[4] Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Guangzhou 519082, Peoples R China
基金
中国国家自然科学基金;
关键词
Antarctica; blue ice; coherence; microwave; DRONNING MAUD LAND; SURFACE-ENERGY BALANCE; EAST ANTARCTICA; SHELF SYSTEM; MASS-BALANCE; ETM PLUS; SNOW; AREAS;
D O I
10.1109/TGRS.2024.3373876
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The Antarctic blue ice can be classified into wind- and melt-induced based on their origins. They play a different role in the development of surface water systems, the surface energy balance, and the infrastructure. Currently, visible light remote sensing is the most effective method for mapping blue ice. However, optical imagery faces difficulties in classifying blue ice accurately, and it is also greatly influenced by weather conditions. Synthetic aperture radar (SAR) images have the potential to map blue ice under all weather conditions, but it is difficult to distinguish blue ice from other similar weak microwave reflecting surfaces. In this study, by employing a segmentation method based on band ratios of the Sentinel-2 images, we delineated the geographical distribution of blue ice area (BIA) in the Lambert Glacier Basin (LGB). Taking advantage of the disparity in coherence levels between melt- and wind-induced blue ice, we performed blue ice classification in the LGB using Sentinel-1 images. The proposed method achieves an overall accuracy of 0.91 and F1-score of 0.91 and provides blue ice types with a spatial resolution of 10 m. The total area of blue ice in the Lambert Basin was estimated to be approximately 1.986 x 10(4) km(2). Among them, the area of melt-induced blue ice was approximately 1.276 x 10(4) km(2), while the wind-induced blue ice covered around 0.710 x 10(4) km(2). Melt-induced BIA was predominantly distributed in low-altitude coastal areas and downstream of glaciers, exhibiting higher surface temperatures compared to wind-induced BIA. Wind-induced BIA, on the other hand, was mainly found near nunataks and exposed rocks, displaying higher albedo than melt-induced BIA.
引用
收藏
页码:18 / 18
页数:1
相关论文
共 50 条
  • [41] A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin
    Snapir, B.
    Momblanch, A.
    Jain, S. K.
    Waine, T. W.
    Holman, I. P.
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2019, 74 : 222 - 230
  • [42] Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and-2 Data
    Brinkhoff, James
    Vardanega, Justin
    Robson, Andrew J.
    REMOTE SENSING, 2020, 12 (01)
  • [43] SAR and Passive Microwave Fusion Scheme: A Test Case on Sentinel-1/AMSR-2 for Sea Ice Classification
    Khachatrian, Eduard
    Dierking, Wolfgang
    Chlaily, Saloua
    Eltoft, Torbjorn
    Dinessen, Frode
    Hughes, Nick
    Marinoni, Andrea
    GEOPHYSICAL RESEARCH LETTERS, 2023, 50 (04)
  • [44] Seasonal ice flow velocity variations of Polar Record Glacier, East Antarctica during 2016-2019 using Sentinel-1 data
    Tomar, Kiledar Singh
    Kumari, Sheetal
    Luis, Alvarinho J.
    GEOCARTO INTERNATIONAL, 2022, 37 (16) : 4671 - 4686
  • [45] Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2
    Wang, Mingfeng
    Koenig, Marcel
    Oppelt, Natascha
    REMOTE SENSING, 2021, 13 (21)
  • [46] Application of Sentinel-1 VH and VV and Sentinel-2 for soil moisture studies
    Dabrowska-Zielinska, Katarzyna
    Budzynska, Maria
    Gurdak, Radoslaw
    Musial, Jan
    Malinska, Alicja
    Gatkowska, Martyna
    Bartold, Maciej
    ACTIVE AND PASSIVE MICROWAVE REMOTE SENSING FOR ENVIRONMENTAL MONITORING, 2017, 10426
  • [47] ON THE FUSION STRATEGIES OF SENTINEL-1 AND SENTINEL-2 DATA FOR LOCAL CLIMATE ZONE CLASSIFICATION
    Gawlikowski, Jakob
    Schmitt, Michael
    Kruspe, Anna
    Zhu, Xiao Xiang
    IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 2081 - 2084
  • [48] Multispectral Sentinel-2 and SAR Sentinel-1 Integration for Automatic Land Cover Classification
    De Fioravante, Paolo
    Luti, Tania
    Cavalli, Alice
    Giuliani, Chiara
    Dichicco, Pasquale
    Marchetti, Marco
    Chirici, Gherardo
    Congedo, Luca
    Munafo, Michele
    LAND, 2021, 10 (06)
  • [49] An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping
    Ghorbanzadeh, Omid
    Didehban, Khalil
    Rasouli, Hamid
    Kamran, Khalil Valizadeh
    Feizizadeh, Bakhtiar
    Blaschke, Thomas
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2020, 9 (10)
  • [50] Mediterranean Shrublands Biomass Estimation Using Sentinel-1 and Sentinel-2
    Chang, Jisung
    Shoshany, Maxim
    2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 5300 - 5303