利用多源数据对海冰密集度反演的算法验证Validation of concentration retrieval method of sea ice with multi-source data
刘艳霞,王泽民,刘婷婷
摘要(Abstract):
海冰密集度对全球气候变化研究有重要的意义,其反演结果的验证工作也被广泛关注,但结合多源数据反演,同时对两种算法验证的研究较少的现状,该文利用ASPeCt船测海冰密集度数据对Bootstrap算法和NASA Team(NT)算法基于SSM/I数据估算的海冰密集度精度进行验证,并与MODIS影像反演获得的海冰密集度进行对比。研究结果显示两种海冰密集度算法获得的反演结果与ASPeCt船测值偏差分别为2.26%和7.27%,均方根误差分别为11.39%和12.32%。相比之下,MODIS结果与ASPeCt船测海冰密集度比较得到偏差为3%,均方根误差为5.21%。Bootstrap算法、NT算法与ASPeCt船测值比较的偏差和均方根误差显示两种算法精度相近;由于MODIS数据分辨率与ASPeCt船测数据相近,所以其反演精度较优;但因时空分辨率的限制,各种结果都具有一定的不确定性。
关键词(KeyWords): SSM/I;海冰密集度;Bootstrap算法;NASA Team算法;MODIS
基金项目(Foundation): 测绘地理信息公益性行业科研专项资助项目(201412009);; 国家海洋局海洋—大气化学与全球变化重点实验室开放基金课题(GCMAC1405);; 国家高技术研究发展计划项目(2013AA12A301)
作者(Author): 刘艳霞,王泽民,刘婷婷
DOI: 10.16251/j.cnki.1009-2307.2016.07.018
参考文献(References):
- [1]LU Ping,LI Zeng.A method of obtaining ice concentration and floe size from shipboard oblique sea ice images[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(7):2771-2780.
- [2]席颖,孙波,李鑫.利用船测数据以及Landsat-7ETM+影像评估南极海冰区AMSR-E海冰密集度[J].遥感学报,2013,17(3):520-526.
- [3]BOVITH T,ANDERSEN S.Sea Ice Concentration from Single-Polarized SAR data using Second-Order Grey Level Statistics and Learning Vector Quantization[J].Scientific Report,2005:2215.
- [4]COMISO J C.Characteristics of arctic winter sea ice from satellite multi spectral microwave observations[J].Journal of geophysical research,1986,91(C1):975-994.
- [5]MARKUS T,CAVALIERI D J.AMSR-E algorithm theoretical basis document:sea ice products[J].IEEE Transactions on Geoscience and Remote Sensing,2008,31(4):842-852.
- [6]MARKUS T,CAVALIERI D J,ALVARO I.A Algorithm Theoretical Basis Document:Sea Ice Products[M].2011.
- [7]IVANOVA N,JOHANNESSEN O M,PEDERSEN L T.Retrieval of arctic sea ice parameters by satellite passive microwave sensors a comparison of eleven sea ice concentration algorithms[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(11):7233-7244.
- [8]CAVELIERI D J,GLOERSEN P.Determination of sea ice parameters with the NIMBUS 7SMMR[J].Journal of Geophysical Research,1984,89(D4):5355-5369.
- [9]CAVALIERI D J,CRAWFORD J P,DRINKWATER M R.Aircraft active and passive microwave validation of the sea ice concentration from the defense meteorological satellite program special sensor microwave imager[J].Journal of geophysical research,1991,96(c12):21998-22008.
- [10]LIU Tingting,LIU Yanxia,HUANG Xing,et al.Fully constrained least squares for antarctic sea ice concentration estimation utilizing passive microwave data[J].IEEE Geoscience and Remote Sensing Letters,2015,12(11):2291-2295.
- [11]MARKUS T,CAVALIERI D J.An enhancement of the NASA Team sea ice algorithm[J].IEEE Transactions on Geoscience and Remote Sensing,2000,38(3):1387-1398.
- [12]KERN S,KALESCHKE L,CLAUSI D A.A comparison of two 85GHz SSM/I ice concentration algorithms with AVHRR and ERS-2 SAR imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(10):2294-2306.
- [13]CHANG A T C,FOSTER J L,HALL D K.Nimbus-7SMMR derived global snow cover parameters[J].Annals of Glaciology,1987,9(11):39-44.
- [14]ALEXANDER B,STEFAN K.Comparison of AMSRE sea ice concentrations with ASPeCt ship observations around Antarctica[J].Geoscience and Remote Sensing,2012,34(2):3257-3561.
- [15]CAVALIERI D J,MARKUS T,HALL D K.Assessment of AMSR-E Antarctic winter sea Ice concentrations using Aqua MODIS[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(9):3331-3339.
- [16]苏洁,郝光华,叶鑫欣,等.极区海冰密集度AMSR-E数据反演算法的试验与验证[J].遥感学报,2013,17(3):504-513.
- [17]MOHAMMED S.Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations[J].Remote Sensing of Environment.2012,30(2):36-50.