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Journal of Geo-information Science >

2017 , Vol. 19 >Issue 2: 143 - 151

DOI: https://doi.org/10.3724/SP.J.1047.2017.00143

Orginal Article

Optimization of FY Arctic Sea Ice Dataset based on NSIDC Sea Ice Product

  • ZHAI Zhaokun , 1, 2 ,
  • LU Shanlong , 2, * ,
  • WANG Ping 1 ,
  • MA Lijuan 3 ,
  • LI Duo 3 ,
  • REN Yuyu 3 ,
  • WU Shengli 3
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  • 1. College of Geomatics, Shandong University of Science and Technology, Key Laboratory of Surveying and Mapping Technology on Island and Reef, State Bureau of Surveying and Mapping, Qingdao 266590, China
  • 2. Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science, Beijing 100101, China
  • 3. National Climate Center, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
*Corresponding author: LU Shanlong, E-mail:

Received date: 2016-05-01

  Request revised date: 2016-10-11

  Online published: 2017-02-17

Copyright

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Abstract

Arctic sea ice plays a very important role in the modulation of global climate and sea ice extent is a basic parameter for sea ice monitoring. In recent 40 years, a series of environmental and climatic issues such as degradation of Arctic natural environment, frequent extreme weather in the Northern Hemisphere and global sea-level rise are caused by continuous warming and apparent sea ice decrease in Arctic. So it′s important to know the extent, variation, trend of Arctic sea ice and its response to global climate change. The most commonly used datasets such as HadISST and OISST sea ice dataset provided long time series of changes in sea ice of the Arctic regions. However, the spatial resolution of these datasets is relatively low. There are some limits in the study of response of sea ice change in Arctic key regions to weather and climate in China. To overcome these problems and to make up the lack of passive microwave sea ice dataset provided by China, FY (Feng Yun) sea ice dataset is developed by NSMC (National Satellite Meteorological Center) on June 27th, 2011. In this dataset, the Enhanced NASA Team (NT2) algorithm is used based on the data of MWRI (Microwave Radiation Imager) sensor carried on FY satellite. In this algorithm, direct radiative transfer model is used to model MWRI brightness temperature for four surface types (ice-free ocean, new-formed ice, one-year ice and multi-years ice) and for different atmospheric conditions. Then, sea ice coverage lookup table (0% to 100% in 1% increments) is obtained based on modeled brightness temperature considering different atmospheric conditions. Sea ice coverage is confirmed by comparing observed value with modeled value. Sea ice extent is consistent with the actual situation in most Arctic regions. Although matching errors between channels and positioning errors have been corrected in FY dataset, the received echo signal is relatively weak due to the shorter antenna on MWRI. The weak echo signal makes it difficult to correctly differentiate the boundary between sea ice and near sea shore land, which greatly impact the total accuracy of the dataset and its application. In order to solve this problem, this study introduces a method of optimizing FY Arctic sea ice dataset based on NSIDC (National Snow and Ice Data Center) sea ice product. In NSIDC product, judgment matrix was created covering the entire grid and identifying each pixel as land, shore, near-shore, offshore or ocean as determined by the land/sea mask. Then, these different pixels are corrected in different degrees, respectively. Sea ice extent calculated from NSIDC product is strongly consistent to the actual situation. The accuracy of FY dataset is greatly improved. The analysis results indicated an extremely significantly positive correlation with the NSIDC product (R2 = 0.9997) during June 27th, 2011-December 31st, 2015. The maximum deviation percent of daily, monthly and annually sea ice extent is 3.5%, 1.9% and 0.9%, respectively. Also, the optimization process of FY dataset has no obvious influence on the spatial stratified heterogeneity of the dataset. The optimized FY dataset can correctly reflect Arctic sea ice extent and its variation, especially in coastline regions. It can provide reliable basic data for the study of Arctic sea ice change.

Key words： sea ice dataset; FengYun(FY)-3B; remote sensing; Arctic; spatial stratified heterogeneity

Cite this article

ZHAI Zhaokun , LU Shanlong , WANG Ping , MA Lijuan , LI Duo , REN Yuyu , WU Shengli . Optimization of FY Arctic Sea Ice Dataset based on NSIDC Sea Ice Product[J]. Journal of Geo-information Science, 2017 , 19(2) : 143 -151 . DOI: 10.3724/SP.J.1047.2017.00143

1 引言

北极海冰约占全球海冰面积的30%[1],在全球气候系统中扮演重要角色[2-4]。海冰的反照率高达80%[5],可通过改变海气之间的热量、水汽交换通 量[6]来影响区域乃至全球气候系统。近年来,北极地区持续变暖,且速度远高于全球平均水平,北极年均海冰范围在1978-2012年持续减少,每10年减少3.5%~4.1%,夏季减小最快[7],2007年和2012年夏季北极海冰覆盖面积大幅度减少,在过去1450年以来都是独一无二的[8]。北极海冰的上述变化对区域和全球气候变化影响成为了热点研究内容,尤其是2007年和2012年夏季北极海冰极小值的出现与中纬度地区天气、气候的响应关系引起了国内外很多学者的关注[9-12],从而使人们对反映海冰变化数据集的准确性、时间过程连续性和空间分辨率一致性提出了更高要求。
卫星遥感技术在海冰监测中发挥不可替代的作用[13],多光谱遥感数据空间分辨率相对较高,但受天气影响较大,数据量有限,很难形成质量较高海冰产品;微波遥感具有全天时、全天候的特点[14],利用微波辐射亮度温度数据反演得到海冰覆盖度,进而得到海冰范围和面积是目前全球海冰监测的主要方法[15]。很多学者根据一年冰、多年冰、有强烈地表散射的海冰等不同类型海冰特点以及卫星传感器的特性,设计了多种海冰提取算法并制作了一系列海冰产品。Cavalieri等[16]和Comiso等[17]利用搭载在Nimbus-7上的多通道扫描微波辐射计（SMMR）、DMSP上的专用微波成像仪（SSM/I）和专用微波成像/探测仪（SSMIS）的低空间分辨率频段数据,分别采用NASA Team算法[18]和Bootstrap算法[19],反演得到空间分辨率为25 km的NSIDC全球海冰产品;Spreen等[20]利用分别搭载在Aqua和GCOM-W1上的AMSR-E和AMSR2的高空间分辨率频段数据,采用ARTIST Sea Ice（ASI）算法[20]反演得到空间分辨率为6.25 km的ASI全球海冰产品,但该产品时间过程存在中断情况;Rayner等[21]和Reynolds等[22]综合现有的数字化海冰图表和微波海冰产品,并在误差校正的基础上分别制作了全球海冰数据集HadISST和OISST,但产品的空间分辨率仅为1°。
风云三号B星（FY3B）是中国新一代极轨气象卫星,为中期数值天气预报、全球变化研究和预测、大范围自然灾害和地表生态环境监测提供基础数据。搭载在该星上的微波成像仪（Microwave Radiation Imager, MWRI）为海冰研究增添了新的数据源,杨虎等[23]将MWRI传感器与国际上同类仪器进行对比,该传感器偏差特性与同类微波遥感仪器相当,能够为地球物理参数的反演提供足够高的精 度,且该传感器空间分辨率优于国外同类型传感器（SMMR、SMM/I、SSMIS）。杨虎等利用MWRI数据,采用Enhanced NASA Team算法[24],反演得到了空间分辨率为12.5 km的FY全球海冰产品。该产品虽已进行通道扫描方向偏差修正和定位精度偏差订正,但由于MWRI传感器天线长度有限,造成传感器探测到的地物回波信号相对较弱,且传感器获取的数据地理定位不准,导致已发布的FY海冰数据集海岸线附近海水、海冰、陆地像元存在错分现象,由该产品得到的北极海冰范围整体偏大,与实际海冰分布情况不符,影响了该产品在北极海冰监测业务系统中的应用。
目前时间一致性较好的2套产品分别为哈德莱气候预报中心（Hadley）制作的HadISST海冰数据和美国国家环境预报中心（NCEP）制作的OISST海冰数据,二者对重叠时间段的海冰数据进行偏差调整,取得了非常好的一致性,但2套产品空间分辨率仅为1°,用于分析北极海冰变化对中国气候影响,特别是研究格陵兰海、巴伦支海、喀拉海等关键海区的影响方面,存在较大的局限;MODIS海冰产品利用光学遥感数据得到,空间分辨率最高可达1 km[25],但受云雾影响,难以形成时间过程连续、空间范围全覆盖的海冰产品;而其他高空间分辨率光学传感器数据由于卫星重访周期较长,目前仅应用于局地海域海冰监测,还未大规模应用于北极海冰监测;NSIDC和NCEP海冰产品空间分辨率相对较高[26-27],时间序列也较长,且NSIDC产品在制作过程中,对海岸线附近海水、海冰、陆地像元错分现象进行了修正,使海冰的分布更符合实际情况。为了制作一套时空过程连续且能满足应用精度需求的FY海冰数据集,本文以NSIDC产品为参考数据源,对FY北极海冰数据集进行了优化,得到了空间分布较为准确、空间分辨率较高的北极海冰数据集,为北极海冰变化研究提供了基础数据。

2 研究区和数据源

2.1 研究区概况

研究区为整个北极地区,即北纬66°34'以内的区域,总面积为2100万km2,包括北冰洋以及格陵兰岛、埃尔斯米尔岛、帕里群岛等诸多岛屿和亚、欧、北美大陆北部的部分区域[28],主要海域有格陵兰海、喀拉海、巴伦支海等海域（图1）。
Fig. 1 The study area

图1 研究区域

2.2 数据源及预处理

本文采用的数据源包括NSIDC和FY北极海冰数据集,由于FY海冰数据集是使用Enhanced NASA Team算法反演得到的空间分辨率为12.5 km的产品,研究选用经插值得到的使用NASA Team算法反演得到的空间分辨率为12.5 km的NSIDC海冰产品。其中,NSIDC和FY海冰数据集分别是美国国家冰雪数据中心网站（http://nsidc.org/data/polaris/）和国家卫星气象中心风云卫星遥感数据服务网（http://fy3.satellite.cma.gov.cn/portalsite/default.aspx）公开共享的海冰产品,二者时间跨度均为2011年6月27日至 2015年12月31日。
数据预处理包括格式转换、投影处理和海冰覆盖度范围调整。其中,数据集格式统一转换为Geotiff;投影参数为极方位立体投影（Polar Stereographic Grids）[29-30];海冰覆盖度范围统一调整为0~100,110为极点区域无意义值,120为陆地。
Fig. 2 Simulated and measured brightnesstemperature between land and sea[28]

图2 陆地、海水辐射亮温的模型模拟值和实际测量值[28]

3 海冰数据集优化方法

在微波遥感影像上,陆地区域的实际辐射亮度温度高于海水,且差异较为明显（图2）,但由于传感器自身的原因,实际获取的亮温差异变小,导致海冰产品海岸线附近海水、海冰、陆地像元错分现象,这种情况在夏季尤为严重。NSIDC产品利用判断矩阵对海岸线附近的像元进行识别并对异常情况分别进行不同程度的修正,使海冰分布情况更符合实际情况;FY的MWRI传感器精度略低于SMMR、SMM/I、SSMIS,虽然FY产品虽也进行过类似的处理,但在现有发布的产品中此误差仍然非常明显,通过对比分析同时相的NSIDC和FY产品,发现在北极地区大部分区域,二者海冰分布情况较为一致,但在海陆交界处二者差异较大,主要表现为FY产品部分海水和陆地被错分为海冰,且FY产品北极点中心无意义区域过大,导致FY产品海冰范围偏大,通过处理这两部分误差即可实现FY海冰数据集精度的大幅提高。由于二者均是对北极海冰实际分布情况的反映,且空间分辨率一致,因此,本研究使用NSIDC产品对FY产品进行修正。
Fig. 3 Linear regression analysis between NSIDC and enhanced FY sea ice extent

图3 NSIDC与FY修正后海冰范围散点图

对比分析NSIDC和FY产品空间分布（图3）,本研究对FY产品海水错分为海冰、陆地错分为海冰、海冰错分为陆地、海冰错分为无意义区域、海水错分为无意义区域5种情况。对其分别参照NSIDC产品进行修正,具体方法如下：
Case 1 : D 1 ⊆ A ∈ 0,15 D 2 ⊆ B ∈ 15,100 → D 2 = D 1 （1）
Case 2 : D 1 ⊆ D ∈ 120 D 2 ⊆ B ∈ 15,100 → D 2 = D 1 （2）
Case 3 : D 1 ⊆ B ∈ 15,100 D 2 ⊆ D ∈ 120 → D 2 = D 1 （3）
Case 4 : D 1 ⊆ B ∈ 15,100 D 2 ⊆ C ∈ 110 → D 2 = D 1 （4）
Case 5 : D 1 ⊆ A ∈ 0,15 D 2 ⊆ C ∈ 110 → D 2 = D 1 （5）
式中：D1和D2分别为NSIDC和FY海冰产品;A、B、C、D分别代表海水、海冰覆盖度、无意义值和陆地取值范围。

4 精度评价

本文选取判定系数、最大偏差百分比、空间分异性来评价修正后FY产品的精度。相关系数R（式（6））是衡量2个变量之间线性相关程度的指标,相关系数的平方称为判定系数,判定系数的大小决定了2个变量之间相关的密切程度;偏差百分比（Percentage of Deviation, PD）为修正值与参考值之间偏差百分比（式（7））,其中,CE（Corrected Extent）为修正后FY产品海冰范围,RE（Reference Extent）为NSIDC产品海冰范围;空间分异性,全称空间分层异质性（spatial stratified heterogeneity）,是指某一属性值在不同区域之间存在差异,如气候分带、生态分区、地理区划、以及各种分类现象,可利用地理探测器q-statistic（式（8））来检验不同产品的可分 性[31-32]。式（8）中,总体（population）被划分为h=1,…,L个层（strata）,即L个子区域或L个子总体,N和 σ 2 分别表示总体大小及其方差。
R = ∑ i = 0 n ( x i - x ¯ ) ( y i - y ¯ ) ∑ i = 0 n ( x i - x ¯ ) 2 ∙ ∑ i = 0 n ( y i - y ¯ ) 2 （6）
PD = CE - RE RE ×100%（7）
q = 1 - 1 N σ 2 ∑ h = 1 L N h σ h 2 （8）

4.1 判定系数

对NSIDC产品和FY修正后产品重叠时间段内日平均海冰范围偏差值进行统计,发现二者的偏差较小,且对二者偏差最大的前20组数据进行线性回归分析表明,二者之间的判定系数R2为0.9997（图3）,说明修正后的FY产品与NSIDC产品一致性较好。

4.2 最大偏差百分比

利用式（7）对2套产品进行不同时间尺度偏差分析,结果表明,二者日平均、月平均、年平均最大偏差分别出现在2014年6月30日、2015年7月、2015年,最大偏差百分比分别为3.5%、1.9%、0.9%,修正后FY产品与NSIDC产品具有非常好的一 致性。

4.3 空间分异性

利用式（8）对NSIDC产品和修正前后FY产品进行空间分异性检验,发现q值均为1,说明NSIDC产品和FY产品修正前后均具有较好的空间分异,且FY数据集优化过程对其较好的空间分异特征无明显影响。

5 结果分析

本文选取NSIDC产品与修正前后FY产品在重叠时间段内（2011年6月27日至2015年12月31日）春、夏、秋、冬4个季节同一时相海水、海冰、陆地空间分布进行对比（图4）。研究表明：① 春季,FY产品亚欧大陆海岸线附近存在部分陆地错分为海冰的情况,冰岛海岸线附近此情况较为严重,北大西洋和五大湖地区均存在部分海水错分为海冰的情况（图4（b））,经过修正,错分现象得到明显改善（图4（c））;② 夏季,FY产品在格陵兰岛海岸线附近存在部分陆地错分为海冰的情况,北美大陆海岸线附近、巴伦支海附近及五大湖地区错分情况较为严重（图4（e））,修正后,分布情况趋于正常（图4（f））;③ 秋季,FY产品在亚欧大陆及北美大陆海岸线附近陆地错分为海冰的情况特别严重,北美大陆部分地区存在少量海水错为分海冰的情况（图4（h））,修正后,分布情况趋于正常（图4（i））;④ 冬季,FY产品陆地错分为海冰的现象主要集中在冰岛和北美大陆,巴伦支海附近也存在少量陆地错分为海冰的情况（图4（k））,经过修正,错分现象得到明显改善（图4（l））;且FY产品北极点中心无意义区域过大的现象均得到了修正。
Fig. 4 Spatial distribution maps of NSIDC (a, d, g, j), the original FY product (b, e, h, k) and the enhanced FY product (c, f, i, l)

图4 NSIDC产品（a, d, g, j）与FY产品修正前（b, e, h, k）、修正后（c, f, i, l）空间分布对比图

研究区NSIDC产品与修正前后FY产品在重叠时间段内的日平均海冰范围对比结果表明,修正前FY产品海冰范围整体比NSIDC产品大,且二者差异在2012年夏季达到最大,修正后此现象得到消除;将轨道数据插值到网格数据时发生异常,修正前FY产品海冰范围在2011年9月30日、2012年5月7日、20日及7月5日、6日、7日和2015年5月4日存在异常跳跃现象（图5中圆圈所示）,修正后此现象得到消除;修正前FY产品海冰范围春秋两季与NSIDC产品差异较小,冬夏两季差异较大,且FY产品海冰范围在冬季波动范围较大,修正后海冰范围与NSIDC产品差异较小,变化趋势趋于稳定,海冰范围在2012年夏季减小速度最快（图（5））。
Fig. 5 Comparisons of daily sea ice extent variation between NSIDC and FY product from June 27th, 2011 to December31st, 2015

图5 NSIDC产品、FY产品修正前后2011年6月27日至2015年12月31日日平均海冰范围变化对比图

与日平均海冰范围类似,FY产品月平均海冰范围修正前整体比NSIDC产品大,且在2012年夏季差异最大,修正后FY与NDSIDC产品月平均海冰范围差异较日平均海冰范围更小,同样,月平均海冰范围在2012年夏季减小的速度最快（图6）。修正前FY产品年平均海冰范围整体也呈现增大的情况,修正后二者表现出更好的一致性（图7）。
Fig. 6 Comparisons of monthly sea ice extent variation between NSIDC and FY product from June, 2011 to December, 2015

图6 NSIDC产品、FY产品修正前后2011年6月至2015年12月月平均海冰范围变化对比图

Fig. 7 Comparisons of annual sea ice extent variation between NSIDC and FY product during 2011-2015

图7 NSIDC产品、FY产品修正前后2011-2015年年平均海冰范围变化对比图

图8、9给出了2011-2015年不同季节NSIDC产品和FY修正后产品海冰范围时间序列,其中春季取3-5月的平均,夏季取6-8月的平均、秋季取9-11的平均,冬季取当年12月至次年2月的平均。从图中可看出,修后的FY产品与NSIDC产品虽然在不同季节年际变率存在一定的差异,春冬季节二者差异较大,夏秋季节差异较小,但各个季节2条曲线都有非常一致的年际变率,二者的相关系数的显著性均在99%置信度水平之上,这说明修正后的FY海冰数据集可有效地反映北极地区海冰分布情况。
Fig. 8 Comparisons of sea ice extent variation between NSIDC and FY product in spring andsummer during the period of 2011-2015

图8 2011-2015年春、夏两季NSIDC产品和FY修正后产品海冰范围时间序列

Fig. 9 Comparisons of sea ice extent variation between NSIDC and FY product in autumn andwinter during the period of 2011-2015

图9 2011-2015年秋、冬两季NSIDC产品和FY修正后产品海冰范围时间序列

6 结论与讨论

自2011年FY海冰数据集公开发布以来,还没有针对FY海冰数据集误差像元修正的相关研究,本文通过分析现有的海冰产品,参照NSIDC海冰产品修正了FY海冰产品中因传感器精度问题导致的误差,研究结果表明,修正后FY产品与NSIDC产品的R2为0.9997,二者日平均、月平均、年平均最大偏差分别为3.5%、1.9%、0.9%,且FY数据集优化过程对其较好的空间分异特征无明显影响。
本文算法可实现FY产品优化的自动处理,解决了海岸线附近海水、海冰、陆地错分现象和极点无海冰覆盖区域偏大的情况,优化后的FY产品与NSIDC产品具有较好的一致性;缺点是并未考虑错分像元具体的海冰覆盖度,随着后续卫星传感器姿态的自我修正能力的提高,获取的数据地理定位精度的提升,错分现象可能会得到改善;光学遥感海冰产品虽然覆盖范围有限,但空间分辨率相对较高,如何有效利用光学遥感产品对FY产品优化进行有效补充是后续研究工作的重点。
FY北极海冰数据集的精度的提高,为探索北极海冰变化趋势与全球气候变化的内在联系提供了精度更高的基础数据,尤其在揭示北极海冰对中国气候的影响机制,提取海冰影响的关键区方面具有重要的应用价值。该产品的广泛使用不仅有助于推动国产被动微波传感器的改进与发展,而且对推动国产传感器数据在全球海冰监测中的应用也具有重要意义。
本文的优化算法只针对2011-2015年的FY产品,如何制作一套以FY产品为基础,综合1978-2011年的遥感海冰产品,为北极地区长时间序列海冰变化分析研究提供基础数据,是气象部门关注的重点,也是下一步的研究工作。

The authors have declared that no competing interests exist.

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[31]
Wang J F, Li X H, Christakos G, et al.Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China[J]. International Journal of Geographical Information Science, 2010,24(1):107-127.Physical environment, man-made pollution, nutrition and their mutual interactions can be major causes of human diseases. These disease determinants have distinct spatial distributions across geographical units, so that their adequate study involves the investigation of the associated geographical strata. We propose four geographical detectors based on spatial variation analysis of the geographical strata to assess the environmental risks of health: the risk detector indicates where the risk areas are; the factor detector identifies factors that are responsible for the risk; the ecological detector discloses relative importance between the factors; and the interaction detector reveals whether the risk factors interact or lead to disease independently. In a real-world study, the primary physical environment (watershed, lithozone and soil) was found to strongly control the neural tube defects (NTD) occurrences in the Heshun region (China). Basic nutrition (food) was found to be more important than man-made pollution (chemical fertilizer) in the control of the spatial NTD pattern. Ancient materials released from geological faults and subsequently spread along slopes dramatically increase the NTD risk. These findings constitute valuable input to disease intervention strategies in the region of interest.

DOI

[32]
Wang J F, Zhang T L, Fu B J.A measure of spatial stratified heterogeneity[J]. Ecological Indicators, 2016,67:250-256.Spatial stratified heterogeneity, referring to the within-strata variance less than the between strata-variance, is ubiquitous in ecological phenomena, such as ecological zones and many ecological variables. Spatial stratified heterogeneity reflects the essence of nature, implies potential distinct mechanisms by strata, suggests possible determinants of the observed process, allows the representativeness of observations of the earth, and enforces the applicability of statistical inferences. In this paper, we propose a q -statistic method to measure the degree of spatial stratified heterogeneity and to test its significance. The q value is within [0,1] (0 if a spatial stratification of heterogeneity is not significant, and 1 if there is a perfect spatial stratification of heterogeneity). The exact probability density function is derived. The q -statistic is illustrated by two examples, wherein we assess the spatial stratified heterogeneities of a hand map and the distribution of the annual NDVI in China.

DOI

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