王泉斌,秦 平,趙曉晨

(1.國(guó)家海洋局第一海洋研究所,山東青島266061; 2.中國(guó)海洋大學(xué)信息科學(xué)與工程學(xué)院,山東青島266100;3.青島科技大學(xué)海洋科學(xué)與生物工程學(xué)院,山東青島266042)

世界首顆靜止軌道海洋水色衛(wèi)星應(yīng)用研究進(jìn)展

王泉斌1,秦 平2,趙曉晨3

(1.國(guó)家海洋局第一海洋研究所,山東青島266061; 2.中國(guó)海洋大學(xué)信息科學(xué)與工程學(xué)院,山東青島266100;3.青島科技大學(xué)海洋科學(xué)與生物工程學(xué)院,山東青島266042)

海洋環(huán)境參數(shù)和赤潮、綠潮等海洋災(zāi)害在一天之內(nèi)會(huì)有明顯的變化,需要高頻率的觀測(cè)才能滿足監(jiān)測(cè)的需求,極軌水色衛(wèi)星觀測(cè)頻率低,而靜止軌道水色衛(wèi)星在觀測(cè)頻率方面具有絕對(duì)優(yōu)勢(shì)。2010年韓國(guó)發(fā)射了世界上第一顆靜止軌道海洋水色衛(wèi)星GOCI(Geostationary Ocean Color Imager),使小時(shí)級(jí)時(shí)間分辨率的水色遙感成為現(xiàn)實(shí),各國(guó)科學(xué)家圍繞該數(shù)據(jù)迅速開展了大量研究工作。本文首先介紹了GOCI遙感器的主要參數(shù)信息及其數(shù)據(jù)處理軟件,然后綜述了GOCI自問世至2016年的主要研究進(jìn)展,涉及衛(wèi)星數(shù)據(jù)處理、產(chǎn)品質(zhì)量評(píng)價(jià)、海洋環(huán)境探測(cè)、海洋災(zāi)害監(jiān)測(cè)、海洋動(dòng)力過程探測(cè)、大氣探測(cè)等方面,以期對(duì)我國(guó)水色遙感特別是靜止軌道水色遙感應(yīng)用研究提供參考。

GOCI;靜止軌道;遙感應(yīng)用;水色遙感

2010-06韓國(guó)發(fā)射了世界上第一顆靜止軌道水色衛(wèi)星COMS(Communication Ocean and Meteorological Satellite),其上搭載了水色成像儀GOCI(Geostationary Ocean Color Imager),不同于傳統(tǒng)極軌衛(wèi)星1 d只能過境1景次,GOCI每天可獲取時(shí)間間隔為1 h的8景影像,使小時(shí)級(jí)時(shí)間分辨率水色遙感成為現(xiàn)實(shí)[1-7],使得海洋環(huán)境、海洋災(zāi)害的逐時(shí)變化監(jiān)測(cè)成為可能。圍繞靜止軌道衛(wèi)星水色遙感,各國(guó)科學(xué)家都表現(xiàn)出了濃厚的興趣,研究雖剛起步不久,但發(fā)展較快。我國(guó)科學(xué)家也在跟蹤GOCI應(yīng)用研究的步伐。

本文對(duì)GOCI問世幾年來的相關(guān)研究進(jìn)展進(jìn)行回顧和評(píng)述,內(nèi)容涉及衛(wèi)星數(shù)據(jù)處理、產(chǎn)品質(zhì)量評(píng)價(jià)、海洋環(huán)境探測(cè)、海洋災(zāi)害監(jiān)測(cè)、大氣探測(cè)等方面。其中衛(wèi)星數(shù)據(jù)處理的研究進(jìn)展主要集中在大氣校正算法研究;海洋生態(tài)環(huán)境探測(cè)的研究進(jìn)展主要集中在海洋葉綠素濃度、懸浮物濃度等生態(tài)環(huán)境參數(shù)反演方法與應(yīng)用方面;海洋災(zāi)害監(jiān)測(cè)的研究進(jìn)展主要集中在海冰、綠潮等黃、渤海常見的環(huán)境災(zāi)害方面。通過對(duì)GOCI相關(guān)研究現(xiàn)狀的分析,希望能對(duì)我國(guó)水色遙感特別是靜止軌道水色遙感應(yīng)用研究提供參考。

1 GOCI及其數(shù)據(jù)處理軟件

GOCI是世界上第一個(gè)靜止軌道海洋水色傳感器,搭載在COMS衛(wèi)星上于2010-06發(fā)射,傳感器設(shè)計(jì)壽命為7 a,觀測(cè)范圍為2 500 km×2 500 km,觀測(cè)中心經(jīng)緯度為(130°E,36°N),可覆蓋我國(guó)渤海、黃海和東海部分海域。GOCI的軌道相關(guān)參數(shù)如表1所示,根據(jù)海洋水色的觀測(cè)目的,GOCI共設(shè)置了8個(gè)波段,其中6個(gè)為可見光波段,2個(gè)為近紅外波段,波段寬度10～40 nm,波段設(shè)置及對(duì)應(yīng)的用途見表2。韓國(guó)海洋衛(wèi)星中心(KOSC)負(fù)責(zé)數(shù)據(jù)的發(fā)布。

(王 燕 編輯)

表1 GOCI相關(guān)參數(shù)及與SeaWiFS的對(duì)比Table 1 Comparison of the parameters between the GOCI and the SeaWiFS

表2 GOCI的波段設(shè)置Table 2 GOCI band settings

KOSC專門開發(fā)用于GOCI數(shù)據(jù)處理的系統(tǒng)軟件GDPS(GOCI Data Processing System)(圖1),該軟件包括數(shù)據(jù)瀏覽、數(shù)據(jù)處理、數(shù)據(jù)分析和數(shù)據(jù)輸出等模塊。其中,數(shù)據(jù)處理模塊包含了大氣校正算法和常用的水色遙感產(chǎn)品反演算法,輸入GOCI L1B數(shù)據(jù),可生成瑞利校正反射率(Rrc)、離水輻亮度(Lw)、歸一化離水輻亮度(n Lw)、遙感反射率(Rrs)、葉綠素濃度、可溶性黃色有機(jī)質(zhì)(CDOM)和懸浮物濃度(SPM)等L2級(jí)海洋分析數(shù)據(jù)產(chǎn)品。

圖1 GDPS軟件界面Fig.1 Interface of GDPS software

2 GOCI研究進(jìn)展

作為世界上第一顆靜止軌道水色傳感器,GOCI的發(fā)射升空引起了國(guó)內(nèi)外眾多海洋水色研究者的關(guān)注,目前已有大量基于GOCI數(shù)據(jù)開展的研究工作。作者對(duì)2011—2016年國(guó)內(nèi)外主要期刊上發(fā)表的與GOCI數(shù)據(jù)相關(guān)的研究論文進(jìn)行了統(tǒng)計(jì)和分析,結(jié)果如圖2所示。針對(duì)GOCI的研究?jī)?nèi)容主要集中在GOCI數(shù)據(jù)處理、GOCI數(shù)據(jù)質(zhì)量評(píng)價(jià)、海洋生態(tài)環(huán)境探測(cè)、海洋災(zāi)害探測(cè)、海洋動(dòng)力過程探測(cè)及大氣探測(cè)等方面,其中GOCI數(shù)據(jù)在海洋生態(tài)環(huán)境監(jiān)測(cè)方面的應(yīng)用最為廣泛。

圖2 GOCI相關(guān)研究統(tǒng)計(jì)Fig.2 Statistics of the researches related to the GOCI

2.1 GOCI數(shù)據(jù)處理方法

GOCI數(shù)據(jù)處理方法的研究主要集中在大氣校正方面[8-11],Ahn等[8]基于SeaWiFS的標(biāo)準(zhǔn)大氣校正算法,利用一個(gè)紅光和近紅外波段水體反射率的區(qū)域性經(jīng)驗(yàn)關(guān)系,發(fā)展了GOCI數(shù)據(jù)的大氣校正算法,該算法集成到了GDPS 1.1版本中,利用該算法進(jìn)行大氣校正后,GOCI的離水輻亮度與實(shí)測(cè)數(shù)據(jù)的比值接近于1,證明了該算法的有效性。Wang等[9]提出了一種利用近紅外波段的GOCI渾濁水體大氣校正算法,利用該方法對(duì)GOCI數(shù)據(jù)進(jìn)行大氣校正后,與MODIS衛(wèi)星的觀測(cè)數(shù)據(jù)具有很好的一致性。Kim等[11]針對(duì)GOCI L1級(jí)數(shù)據(jù)存在輻射偏差的問題,發(fā)展了一種雜散光校正方法,輻射偏差降至≤5%。

2.2 GOCI數(shù)據(jù)產(chǎn)品質(zhì)量評(píng)價(jià)

數(shù)據(jù)質(zhì)量的優(yōu)劣勢(shì)是水色衛(wèi)星數(shù)據(jù)能否在海洋探測(cè)中發(fā)揮重要作用的關(guān)鍵[12-22]。Oh等[18-19]、Lee等[20]、Cho等[21]開展了GOCI在軌性能模擬、在軌光學(xué)性能評(píng)估和在軌性能變化分析等方面的研究。Moon等[14]利用船基觀測(cè)數(shù)據(jù)評(píng)估了GOCI遙感反射率、葉綠素濃度、黃色物質(zhì)和懸浮物等產(chǎn)品的精度,結(jié)果表明,GOCI可見光波段遙感反射率的平均相對(duì)偏差為18%～33%;葉綠素濃度產(chǎn)品與實(shí)測(cè)數(shù)據(jù)的相關(guān)性較低(＜0.41),平均相對(duì)偏差為35%;黃色物質(zhì)產(chǎn)品與實(shí)測(cè)數(shù)據(jù)沒有顯著的相關(guān)性;懸浮物產(chǎn)品與實(shí)測(cè)數(shù)據(jù)的相關(guān)性優(yōu)于0.73。Lamquin等[16]基于MODIS,MERIS和實(shí)測(cè)數(shù)據(jù)評(píng)估GOCI輻亮度產(chǎn)品,結(jié)果表明,GOCI與MODIS,MERIS和實(shí)測(cè)數(shù)據(jù)具有較好的一致性,但GOCI的大氣校正算法會(huì)將大量的渾濁水體掩膜掉,需要后續(xù)研究中進(jìn)一步改進(jìn)。Xiao等[22]基于2012—2013年的實(shí)測(cè)氣溶膠光學(xué)厚度(AOD)數(shù)據(jù)評(píng)估了VIIRS,GOCI和MODIS的氣溶膠產(chǎn)品,結(jié)果表明,與其他傳感器相比,GOCI的氣溶膠產(chǎn)品精度有待提高。

2.3 海洋生態(tài)環(huán)境探測(cè)

海洋生態(tài)環(huán)境參數(shù)的反演是水色衛(wèi)星的重要應(yīng)用之一,研究者利用GOCI數(shù)據(jù)在葉綠素濃度、懸浮物濃度、光合有效輻射等生態(tài)環(huán)境參數(shù)反演方面開展了大量的研究工作[23-39]?；贕OCI可逐時(shí)獲取影像的優(yōu)勢(shì),Choi等[26,29]、Ryu等[25]和劉猛等[24]分析了近岸懸浮泥沙的日變化特征。Qiu等[30]提出一種針對(duì)GOCI數(shù)據(jù)的黃河口懸浮泥沙濃度反演算法,該算法的平均相對(duì)偏差為34.2%;He等[31]利用GOCI數(shù)據(jù)發(fā)展了杭州灣水體懸浮泥沙反演算法,并利用反演結(jié)果分析了杭州灣懸浮泥沙的日變化特征。Qiu等[35]針對(duì)GOCI數(shù)據(jù)提出了一種高渾濁水體的濁度反演算法,利用該算法反演了2014-12-30浙江近岸海域8個(gè)時(shí)刻濁度,發(fā)現(xiàn)濁度從近岸到遠(yuǎn)岸,從上午到下午逐漸降低。金惠淑等[36]利用波段比值法建立了基于GOCI遙感影像的葉綠素a質(zhì)量濃度反演模型,以此探討利用GOCI數(shù)據(jù)估算湖泊水體富營(yíng)養(yǎng)化程度的可能性,研究結(jié)果表明GOCI遙感數(shù)據(jù)具有對(duì)湖泊富營(yíng)養(yǎng)化程度進(jìn)行監(jiān)測(cè)的潛力。Kim等[37]首先按照SPM濃度將水體分為三類,然后評(píng)估了波段比、熒光基線高度等葉綠素反演算法對(duì)GOCI的適用性,發(fā)現(xiàn)GOCI葉綠素濃度反演結(jié)果的平均偏差約為35%。Hwang等[38]基于長(zhǎng)時(shí)間序列的GOCI數(shù)據(jù)分析了漢江口海域的懸浮物濃度變化。Kim等[39]基于GOCI數(shù)據(jù)估算了光合有效輻射(PAR),并基于實(shí)測(cè)數(shù)據(jù)進(jìn)行了檢驗(yàn),同時(shí)與MODIS結(jié)果進(jìn)行了對(duì)比,結(jié)果表明GOCI一天可獲取8景數(shù)據(jù)的優(yōu)勢(shì)能有效評(píng)估PAR的日變化。

2.4 海洋災(zāi)害探測(cè)

在海洋災(zāi)害探測(cè)方面,國(guó)內(nèi)外研究者利用GOCI數(shù)據(jù)開展了赤潮、綠潮等災(zāi)害的探測(cè)研究[40-48]。Son等[40]發(fā)展了針對(duì)GOCI數(shù)據(jù)的綠潮探測(cè)指數(shù)IGAG,該指數(shù)利用了555,660和745 nm三個(gè)波段,與NDVI, EVI和KOSC等綠潮探測(cè)常用方法相比,IGAG指數(shù)能否增強(qiáng)低密度綠潮在圖像上的信號(hào)強(qiáng)度。Lou等[41]基于GOCI數(shù)據(jù),采用修正的赤潮指數(shù)RI,分析了東中國(guó)海赤潮的日變化,結(jié)果表明,赤潮在14:30時(shí)面積達(dá)到最大。Hong等[42]利用GOCI和MODIS數(shù)據(jù),對(duì)黃海入海高營(yíng)養(yǎng)污水進(jìn)行了監(jiān)測(cè)。Young等[43]將衛(wèi)星數(shù)據(jù)與數(shù)值模擬結(jié)合,分析了黃海和東海海面漂浮綠潮的漂移路徑,發(fā)現(xiàn)漂浮綠潮的漂移由海流和風(fēng)控制。Gong等[44]利用GOCI數(shù)據(jù)提取了黃海和渤海海冰的漂移方向和速度。Bak等[45]基于GOCI數(shù)據(jù)發(fā)展了一種赤潮監(jiān)測(cè)算法,并與傳統(tǒng)的赤潮監(jiān)測(cè)算法進(jìn)行了對(duì)比,可有效避免誤檢情況。劉文宋等[46]發(fā)展了一種基于GOCI數(shù)據(jù)的海冰厚度監(jiān)測(cè)算法,并基于實(shí)測(cè)進(jìn)行了檢驗(yàn),RMS為6.82 cm。

2.5 海洋動(dòng)力過程探測(cè)

利用GOCI的逐時(shí)數(shù)據(jù)可進(jìn)行海冰漂移速度、海表流場(chǎng)等的探測(cè)[49-57]。Lang等[49]利用GOCI逐時(shí)產(chǎn)品估算了渤海海冰的漂移速度,并分析了海冰漂移的影響因素及各因素的貢獻(xiàn)率。Lou等[50]基于GOCI數(shù)據(jù)的逐時(shí)特性,利用MCC法估算了東中國(guó)海的海表流場(chǎng),通過分析發(fā)現(xiàn)流速的短時(shí)間變化主要是受潮汐的影響。海洋中的中尺度渦能夠引起海表層葉綠素濃度的變化,利用GOCI的葉綠素濃度產(chǎn)品可觀測(cè)海洋的中尺度渦等海洋動(dòng)力過程。Park等[51]利用GOCI的葉綠素濃度產(chǎn)品探測(cè)到了日本海的中尺度渦;Lim等[52]利用GOCI觀測(cè)到了反氣旋暖渦引起葉綠素濃度升高現(xiàn)象。此外,Park等[53-54]還利用GOCI逐時(shí)數(shù)據(jù)發(fā)展了潮汐訂正模型。Hu等[55]基于GOCI數(shù)據(jù),利用MCC方法計(jì)算了杭州灣海域的表面流場(chǎng),同時(shí)評(píng)估了杭州灣海域的懸浮泥沙的快速沉降和再懸浮過程。Warren等[56]也利用GOCI和MCC方法數(shù)據(jù)開展了海表流場(chǎng)的分析工作。吳頡等[57]基于GOCI的總懸浮體數(shù)據(jù)分析了長(zhǎng)江口海域表層懸浮體鋒面的變化特征,并對(duì)其機(jī)制進(jìn)行了初步分析。

2.6 大氣探測(cè)

Lee等[58]利用GOCI數(shù)據(jù)生成了空間分辨率為500 m的海上氣溶膠光學(xué)厚度產(chǎn)品,與地面觀測(cè)數(shù)據(jù)的相關(guān)性為0.99。Park等[59]將GOCI逐時(shí)氣溶膠產(chǎn)品與模式模擬相結(jié)合,用于監(jiān)測(cè)PM運(yùn)移事件的次數(shù),證明了GOCI氣溶膠產(chǎn)品在PM監(jiān)測(cè)中的應(yīng)用潛力。Choi等[60]利用GOCI數(shù)據(jù)反演了東亞地區(qū)陸地和海洋的氣溶膠光學(xué)厚度(AOD)、細(xì)模態(tài)比(FMF)、單次散射比(SSA)等氣溶膠光學(xué)性質(zhì)。Yuan等[61]基于GOCI數(shù)據(jù)發(fā)展了一種海霧探測(cè)算法,并分析了海霧的逐時(shí)變化特征。

3 結(jié) 語

GOCI問世6 a間,在數(shù)據(jù)處理方法、數(shù)據(jù)產(chǎn)品精度評(píng)價(jià)等方面已開展相關(guān)研究工作,GOCI數(shù)據(jù)在海洋生態(tài)環(huán)境監(jiān)測(cè)、海洋災(zāi)害監(jiān)測(cè)、海洋動(dòng)力過程探測(cè)和大氣探測(cè)等方面,特別是逐時(shí)變化監(jiān)測(cè)方面顯示了其獨(dú)特優(yōu)勢(shì)。

與此同時(shí),值得注意的是,我國(guó)科學(xué)家在上述相關(guān)領(lǐng)域的工作還比較少,作為水色遙感領(lǐng)域的新興方向,希望能引起我國(guó)科學(xué)家的充分重視,迎頭趕上。

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Advances in the Application Study of the First Geostationary Ocean Color Imager

WANG Quan-bin1,QIN Ping2,ZHAO Xiao-chen3
(1.The First Institute of Oceanography,SOA,Qingdao 266061,China; 2.College of Information Science&Engineering,Ocean University of China,Qingdao 266100,China; 3.College of Marine Science and Biological Engineering,Qingdao University of Science&Technology,Qingdao 266042,China)

Marine environmental parameters and marine disasters such as red tide and green tide can change obviously within a day.Therefore,high frequency observations are required to meet the monitoring needs. The polar orbiting ocean color satellites are relatively lower in observation frequency,whereas the geostationary orbiting ocean color satellites have an absolute advantage in the observation frequency.The first geostationary orbiting ocean color satellite in the world,i.e.the Geostationary Ocean Color Imager(GOCI) was launched by South Korea in 2010,which has made the ocean color monitoring realized an hour level time resolution.Scientists around the world have quickly carried out a large amount research work by using the GOCI data.In the present paper,the major parameters and the data processing software of the GOCI remote sensor are introduced,and then the main advances in the researches of GOCI from its advent to 2016,which involve satellite data processing,product quality evaluation,marine environment and disaster monitoring,marine dynamic process detection,atmospheric sounding,and so on,are reviewed in order to provide references for the application of ocean color remote sensing in China,especially for the application of the geostationary ocean color remote sensing.

GOCI;geostationary;remote sensing application;ocean color remote sensing

December 12,2016

P715.7

A

1002-3682(2017)02-0071-08

10.3969/j.issn.1002-3682.2017.02.009

2016-12-12

中央級(jí)公益性科研院所基本科研業(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目——中韓海洋功能區(qū)選劃政策及技術(shù)方法比較研究(2015G19)

王泉斌(1980-),男,工程師,碩士,主要從事海洋環(huán)境與資源管理、中韓海洋領(lǐng)域合作等方面研究.E-mail:wangquanbin@fio. org.cn