海洋水色卫星数据在南海海域的应用

海洋水色卫星作为一种光学传感器,可以实现对南海海域的海温、叶绿素ａ浓度等多个海洋环境参数的监测,并且在此基础上实现对初级生产力、海气界面二氧化碳通量等参量的测量。南海海域作为台风多发海域,海洋环境变化受台风影响较大。文章通过比较台风过境前、过境期间及过境后的海温、叶绿素ａ浓度及初级生产力的变化,结果表明,台风过境期间,海温下降,叶绿素ａ浓度和初级生产力由于海水的垂直泵吸作用而上升,这一增加有利于海洋生物的生长,从而有利于改善生态环境。     

南海叶绿素a浓度垂直分布的统计估算

分析整理了1993—2006年近10 a南海北部海域、南沙海域和南海其他海域的叶绿素a浓度历史航次调查资料,基于前人提出的全球叶绿素浓度垂直分布的统计分析模式,根据南海表层叶绿素a浓度大小的不同分级,对南海叶绿素a浓度进行了参数化处理,拟合估算了南海各水层剖面的叶绿素a浓度分布值,并结合不同海区的环境特征,分析了南海叶绿素a浓度垂直分布与其海水物理环境的关系。初步分析结果表明,叶绿素a浓度随深度垂直变化的拟合曲线呈一定倾斜的正态分布特征,当表层叶绿素a浓度较低时,作为南海深水海盆区的代表,拟合值更接近实测平均值的分布,叶绿素a浓度高值集中在次表层剖面上;当表层叶绿素a浓度较高时,作为近岸区和河口区的代表,高值多集中在表层海水,拟合误差偏大。该统计估算模式对于揭示南海叶绿素a浓度垂直分布结构进行了有益的尝试,为发展适合不同海区特点的模式以及校正参数奠定了基础。利用该模式与海洋水色卫星遥感数据有效结合,将对南海叶绿素a浓度时空分布格局的研究具有重要的意义。     

基于实测数据的北海区海水表层叶绿素a浓度遥感定量反演

为提高我国海洋水色遥感技术和海水环境监测水平,文章根据北海区海水遥感现场监测数据,基于经验算法和荧光基线高度法的回归分析,开展海水表层叶绿素a浓度的遥感定量反演,并选取北黄海近岸海域样本数据进行算法检验。研究结果表明:辽东湾等9个北海区典型海域具有相同或相似海水表层光学特性,适宜建立海水表层叶绿素a浓度遥感定量反演模型;典型海域海水表层叶绿素a浓度与遥感反射率之间的相关关系较强,模型均为简单波段比值模型;二类海水研究区域海水表层叶绿素a浓度与荧光基线高度之间的相关关系不明显;北黄海近岸海域海水表层叶绿素a浓度的最优模型遥感定量反演值的相对误差的平均值为0.669μg/L。     

大太阳天顶角下水色卫星叶绿素遥感探测能力研究

本文利用考虑地球曲率的矢量辐射传输模型PCOART-SA，对大太阳天顶角下叶绿素浓度的卫星遥感探测极限能力进行了模拟研究。结果表明:太阳-传感器几何参数，尤其是太阳天顶角对叶绿素浓度变化的探测极限能力影响较大；大太阳天顶角下，卫星对叶绿素浓度变化的探测能力下降十几倍。在典型陆架水体（叶绿素浓度为1 μg/L），低太阳天顶角（30°）时，叶绿素浓度变化探测极限为0.012 8 μg/L（约为原浓度的1.2%），而大太阳天顶角（80°）时，探测极限为0.136 μg/L（约为原浓度的13.6%）。相比于太阳天顶角，观测天顶角增大造成的叶绿素浓度探测能力衰减较小。叶绿素浓度越高，吸收作用越强，对卫星遥感器的辐射探测灵敏度、定标及大气校正精度的要求越高。     

基于遥感反演的莱州湾水色变化的多时相分析

悬浮泥沙和叶绿素是海洋水色的重要部分,是反映河口海岸地区生态环境状况的重要指标。本文基于Landsat TM/ETM+/OLI遥感影像,在不依赖地面实测数据的条件下,结合水文气象数据,利用光谱信息建立水色遥感模型对莱州湾1996—2015年不同时期的悬浮泥沙和叶绿素变化进行研究。研究结果表明:(1)此模型可以快速反演出较大空间尺度内的水色时空分布情况。(2)1996—2015年这一时期内悬浮泥沙浓度变化明显,枯水期的悬浮泥沙扩散范围总体大于丰水期,悬浮泥沙高浓度区主要分布在黄河口附近海域和沿岸区域,泥沙主要来源于陆源输沙和海水中的泥沙再悬浮,悬浮泥沙的扩散主要受潮流的影响,风和波浪等动力因素也在一定程度上影响着悬浮泥沙的扩散;(3)此外,莱州湾叶绿素高浓度区主要分布在莱州湾东—南部海域,其分布具有明显的季节性,春季(5月)海水温度升高,水中营养物质垂直混合好使得叶绿素浓度处于较高态势。     

南海西北部夏季叶绿素a浓度的分布特征及其对海洋环境的响应

南海生态动力学过程复杂,尤其是夏季,在东南季风的影响下,南海西部的上升流、西北部东北向的离岸流对该海区乃至整个南海生态动力学过程都有重要的影响。根据1999—2003年的SeaW-iFS卫星遥感叶绿素a浓度数据,结合2004年在南海北部海洋观测航次实测的叶绿素a浓度数据,分析了南海西北部夏季叶绿素a的分布特征;同时根据海表温度、风场、海面高度等卫星遥感历史资料,探讨了叶绿素a浓度的分布及其对环境因子的响应。结果表明,南海西北部夏季(6—8月)叶绿素a浓度的分布有显著的空间变化:在西部半径达500km低温、强风的半圆形海域范围叶绿素a浓度较高(>0.15mg.m-3),其中位于越南金兰湾东北部有一叶绿素a浓度更高的激流形带(>0.2mg.m-3);而在南海东北部夏季(6—8月)叶绿素a浓度明显偏低(<0.12mg.m-3)。叶绿素a的这种空间分布特征同季风等海洋环境因素之间有密切的关系。对比实测叶绿素a浓度显示,遥感叶绿素a浓度同实测叶绿素a浓度有很好的一致性。     

南海北部秋季营养盐、溶解氧、pH值和叶绿素a分布特征及相互关系

通过2004年9月至10月对南海北部水域的现场调查,分析了表层海水中溶解氧、叶绿素a、pH值和营养盐等水质因子的空间分布分布特征,并讨论了它们之间的相互关系。结果表明:在南海北部海区的表层海水中,各水质因子在空间分布上大多呈现块状分布,且东西两侧的海水有较为明显的差异;海水中的溶解氧、pH值均表现出与海水温度相反的分布趋势;海水中的叶绿素a(Chla)和众多的水质因子表现出多元相关性,说明水体中浮游植物的生长繁殖是众多水质因子在南海北部综合作用的结果,而Chla和水体中亚硝酸盐的高相关性,说明南海北部水体中浮游植物的生长和亚硝酸盐有着比其他营养盐因子更为密切的联系。     

南海叶绿素浓度的时空变化特征分析

运用经验正交函数(EOF)分解方法,分析了 SeaWiFS传感器获取的近13年的逐月叶绿素浓度资料,得出南海叶绿素浓度的空间分布形态及其随时间的变化特征.结果显示,南海叶绿素浓度在空间上主要表现为4种典型的分布结构,而时间上以季节变化为主:EOF1呈现了南海叶绿素浓度近海高、海盆区低的基本分布特征;EOF2显示出夏季越南沿岸激流形叶绿素浓度高值带的存在,除显著的季节变化外,其时间序列也表现出明显的年际变化特征,并与ENSO事件关系紧密;EOF3体现了南海叶绿素浓度随东北季风加强而升高的现象,其高值区分布于东北-西南向的海盆主轴以北,并在吕宋岛西北海域形成一个极大值中心;另外, EOF4反映了叶绿素浓度较短时间尺度的变化规律,在空间分布上表现为明显的三涡结构,与南海海面高度的三涡结构有极好的对应关系.     

南海叶绿素浓度季节变化及空间分布特征研究

以南海海域1997年10月至2002年9月SeaWiFS卫星遥感叶绿素浓度的资料为基础,分析了多年平均的南海叶绿素浓度的时空分布,初步分析结果表明,冬季南海大部分海域叶绿素浓度普遍较高,春季大部分海域较低;南海各个海区的叶绿素月平均最低浓度基本出现在春季的4月或5月,而最高浓度出现的月份却有不同的特征,在中央海盆区出现在12月,在广东沿岸海区出现在7月,在越南东南部近岸海域在8月和12月有两个最高值;在吕宋海峡的西部区域,尽管叶绿素浓度的最高值也出现在12月,但是叶绿素浓度的最低值却出现在夏季的7月.在空间上近岸区域的叶绿素浓度明显高于中央海盆区,西部海域普遍高于东部海域.南海叶绿素浓度的这一时空分布特征与流场(如上升流等)、海面温度场和风场等的变化有关,也与陆源物质的输入等关系密切.     

南海北部水体叶绿素a浓度反演的生物光学模型

利用2003年至2005年秋季在南海多个航次的现场观测数据,研究了南海北部海区遥感反射率的变化,并分析了用于全球海洋叶绿素a浓度反演的OC2和OC4模型在本海区的适用性。结果表明,在南海北部海域,OC2和OC4模型高估了叶绿素a浓度,高估范围一般约在80%—200%之间,其中最高可达640%,即OC2和OC4模型并不适用于南海海域。在此基础上,根据现场实测的表观光学数据,利用遥感反射率比值(Rrs(433)/Rrs(555))与叶绿素a浓度的关系建立了两套能够精确反演南海北部海域叶绿素a浓度的本地化经验算法———算法1和算法2,并利用其对南海北部海域的叶绿素a浓度进行反演。结果表明,由本地化模型反演得到的叶绿素a浓度与实测的叶绿素a浓度具有较好的相关关系,其平均相对偏差分别为51%和53%,相关系数为0.75。     

SeaWiFS和MODIS叶绿素浓度数据及其融合数据的全球可利用率

对2001年Sea WiFS和MODIS叶绿素浓度数据的全球可利用率进行了定量分析，二者在全球范围的变化趋势一致，年平均可利用率分别为12．4％和13．6％，其中MODIS叶绿素浓度的可利用率略高于SeaWiFS。利用小波变换方法对二者进行数据融合，经分析：SeaWiFS／MODIS叶绿素浓度融合数据相对于单一传感器数据提高了全球可利用率，其年平均为20．50％；融合数据保持了较高空间分辨率MODIS数据的海洋特征；融合数据与实测值比较，差值的均值和标准偏差分别为0．16mg／m^3和1．07mg／m0（SeaWiFS：0．46mg／m^3和2．22mg／m^3，MODIS：0．13mg／m^3和0．82mg／m^3）。与MODIS和Sea WiFS相比。融合数据接近MODIS优于SeaWiFS。结果表明小波变换方法用于SeaWiFS和MODIS叶绿素浓度数据融合的有效性。     

河口悬浮泥沙浓度Sea WiFS遥感定量模式研究

利用海洋水色卫星 Seastar/SeaWiFS数据和准同步实测表层含沙量资料,建立长江口区悬浮泥沙遥感定量模式.通过“exact closed-form”算法对多时相SeaWiFS数据进行几何定位,并利用大气辐射传输简化理论对其进行大气校正,求得各波段遥感反射率;在完成投影变换和几何精校正之后,建立遥感参数与含沙量相关关系,得到长江口区悬浮泥沙遥感定量模式.     

Estimation of chlorophyll-a concentration in the Zhujiang Estuary from SeaWiFS data

The chlorophyll-a concentration is generally overestimated for the southern China coastalwaters if the default algorithm of the SeaDAS is employed. An algorithm is developed for retrieval of chlorophyll-a concentration in the Zhujiang Estuary, Guangdong Province, China, by using simulated reflectance data. The simulated reflectance is calculated corresponding to the SeaWiFS wavelength bands, via a general model by inputting measured water components, i.e. , the suspended sediment, chlorophyll-a, and yellow substance (DOC) concentration data of 130 samples. Empirical relationships of the chlorophyll-a concentration to 240 different band combinations are investigated based on the simulated reflectance data, and the band combination, R_5R_6/R_3R_4, is found to be the optimum one for the development of an algorithm valid for the Zhujiang Estuary. This algorithm is then employed to determine the chlorophyll-a concentration from SeaWiFS data. The estimated concentrations have a better accuracy than those obtai     

Comparative Analysis of Chlorophyll-a Distribution from SeaWiFS,MODIS-Aqua,MODIS-Terra and MERIS in the Arabian Sea

The variability of Chlorophyll-a (Chl-a) distribution derived from MODIS (on Aqua and Terra platforms) and MERIS sensors have been compared with SeaWiFS data in the Arabian Sea. MODIS Aqua has overestimated the SeaWiFS Chl-a within 25–32% in the coastal turbid (eutrophic) waters and underestimated in open ocean waters with error within 20%. However, there is no significant bias (?0.1 on log-scale) observed as the slope is well within 0.97-1.1 (log transformed). MODIS-Terra has underestimated the Chl-a concentration in open ocean waters by about 29–31%, which is higher than MODIS-Aqua. MODIS-Terra is observed to be more accurate than MODIS-Aqua in the coastal waters. MERIS is overestimating the SeaWiFS Chl-a with log RMS error of ～0.15 and log bias of ～0.13–0.2. The differences in the Chl-a estimates between each sensor are possibly due to differences in the sensor design, bio-optical algorithms and also due to the time differences between the satellites over passes. We have examined that the MERIS is performing similar to SeaWiFS and the MODIS-Aqua (Terra) data are reliable in open ocean (coastal) waters. However, Chl-a retrieval algorithms need to be improved especially for coastal turbid waters to continue with SeaWiFS data for long-term studies.     

Atmospheric Correction of SeaWiFS Data for the Retrieval of Suspended Sediment in East China Coastal Waters

The data of SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), installed on SeaStar, has been used to generate SSC (suspended sediment concentration) of complex and turbid coastal waters in China. In view of the problems of the SeaDAS (SeaWiFS Data Analysis System) algorithm applied to China coastal waters, a new atmospheric correction algorithm is discussed, developed, and used for the SSC of East China coastal waters. The advantages of the new algorithm are described through the comparison of the restdts from different algorithms.     

我国海区SeaWiFS资料大气校正

利用光谱辐射传输理论，结合海上同步实测资料，开发出我国海区SeaWiFS资料大气校正模型。经卫星资料处理结果比对，本模型在一类水体，基本消除了412nm和443nm波段离水辐射率小于0的现象；在二类水体，利用临近一类水体的大气条件进行了有效的大气校正；同时建立了670nm,765nm,865nm波段的大气校正模型，这三个面适用于高浓度悬浮泥沙的信息提取。本模型用于处理我国海区的SeaWiFS资料比美国NASA模型更适合我国海区特定的大气和海洋环境，为SeaWiFS资料海洋水色信息提取和我国海洋一号（HY－1）及风云一号（FY－1C）卫星资料的大气校正研究提供了技术基础。     

SeaWiFS retrievals of chlorophyll in Chesapeake Bay and the mid-Atlantic bight

This paper presents three years (1998–2000) of chlorophyll a (chl a) data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) for Case 2 waters of Chesapeake Bay and the middle Atlantic bight (MAB) to describe phytoplankton dynamics on seasonal to interannual time scales. We used extensive data on inherent and apparent optical properties in conjunction with satellite retrievals to: (1) characterize the bio-optical properties of the study area relevant to processing and interpreting SeaWiFS data; (2) test the applicability of the SeaWiFS bio-optical algorithm (OC4v.4) for the estuarine and coastal waters; (3) evaluate the accuracy of the SeaWiFS remote sensing reflectance (R_RS) and chl a products on regional and seasonal bases using in situ observations. The characteristically strong absorption by chromophoric dissolved organic matter (a_cdom) and non-pigmented particulate matter (a_d) in estuarine and coastal waters contributed to overestimates of chl a using OC4v.4 applied to in situ radiances for the Bay (mean ratio 1.42±1.20) and the MAB (2.60±1.36). Values of R_RS from SeaWiFS in the blue region of the spectrum were low compared to in situ R_RS, suggesting that uncertainties remain in atmospheric correction. Direct comparisons of SeaWiFS retrievals of chl a with in situ chl a for the Bay showed larger biases and uncertainties (mean ratio 1.97±1.85) than for chl a estimated from OC4v.4 applied to in situ R_RS. The larger biases were attributed to errors in SeaWiFS radiances and the larger uncertainties to time-space “aliasing” of satellite observations and in situ measurements. To reduce the time differences between SeaWiFS and in situ data, we compared chl a obtained from continuous underway fluorometric measurements on selected ship tracks to SeaWiFS chl a and showed that SeaWiFS captured phytoplankton dynamics in much of the Bay. The agreement of SeaWiFS chl a with in situ chl a was strongest in the mid- (regions 3, 4) to lower Bay (regions 1, 2), and deteriorated toward the upper Bay (regions 5, 6), in part due to a reduction of sensitivity and an increase of noise for SeaWiFS products in the highly absorbing, low R_RS waters of the upper Bay. A three-year time-series of SeaWiFS and in situ data showed that SeaWiFS accurately and reliably captured seasonal and interannual variability of chl a associated with variations of freshwater flow. Significant short-term variability of chl a in summer that was unresolved with shipboard data was detected in the SeaWiFS time-series and the implications are discussed. The overall performance of SeaWiFS in the mid- to lower Bay and the MAB, combined with high spatial (∼1 km²) and temporal (∼100 clear scenes per year) resolution, indicate current SeaWiFS products are valuable for quantifying seasonal to interannual variability of chl a in estuarine and coastal waters.     

高效液相色谱法测量海洋浮游植物色素浓度

JGOFS计算和SeaWiFS计划推荐高效液相色谱（HPLC）作为海洋浮游植物色色浓度的测量方法。文章参考SeaWiFS计划制定的HPLC分析规范，叙述采用反相C18ODS色谱柱、996PDA、自动进样器（未配柱温箱）和三元梯度的色谱法测量海洋浮游植物色素浓度的具体实践。     

Remote sensing of sea surface nutrient in the frontal zone of Changjiang diluted water and the Taiwan Warm Current

Field investigation was carried out during 4 to 15 April 2001 around the Changjiang River Estuary. The similar distribution of sea surface nutrients and suspended sediment (SS) concentration is attributed to the physical mixing of Changjiang diluted water (CDW) with the Taiwan Warm Current (TWC). On the basis of the observed positive relationship between total phosphorus (TP) and SS concentration, the sea surface TP is inversed from satellite SS data. SS is believed to be an ideal eutrophic state assessing index substitution for TP, the eutrophication classification critical value of SS adopted in this research was based on the linear model: cTP=0.000 6cSSsat 0.016 3, r2=0.564 5, n=32. Although lack of in-situ chromophoric dissolved organic matter (CDOM) measurement, a good relationship was observed between the in-situ DIN (dissolved inorganic nitrogen) concentration with near real time SeaWiFS absorption coefficient of CDOM (ACD) data: cDIN=1.406 5AACBsat-0.035 9,r2=0.741 5,n=16. This empirical regression algorithm was also utilized for inversing the DIN concentration from SeaWiFS ACD data, and for establishing the eutrophication classification critical value of satellite ACD data. The established remote eutrophication classification system was later used for seawater eutrophic state assessment. The evaluation suggested that the Zhoushan Fishing Ground especially the western border is affected seriously with the nutrient input. The nutrient is mainly from the terrestrial source transported by the Changjiang River runoff. The seawater quality classification precision was assessed by in-situ data, which suggested the seawater quality distribution is similar to the two classification systems, and the remote classification error is below 25%.     

台风对海洋叶绿素a浓度影响的延迟效应

利用MODIS、SeaWiFS 3A资料详细分析了2000-2006年间西北太平洋海域主要台风对叶绿素a浓度的影响.结果发现,台风可导致叶绿素a浓度最大增长平均值为2.385倍,个别最高达10倍以上,且增长到最大值平均延迟5.94d;同时叶绿素a浓度最大值与无台风时叶绿素a浓度具有线性相关性,相关系数达0.889;叶绿素a浓度与相应的海域平均海水深度具有负的乘幂关系,其相关系数是0.87;台风后叶绿素a浓度的最大增长量与相应海域海水平均深度也呈负乘幂关系,其相关性略低于前者,相关系数为0.75.