Assessment of GOCI radiometric products using MERIS, MODIS and field measurements

The first Geostationary Ocean Color Imager (GOCI) launched by South Korea in June 2010 constitutes a major breakthrough in marine optics remote-sensing for its capabilities to observe the diurnal cycles of the ocean. The light signal recorded at eight wavelengths by the sensor allows, after correction for Solar illumination and atmospheric effects, the retrieval of coloured biogeochemical products such as the chlorophyll, suspended sediment and coloured dissolved organic matter concentrations every hour between 9:00 am and 4:00 pm local time around the Korean peninsula. However operational exploitation of the mission needs beforehand a sound validation of first the radiometric calibration, i.e. inspection of the top-of-atmosphere reflectance, and second atmospheric corrections for retrieval of the water-leaving reflectance at sea surface. This study constitutes a contribution to the quality assessment of the GOCI radiometric products generated by the Korea Ocean Satellite Center (KOSC) through comparison with concurrent data from the MODerate-resolution Imaging Spectroradiometer (MODIS, NASA) and MEdium Resolution Imaging Spectrometer (MERIS, ESA) sensors as well as in situ measurements. These comparisons are made with spatially and temporally collocated data. We focus on Rayleigh-corrected reflectance (?? _RC ) and normalized remote-sensing marine reflectance (nRrs). Although GOCI compares reasonably well with MERIS and MODIS, what demonstrates the success of Ocean Colour in geostationary orbit, we show that the current GOCI atmospheric correction systematically masks out data over very turbid waters and needs further examination and correction for future release of the GOCI products.     

Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS)

GOCI, the world??s first geostationary ocean color satellite, provides images with a spatial resolution of 500 m at hourly intervals up to 8 times a day, allowing observations of short-term changes in the Northeast Asian region. The GOCI Data Processing System (GDPS), a specialized data processing software for GOCI, was developed for real-time generation of various products. This paper describes GOCI characteristics and GDPS workflow/products, so as to enable the efficient utilization of GOCI. To provide quality images and data, atmospheric correction and data analysis algorithms must be improved through continuous Cal/Val. GOCI-II will be developed by 2018 to facilitate in-depth studies on geostationary ocean color satellites.     

The algorithms of chlorophyll-a concentration for CMODIS

The algorithms of extracting chlorophyll-a(Chl-a) concentration have been established for Chinese moderate resolution imaging spectrometer(CMODIS) mounted on Shenzhou-3 spaceship launched on 25 March 2002.The CMODIS is an ocean color sensor with 30 visible channels and 4 infrared channels,much different from other ocean color satellites and needs new algorithms to process data.Three models of Chl-a concentration were established based on Chl-a data retrieved from sea-viewing wide field-of-view sensor(SeaWiFS),with the average relative errors of 26.6%,24%.0% and 33.5%,respectively.This practical and economic approach can be used for developing the algorithms of Chinese ocean color and temperature sensor(COCTS) on the satellite Haiyang-1 to derive the Chl-a concentration concentration distribution.The applicability of the algorithms was analyzed using some in situ measurements.Suspended sediment is the main factor influencing the accuracy of the spectral ratio algorithms of Chl-a concentration.The algorithms are suitable to using in the regions where suspended sediment concentrations(SSC) are less than 5 g/m3 under the condition of relative error of Chl-a concentration retrieval within 35%.High concentration of suspended sediment leads to the overestimate remote sensing retrieval of concentration of Chl-a,while low-middle SSCs lead to the low Chl-a concentration values using the spectral ratio algorithms.Since the accuracy of Chl-a concentration by the spectral ratio algorithms is limited to waters of Case 2,it is necessary to develop semi-analytical models to improve the performance of satellite ocean color remote sensing in turbid coastal waters.     

Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI)

This paper describes an atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI) and its early phase evaluation. This algorithm was implemented in GOCI Data Processing System (GDPS) version 1.1. The algorithm is based on the standard SeaWiFS method, which accounts for multiple scattering effects and partially updated in terms of turbid case-2 water correction, optimized aerosol models, and solar angle correction per slot. For turbid water correction, we used a regional empirical relationship between water reflectance at the red (660 nm) and near infrared bands (745 nm and 865 nm). The relationship was derived from turbid pixels in satellite images after atmospheric correction, and processed using aerosol properties derived for neighboring non-turbid waters. For validation of the GOCI atmospheric correction, we compared our results with in situ measurements of normalized water leaving radiance (nL _w ) spectra that were obtained during several cruises in 2011 around Korean peninsula. The match up showed an acceptable result with mean ratio of the GOCI to in situ nL _w (??), 1.17, 1.24, 1.26, 1.15, 0.86 and 0.99 at 412 nm, 443 nm, 490 nm, 555 nm, 660 nm and 680 nm, respectively. It is speculated that part of the deviation arose from a lack of vicarious calibration and uncertainties in the above water nLw measurements.     

浮游植物叶绿素a 含量测定方法的比较及优化

比较了丙酮萃取法和乙醇-超声波法测定水体中叶绿素a的方法,并对后者进行优化。结果显示,乙醇-超声波法的萃取效率显著地高于丙酮法的效率,两种方法的相关系数为0.9990,回归方程为Chla丙酮=1.0932Chla乙醇–11.677。优化后的乙醇-超声波法的最佳处理条件为:带样滤膜–20℃,冰冻24 h以上,加适量80℃热乙醇在80℃水浴中解冻,经超声振荡处理3 min后,室温黑暗条件下萃取6 h,再离心定容测定。     

离水辐射非朗伯特性的Monte Carlo模拟及分析

水体光场的非朗伯特性(二向性)是目前水色反演算法中的误差因素之一。随着新一代高精度水色遥感器的发射,原来的许多次要因素已变得不可忽略。为研究水体光场的非朗伯特性,首先建立了一个三维MonteCarlo海洋光学模型,并利用国际上提出的7个海洋光学标准问题中的4个对模型的正确性进行了检验;在此基础上,模拟了不同太阳天顶角、体成分等参数对离水辐射率的方向特性的影响。模拟结果表明,在一定的遥感器-太阳-像元几何条件下,同一水体的光场二向性带来的离水辐射率变化可能大于已有的业务化水色大气修正算法反演高水辐射率的误差。模拟结果对水色遥感中正确进行现场数据获取及遥感与地面数据比对有一定的意义。     

基于现场数据的叶绿素荧光量子产量算法研究

自然水体中太阳光激发的叶绿素a荧光的最早观测记录可以追溯到1967年.Tyler et al.发现,在自然水体的反射光谱中685 nm附近存在一个比较明显的反射峰[1],当时解释为由于670nm处的叶绿素吸收而导致的“异常色散”[2].随着Morel et al.[3]和Neville et al.[4]进一步的研究,人们认识到这应该是太阳光激发的叶绿素荧光峰,但这一荧光的量子产量非常低,Gordon估算了它的值在0.002到0.020之间[5].     

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。     

MODIS和GOCI卫星遥感反射率产品在浑浊海区交叉检验分析

对Geostationary Ocean Color Imager(GOCI)和Moderate Resolution Imaging Spectroradiometer(MODIS)传感器在中国渤海辽东湾海区的卫星大气校正算法开展评估工作。主要对比了GOCI和MODIS的412 nm,443 nm,488 nm,547 nm,678 nm波段的遥感反射率(Remote Sensing Reflectance:Rrs)。结果表明:GOCI的去云算法较严格,在卫星有效数据覆盖率方面差于MODIS;遥感反射率产品比对结果表明:GOCI和MODIS的遥感反射率产品有较好的线性相关,且GOCI反演值大于MODIS反演值;分区域的对比结果表明,MODIS和GOCI的遥感反射率差异随着水体的浑浊度增加而增大,GOCI需要开发适用于近岸水体的大气校正算法。     

ATOVS亮温资料同化在台风数值模拟中的应用

利用三维变分同化技术,通过将ATOVS(Advanced TIROS-N Operational Vertical Sounder)(包括AMSUA和AMSUB微波辐射探测器及高分辨率红外辐射探测器HIRS)亮度温度资料直接同化进中尺度数值预报模式WRF中,以提高模式对台风路径的模拟精度。针对两个台风个例鲇鱼(2010)和纳沙(2011)进行了同化试验,结果表明:1)同化卫星亮温资料能够改善台风初始场结构(大气流场、温度场和水汽场),进而提高对台风路径的模拟精度;2)不同资料的同化效果不一样,同化AMSUA资料对台风的路径模拟有较明显的改善效果,而同化HIRS3资料和AMSUB资料则无明显改善效果;3)卫星资料同化对于改变环境引导气流有较大作用,可以通过影响副高的强弱和位置改变环境气流场,从而影响台风的路径。     

Surveillance of waste disposal activity at sea using satellite ocean color imagers: GOCI and MODIS

Korean Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua observations of the variation in ocean color at the sea surface were utilized to monitor the impact of nutrient-rich sewage sludge disposal in the oligotrophic area of the Yellow Sea. MODIS revealed that algal blooms persisted in the spring annually at the dump site in the Yellow Sea since year 2000 to the present. A number of implications of using products of the satellite ocean color imagers were exploited here based on the measurements in the Yellow Sea. GOCI observes almost every hour during the daylight period, every day since June 2011. Therefore, GOCI provides a powerful tool to monitor waste disposal at sea in real time. Tracking of disposal activity from a large tanker was possible hour by hour from the GOCI timeseries images compared to MODIS. Smaller changes in the color of the ocean surface can be easily observed, as GOCI resolves images at smaller scales in space and time in comparison to polar orbiting satellites, e.g., MODIS. GOCI may be widely used to monitor various marine activities in the sea, including waste disposal activity from ships.     

On the long-term variability of normalized radiance of the Black Sea

Basin-wide distributions of normalized spectral radiances averaged over different time periods were plotted based on the estimates of the average radiance for 20 × 20 km squares in 482 digital images of the Black Sea obtained with a SeaWiFS color scanner in 1998–2004. Significant distinctions of the annual average distributions in the amplitude, localization, and configuration of basin-scale inhomogeneities allowed us to consider these distributions as yearly spectral-radiance portraits of the Black Sea that reflect specific features of the annual behavior of the hydrometeorological conditions over the Black Sea region. Seasonal features of the radiance distributions related to the wind field were discovered. Substantial interannual variability in the errors of the standard atmospheric correction algorithm resulting in an underestimation of the shortwave radiances were revealed. It is shown that the river discharge and the stratification of the layer sensed substantially influence the yearly spectral-radiance portraits of the Black Sea.     

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     

基于GOCI影像的长江口及其邻近海域CDOM遥感反演及其日内变化研究

采用静止轨道海洋水色卫星（GOCI）数据对长江口及其邻近海域有色溶解有机物（CDOM）反演。以QAA-CDOM算法为基础,根据实测数据,利用BP神经网络模型来拟合QAA-CDOM算法中需要针对长江口水体进行优化的悬浮颗粒后向散射系数b_bp与吸收系数a_p的关系,从而准确估算CDOM的浓度。结果表明,反演结果准确度较高,平均相对误差为0.35。基于GOCI日内连续成像的优势,选取2014年3月15日8景GOCI影像,利用优化后的QAA-CDOM-BP算法,对长江口及其邻近海域CDOM的日内变化进行反演和分析,得到的变化规律如下:长江口及其邻近海域的CDOM日变化主要受潮流、长江径流等共同影响。长江口内CDOM浓度在涨潮期高于退潮期,由于受长江冲淡水的作用,CDOM从口外往外海区呈现逐渐递减趋势。     

基于DIEOF方法重构海表叶绿素a遥感缺失数据

以2007年1月到2010年12月的MODIS Aqua CHL-a Level 2海表水色产品为基础数据,获得南海北部海域海表叶绿素a浓度的月平均影像集,基于影像集数据的时空相关性利用DIEOF(Data Interpolating Empirical Orthogonal Functions)方法重构其缺失数据。通过分析重构前后数据变化、验证重构结果的时空特征、计算模型精度指标等对重构结果进行评价。研究结果表明:DIEOF方法重构的MODIS海表叶绿素a影像,能够体现研究区海表叶绿素a的时空变化特征,重构结果的复相关系数R2可达到0.98,平均绝对误差MAE小于0.01;该方法重构过程中无需先验信息,易操作,能够有效重构大面积成片缺失或缺失比例较高的影像。     

Seasonal variations in nutrients and chlorophyll-a concentrations in the Northern east China sea

Nutrients, chlorophyll-a, particulate organic carbon (POC), and environmental conditions were extensively investigated in the northern East China Sea (ECS) near Cheju Island during three seasonal cruises from 2003 to 2005. In spring and autumn, relatively high concentrations of nitrate (2.6~12.4 μmol kg^-1) and phosphate (0.17~0.61 μmol kg^-1) were observed in the surface waters in the western part of the study area because of the large supply of nutrients from deep waters by vertical mixing. The surface concentrations of nitrate and phosphate in summer were much lower than those in spring and autumn, which is ascribed to a reduced nutrient supply from the deep waters in summer because of surface layer stratification. While previous studies indicate that upwellings of the Kuroshio Current and the Changjiang (Yangtze River) are main sources of nutrients in the ECS, these two inputs seem not to have contributed significantly to the build-up of nutrients in the northern ECS during the time of this study. The lower nitrate:phosphate (N:P) ratio in the surface waters and the positive correlation between the surface N:P ratio and nitrate concentration indicate that nitrate acts as a main nutrient limiting phytoplankton growth in the northern ECS, contrary to previous reports of phosphate-limited phytoplankton growth in the ECS. This difference arises because most surface water nutrients are supplied by vertical mixing from deep waters with low N:P ratios and are not directly influenced by the Changjiang, which has a high N:P ratio. Surface chlorophyll-a levels showed large seasonal variation, with high concentrations (0.38~4.14 mg m^-3) in spring and autumn and low concentrations (0.22~1.05 mg m^-3) in summer. The surface distribution of chlorophyll-a coincided fairly well with that of nitrate in the northern ECS, implying that nitrate is an important nutrient controlling phytoplankton biomass. The POC:chlorophyll-a ratio was 4~6 times higher in summer than in spring and autumn, presumably because of the high summer phytoplankton death rate caused by nutrient depletion in the surface waters.     

CMODIS资料提取叶绿素a浓度的反演算法研究

中分辨率成像光谱仪(CMODIS)是我国“神舟3号”飞船上对地观测主载荷,是我国第一台上天的具有测量海面叶绿素a浓度能力的成像光谱仪.利用宽视场海洋水色扫描仪(SeaWiFS)反演叶绿素a浓度作为参考值建立CMODIS资料处理模型,得到三个基于蓝绿波段比值法的叶绿素a浓度反演算法,平均相对误差分别为26.6%,24%和33.5%,均方根误差分别为1.16,1.15和1.23 mg/m3.在叶绿素a浓度反演误差允许范围小于35%的条件下,比值算法的适用范围为悬浮泥沙浓度小于5 g/m3的海区.悬浮泥沙的强散射作用导致比值算法在高悬浮泥沙浓度条件下产生高估叶绿素a浓度反演值的现象;在中低悬浮泥沙浓度的海区,悬浮泥沙和浮游植物对离水辐亮度的综合作用使比值算法存在低估叶绿素a浓度的趋势.     

基于GOCI静止水色卫星数据的长江口及邻近海域（490）遥感反演及其在机载激光测深预评估中的应用

长江口水下三角洲地形地貌对于长江口航道安全、生态环境、海岸带工程等均具有重要意义,本次研究拟采用世界上第一颗静止水色卫星GOCI(Geostationary Ocean Color Imager)开展长江口(490)的季节和潮汐变化规律研究,以期为采用机载激光测深提供预评估信息。研究得到结论如下：长江口及邻近海域水体为典型的二类水体,悬浮泥沙含量最高可由杭州湾内几千mg/L迅速降低至10 mg/L以下,因此,分段式的漫衰减系数反演算法适用于研究区域;(490)反演结果表明长江口及邻近海域的(490)值的季节变化特征表现为冬高夏低,春秋居中,长江冲淡水流量和季风是影响其季节变化的主要因素,而在一个潮周期内,(490)值总体表现为低潮期低于高潮期,悬浮泥沙浓度和潮水的潮位是长江口及邻近海域的值的重要影响因素;研究指出,长江口及杭州湾内激光可探测深度约在5 m以下至22 m范围内,夏季退潮低潮位最适合激光雷达观测。由此可见,GOCI每日景每小时景的分辨率可以实现(490)的动态变化监控,而且可以实现在相同潮位下更为合理地描述(490)值的季节变化,为机载激光雷达探测的进一步开展提供了技术支持。