基于奇异谱分析的深圳近海净初级生产力时间序列研究

海洋净初级生产力(Net Primary Productivity, NPP)长时间序列分析可获取NPP多年变化趋势、季节变化等动态信息,对海洋环境的监测与预报具有重要意义。本文以2003～2013年月际尺度净初级生产力卫星遥感产品为数据源,通过奇异谱分析提取标准化净初级生产力时间序列的长期趋势和周期振荡特征。研究表明,①浮标监测数据的叶绿素a浓度、海表温度与MODIS的叶绿素a浓度、海表温度产品变化趋势基本一致,NPP时间序列产品可用于分析深圳近岸海域净初级生产力的变化趋势。②空间分布上,深圳西部海域的NPP和叶绿素a浓度远高于大鹏湾和大亚湾,大鹏湾和大亚湾的NPP和叶绿素a浓度均值及变化趋势非常接近。三个海区NPP、叶绿素a浓度、海表温度和光合有效辐射在季风转换期变化剧烈。③长期趋势上,深圳西部海域呈现2 a周期波动趋势,在均值附近以年为周期上下波动。大鹏湾和大亚湾2003～2006年NPP低于平均水平, 2008年后NPP开始以年为周期围绕平均值上下波动, 2012年后NPP整体与均值持平。④周期特征上,深圳西部海域、大鹏湾和大亚湾的NPP呈"W"或"M"型周年变化,存在夏季主高峰(6～7月)和冬季次高峰(12～1月)。     

南海1998-2002年初级生产力的遥感估算及其时空演化机制

基于SeaWiFS资料得到1998—2002年海洋叶绿素浓度值以及海表温度(SST)数据和其它海洋数据,通过VGPM模型最终反演得到南海1998—2002年逐月初级生产力分布图以及季节分布图,发现在这5年内南海海域初级生产力年平均值变化不大,仅略有差异,1998年的年平均初级生产力要比其它几年的年平均初级生产力小一些;而南海海域初级生产力的季节变化则很明显,冬季要比其它几个季节高得多,而夏季最低。不同季节控制南海海域初级生产力的主导因素各不相同,主要是叶绿素浓度的分布、营养盐的分布、温度条件等,不同海区又略有不同。     

基于VGPM的中国近海净初级生产力的时空变化研究

海洋初级生产力对影响全球碳循环有显著作用,在一定程度上控制着海–气界面CO2的交换,是全球气候变化中的重要课题。随着科学技术的发展,卫星遥感资料已经被广泛地应用于海洋初级生产力研究,文章利用中等分辨率成像光谱仪(MODIS)卫星遥感传感器资料(2003—2014年)分析我国近海海域海洋净初级生产力在近十几年的月度、季度、年度变化趋势,以期对相关研究提供参考。     

海洋初级生产力的遥感研究进展

近年来，海洋初级生产力的遥感反演成为科学界研究的热点问题之一．海洋初级生产力的遥感模型发展迅速．本文综述了海洋初级生产力遥感历史、近几年海洋初级生产力遥感估算模型的新发展和各种模型的局限性．结合各种初级生产力模型，讨论了模型建立所需要的参数；并讨论了初级生产力遥感的主要发展趋势，即根据各个海区的生物一光学特性，对初级生产力参数的估算将越来越区域化．     

遥感海洋初级生产力的生物—地理动力学问题

卫星遥感为大尺度海洋观测提供了一种可行方法。卫星资料应用于海洋初级生产力研究中最重要的是建立由海洋水色遥感资料得出的色素浓度计算初级生产力的合适算法。由于植物生理学方面的问题，建立一个合适的初级生产力算法有一定的困难。本文在分析水下光场和藻类光合作用光谱响应基础上，阐述了海洋初级生产力遥感中的生物－地理动力学问题。     

湛江湾真光层深度与初级生产力的时空变化及其影响因素

基于2016−2017年4个季节航次数据,分析了湛江湾真光层深度与初级生产力的时空变化特征及其影响因素。结果表明,湛江湾真光层深度平均值为（6.95±3.17）m,空间变化比季节变化明显,K_d(PAR)与浊度存在显著的正相关关系,建立的线性回归模型R²为0.73（p<0.01）,表明悬浮颗粒物对湛江湾真光层深度的影响占主导地位。利用VGPM模型得到初级生产力（以碳计）的平均值为（639.53±427.95）mg/(m²·d),其时空特征与真光层深度基本保持一致,真光层深度比叶绿素a浓度更能解释初级生产力的时空分布模式。     

厦门东侧水道海域的初级生产力

厦门东侧水道海域水体叶绿素a年平均值为 2 .79mg/m3,变化于 0 .64～ 8.0 1mg/m3之间 ;初级生产力年平均值为 2 61mg/(m2 ·d) ,变化于 1 5～ 52 5mg/(m2 ·d)之间 .叶绿素a含量以春、夏高 ,秋、冬低为特征 .初级生产力以春、秋高 ,夏、冬低为特点 ,季节差异较明显 ,尤其春、冬季的差别较显著 .研究结果表明该海域具有较高水平的叶绿素a含量和初级生产力 ,这与该海区地处亚热带 ,水温高、光照充足 ,营养盐比较丰富有关 .     

基于VGPM模型的广西北部湾近岸海洋初级生产力估算

以北部湾近岸海域为研究对象,基于海洋水质监测数据与海洋初级生产力实测数据,对VGPM模型各参数分别进行校正,评估校正后的模型估算精度.结果表明:通过参数校正能够有效减小初级生产力计算误差.根据校正模型计算可得,2018年北部湾近岸海洋初级生产力变化范围为101.27～2 161.75 mgC/(m2·d),北部湾近岸海...     

南海北部初级生产力遥感反演及其环境调控机制

选取VGPM模式对南海北部海域初级生产力进行反演,通过SeaWiFS和AVHRR提供的Chl a,K₄₉₀,PAR和SST等遥感数据产品计算得到了模式所需的各项参数,结合现场实测数据对模式进行了修订和误差分析.在此基础上通过反演计算获得了南海北部海域7 a平均(1998～2004年)初级生产力逐月分布图像.研究结果表明,研究海域初级生产力(C)分布趋势为由沿岸带向陆架以及外海逐渐降低,其中沿岸带区高于400 mg/(m2·d),外海区大致在100～300 mg/(m2·d);初级生产力水平冬季最高[平均为608 mg/(m2·d)]、夏季最低[平均为292 mg/(m2·d)],春、秋季基本持平.控制初级生产力时空分布的因子主要有营养盐、温度、光照,其中又以季风和环流驱动下的营养盐变化对初级生产力的调控最为显著.     

改善港湾初级生产力遥感探测方法的探讨

从遥感光谱信号在海水中的传播特点出发，分析了目前应用遥感方法探测港湾初级生产力精度不高的原因；并从港湾初级生产力的自下而上条件着手，指出水交换状态是一种有效的可监测因素，可将其引入到港湾初级生产力的遥感探测模型中。     

基于浮游植物吸收的海洋初级生产力模型的不确定性分析

Satellite-derived phytoplankton pigment absorption(a_(ph)) has been used as a key predictor of phytoplankton photosynthetic efficiency to estimate global ocean net primary production(NPP). In this study, an a_(ph)-based NPP model(Ab PM) with four input parameters including the photosynthetically available radiation(PAR), diffuse attenuation at 490 nm(K_d(490)), euphotic zone depth(Z_(eu)) and the phytoplankton pigment absorption coefficient(a_(ph)) is compared with the chlorophyll-based model and carbon-based model. It is found that the Ab PM has significant advantages on the ocean NPP estimation compared with the chlorophyll-based model and carbonbased model. For example, Ab PM greatly outperformed the other two models at most monitoring sites and had the best accuracy, including the smallest values of RMSD and bias for the NPP estimate, and the best correlation between the observations and the modeled NPPs. In order to ensure the robustness of the model, the uncertainty in NPP estimates of the Ab PM was assessed using a Monte Carlo simulation. At first, the frequency histograms of simple difference(δ), and logarithmic difference(δ~(LOG)) between model estimates and in situ data confirm that the two input parameters(Z_(eu) and PAR) approximate the Normal Distribution, and another two input parameters(a_(ph) and K_d(490)) approximate the logarithmic Normal Distribution. Second, the uncertainty in NPP estimates in the Ab PM was assessed by using the Monte Carlo simulation. Here both the PB(percentage bias), defined as the ratio of ΔNPP to the retrieved NPP, and the CV(coefficient of variation), defined as the ratio of the standard deviation to the mean are used to indicate the uncertainty in the NPP brought by input parameter to Ab PM model. The uncertainty related to magnitude is denoted by PB and the uncertainty related to scatter range is denoted by CV.Our investigations demonstrate that PB of NPP uncertainty brought by all parameters with an annual mean of5.5% covered a range of –5%–15% for the global ocean. The PB uncertainty of Ab PM model was mainly caused by a_(ph); the PB of NPP uncertainty brought by a_(ph) had an annual mean of 4.1% for the global ocean. The CV brought by all the parameters with an annual mean of 105% covered a range of 98%–134% for global ocean. For the coastal zone of Antarctica with higher productivity, the PB and CV of NPP uncertainty brought by all parameters had annual means of 7.1% and 121%, respectively, which are significantly larger than those obtained in the global ocean. This study suggests that the NPPs estimated by Ab PM model are more accurate than others, but the magnitude and scatter range of NPP errors brought by input parameter to Ab PM model could not be neglected,especially in the coastal area with high productivity. So the improving accuracy of satellite retrieval of input parameters should be necessary. The investigation also confirmed that the SST related correction is effective for improving the model accuracy in low temperature condition.     

An optical model for the remote sensing of coloured dissolved organic matter in coastal/ocean waters

An optical model is developed for the remote sensing of coloured dissolved organic matter (CDOM) in a wide range of waters within coastal and open ocean environments. The absorption of CDOM (denoted as a_g) is generally considered as an exponential form model, which has two important parameters – the slope S and absorption of CDOM at a reference wavelength a_g(λ₀). The empirical relationships for deriving these two parameters are established using in-situ bio-optical datasets. These relationships use the spectral remote sensing reflectance (R_rs) ratio at two wavelengths R_rs(670)/R_rs(490), which avoids the known atmospheric correction problems and is sensitive to CDOM absorption and chlorophyll in coastal/ocean waters. This ratio has tight relationships with a_g(412) and a_g(443) yielding correlation coefficients between 0.77 and 0.78. The new model, with the above parameterization applied to independent datasets (NOMAD SeaWiFS match-ups and Carder datasets), shows good retrievals of the a_g(λ) with regression slopes close to unity, little bias and low mean relative and root mean square errors. These statistical estimates improve significantly over other inversion models (e.g., Linear Matrix-LM and Garver-Siegel-Maritorena-GSM semi-analytical models) when applied to the same datasets. These results demonstrate a good performance of the proposed model in both coastal and open ocean waters, which has the potential to improve our knowledge of the biogeochemical cycles and processes in these domains.     

深圳海域弧菌种类组成、数量分布及其与环境因子的关系研究

研究了深圳海域2008年4月、9月的水体弧菌数量、种类组成及其分布,探讨了弧菌总数及各优势类群的数量分布与环境因子间的相关关系。结果表明:弧菌总数分布具有季节性,东部海域春季(4月)的弧菌数量高于秋季(9月)的,其均值分别为1.50×10⁴ ,8.89×10² CFU/cm³;在西部海域秋季的略高于春季的,其均值分别为5.09×10²,2.66×10² CFU/cm³;弧菌数量最高值出现在大亚湾(4.40×10⁴ CFU/cm³)。弧菌的优势类群具有季节性分布特征:春季特有的优势类群有Vibrio gigantis类似种和V. splendidus类似种,秋季特有的优势类群有V. natriegens类似种;V. alginolyticus类似种是两个季节共有的优势类群,其数量春季高于秋季的。弧菌种类、数量与海水温度、盐度及细菌总数、Chl-a等环境因子有一定的相关性,其中盐度为弧菌种类及数量分布的主要影响因子。     

深圳海域弧菌种类组成、数量分布及其与环境因子的关系研究

研究了深圳海域2008年4月、9月的水体弧菌数量、种类组成及其分布,探讨了弧菌总数及各优势类群的数量分布与环境因子间的相关关系。结果表明:弧菌总数分布具有季节性,东部海域春季(4月)的弧菌数量高于秋季(9月)的,其均值分别为1.50×104,8.89×102CFU/cm3;在西部海域秋季的略高于春季的,其均值分别为5.09×102,2.66×102CFU/cm3;弧菌数量最高值出现在大亚湾(4.40×104CFU/cm3)。弧菌的优势类群具有季节性分布特征:春季特有的优势类群有Vibrio gigantis类似种和V.splendidus类似种,秋季特有的优势类群有V.natriegens类似种;V.alginolyticus类似种是两个季节共有的优势类群,其数量春季高于秋季的。弧菌种类、数量与海水温度、盐度及细菌总数、Chl-a等环境因子有一定的相关性,其中盐度为弧菌种类及数量分布的主要影响因子。     

半分析模型在近岸及河口水体透明度反演应用研究

Secchi depth(SD, m) is a direct and intuitive measure of water's transparency, which is also an indicator of water quality. In 2015, a semi-analytical model was developed to derive SD from remote sensing reflectance, thus able to provide maps of water's transparency in satellite images. Here an in-situ dataset(338 stations) is used to evaluate its potential ability to monitor water quality in the coastal and estuarine waters, with measurements covering the Zhujiang(Pearl) River Estuary, the Yellow Sea and the East China Sea where measured SD values span a range of 0.2–21.0 m. As a preliminary validation result, according to the whole dataset, the unbiased percent difference(UPD) between estimated and measured SD is 23.3%(N=338, R~2=0.89), with about 60% of stations in the dataset having relative difference(RD)≤20%, over 80% of stations having RD≤40%. Furthermore, by excluding the field data which with relatively larger uncertainties, the semi-analytical model yielded the UPD of 17.7%(N=132,R~2=0.92) with SD range of 0.2–11.0 m. In addition, the semi-analytical model was applied to Landsat-8 images in the Zhujiang River Estuary, and retrieved high-quality mapping and reliable spatial-temporal patterns of water clarity. Taking into account the uncertainties associated with both field measurements and satellite data processing, and that there were no tuning of the semi-analytical model for these regions, these findings indicate highly robust retrieval of SD from spectral techniques for such turbid coastal and estuarine waters. The results suggest it is now possible to routinely monitor coastal water transparency or visibility at high-spatial resolutions from measurements, like Landsat-8 and Sentinel-2 and newly launched Gaofen-5.     

近30年来江苏海岸带NPP和LST的时空变化及影响因素

近30年来,复杂的气候变化与剧烈的人类活动造成江苏省海岸带生态演变剧烈,且呈现显著的空间异质性。植被净初级生产力（NPP）和地表温度（LST）是生态系统的2个关键参数,通过将1990—2020年Landsat遥感影像与CASA计算模型和相关性分析等方法结合,分析了江苏海岸带NPP和LST的时空变化及影响因素,结果表明：①由于人类对沿海滩涂资源的利用以及养殖业的发展等,江苏海岸带范围随岸线不断变化,岸线逐步向海推进,且南部向海推进范围大于北部。②近30年来,江苏海岸带NPP和LST呈现出显著的时空异质性特征。时间上1990、2000、2010、2020年代的NPP月均值分别为102.88、88.23、156.62、98.90 g C·m−2,呈现下降－上升－下降趋势,而LST月均值分别为32.6、31.7、28.3、37.6 ℃,呈现先下降后上升的趋势。空间上,NPP与LST在江苏海岸带南北分布呈现出一定差异性。③地表覆盖类型是影响江苏海岸带NPP和LST时空异质性的主要因素。林地的NPP最高,养殖池塘NPP最低;人工建筑的LST值最高,湿地、水域与养殖池塘的LST值相对较低。此外,随着气温升高,NPP和LST有逐渐上升的趋势,而植被覆盖度的升高则导致NPP上升和LST下降。     

Method of integrating landsat-5 and landsat-7 data to retrieve sea surface temperature in coastal waters on the basis of local empirical algorithm

A useful radiance-converting method was developed to convert the Landsat-7 ETM+thermal-infrared (TIR) band’s radiance (Lλ,_L7/ETM+) to that of Landsat-5 TM TIR (Lλ,_L5/TM) as: Lλ,_L5/TM=0.9699xLλ,_L7/ETM++0.1074 (R²=1). In addition, based on the radiance-converting equation and the linear relation between digital number (DN) and at-satellite radiance, a DN-converting equation can be established to convert DN value of the TIR band between Landsat-5 and Landsat-7. Via this method, it is easy to integrate Landsat-5 and Landsat-7 TIR data to retrieve the sea surface temperature (SST) in coastal waters on the basis of local empirical algorithms in which the radiance or DN of Lansat-5 and 7 TIR band is usually the only input independent variable. The method was employed in a local empirical algorithm in Daya Bay, China, to detect the thermal pollution of cooling water discharge from the Daya Bay nuclear power station (DNPS). This work demonstrates that radiance conversion is an effective approach to integration of Landsat-5 and Landsat-7 data in the process of a SST retrieval which is based on local empirical algorithms.     

深圳沿岸海域贝类重金属和贝类毒素污染状况分析

通过检测深圳沿岸海域7种贝类的重金属、麻痹性贝毒(PSP)和腹泻性贝毒(DSP)含量,了解其食用安全性.严格按照国家标准进行检测,采用SPSS 20.0对数据进行Kruskal-Wallis H检验和方差分析.结果表明:各种重金属和贝类毒素在样品中均有检出,汞污染状况良好;铜含量范围为0.52~109.65 mg/kg,超标率为13.67%;铅含量范围为0.01~3.60 mg/kg,超标率为68.35%;镉含量范围为0.015~7.807 mg/kg,超标率为53.96%.PSP和DSP检出率为100%,PSP含量范围为0.30~34.23 ug/100 g;DSP含量范围为1.88~38.41 ug/100 g.总体看来,样品铅、镉超标严重,不同种类贝类重金属含量具有差异且贝类毒素检出率较高,应引起有关部门的重视,加强该海域监测工作.