Retrieval and Validation of Chlorophyll-a Concentrations in the Coastal Waters Off Yanam and Kakinada (Godavari) Basin Along East Coast of India

In this study chlorophyll measurements were made during March 2012 in the estuarine waters of Off Kakinada and Yanam coast, Bay of Bengal onboard a coastal vessel. In-situ water samples and optical data was collected at 21 stations (surface to 150 m depth) using Underwater radiometer (Hyperpro-II). In-vivo chlorophyll profiles were collected using wet labs fluorometer integrated with underwater Hyperspectral radiometer. Chlorophyll-a concentrations were estimated using HPLC by collecting the water samples at each sampling location. And also chlorophyll-a concentrations were retrieved from the OCM-2 data of OCEANSAT-2 satellite, processed using SeaDAS v.6.2 with the available global ocean colour algorithms namely, OC2 and OC4V4. A total of 33 samples used covering all the stations for chlorophyll-a estimation, and surface water samples of all the stations only being used for direct comparison among chlorophyll concentrations of HPLC, in-situ (fluorometrically integrated to Hyperpro-II) and retrieved from OCM-2. A good correlation found between the Fluorometer derived and HPLC measured chlorophyll-a concentration with an R² value of 0.78. The relation between Chlorophyll-a concentration measured from HPLC and retrieved from OCM-2 (OC2 and OC4V4 algorithms) using SeaDASv.6.2 for 10 samples has been compared for validation and obtained an R² value of 0.6. Also comparisons done with the in-situ measured (fluorometer) Chlorophyll-a concentration with OCM-2 chlorophyll data (OC4-V4 and OC2 algorithms) and validation with 10 concurrent in-situ surface measurements showed a significant overestimation by OCM-2 at low chlorophyll-a concentrations and underestimation at high chlorophyll-a concentrations.     

Atmospheric Correction Over Coastal Turbid Waters of Bay of Bengal Using OCEANSAT-I Ocean Colour Monitor (OCM) Data

Space-borne ocean-colour remote sensor-detected radiance is heavily contaminated by solar radiation backscattered by the atmospheric air molecules and aerosols. Hence, the first step in ocean-colour data processing is the removal of this atmospheric contribution from the sensor-detected radiance to enable detection of optically active oceanic constituents e.g. chlorophyll-a, suspended sediment etc. In standard atmospheric correction procedure for OCEANSAT-1 Ocean Colour Monitor (OCM) data, NIR bands centered at 765 and 865 nm wavelengths were used for aerosol characterization. Due to high absorption by water molecules, ocean surface in these two wavelengths acts as dark background, therefore, sensor detected radiance can be assumed to have major contribution from atmospheric scattering. For coastal turbid waters this assumption of dark surface fails due to the presence of highly scattering sediments which causes sufficient water-leaving radiance in NIR bands and lead to over-estimation of aerosol radiance resulting in negative water leaving radiance for λ < 700 nm. In the present study, for the turbid coastal waters in the northern Bay of Bengal, the concept of spatial homogeneity of aerosol and water leaving reflectance has been applied to perform atmospheric correction of OCAEANSAT-1 OCM data. The results of the turbid water atmospheric correction have also been validated using in-situ measured water-leaving radiance. Comparison of satellite derived water-leaving radiance for five coastal stations with in-situ measured radiance spectra, indicates an improvement over the standard atmospheric correction algorithm giving physically realistic and positive values. Root Mean Square Error (RMSE) between the in-situ measured and satellite derived water leaving radiance for wavelengths 412 nm, 443 nm, 490 nm, 512 nm and 555 nm was found to be 1.11, 0.718, 0.575, 0.611 and 0.651%, respectively, using standard atmospheric correction procedure. By the use of spatial homogeneity concept, this error was reduced to 0.125, 0.173, 0.176, 0.225, and 0.290 and the correlation coefficient arrived at 0.945, which is an improvement over the standard atmospheric correction procedure.     

A Four-Band Quasi-Analytical Algorithm With MODIS Bands for Estimating Chlorophyll-a Concentration in Turbid Coastal Waters

Accurate estimation of chlorophyll-a concentration in turbid coastal waters by means of remote sensing is challenging due to the optical complexity of these waters. We have developed a four-band quasi-analytical algorithm for assessment of chlorophyll-a concentration in coastal waters. The objectives of this study are to validate the applicability of three-band semi-analytical algorithm, quasi-analytical algorithm, and four-band quasi-analytical algorithm in estimating chlorophyll-a concentration in turbid coastal waters for MODIS sensor. These three algorithms are calibrated and evaluated against coastal evaluation datasets provided by SeaWiFS Bio-optical Archive and Storage System. The algorithm validation results indicate that the four-band quasi-analytical algorithm produces a superior performance to both three-band semi-analytical algorithm and quasi-analytical algorithm. By comparison, using four-band quasi-analytical algorithm produces 21.61 % uncertainty in estimating chlorophyll-a concentration from turbid coastal waters, lower than 77.90 % for three-band semi-analytical algorithm and 74.31 % for quasi-analytical algorithm, respectively. The significantly reduced uncertainty in chlorophyll-a concentration assessment is due to effectively removal of pigment-package effects and particle overlapping effects on the chlorophyll-a absorption estimation using a optical classification method. These findings imply that, provided that an atmospheric correction scheme for visible and near-infrared bands is available, the database of MODIS imagery could be used for quantitative monitoring of chlorophyll-a concentration in turbid coastal waters by four-band quasi-analytical algorithm.     

Variability of Total Suspended Matter in the Northern Coastal Bay of Bengal as Observed from Satellite Data

The coastal regions of northern Bay of Bengal (BoB) are one of the most turbid areas owing to the large freshwater discharge from the three major river systems. This study is conducted to investigate the seasonal and interannual variability of total suspended matter (TSM) in the northern BoB. TSM concentration derived from medium resolution imaging spectrometer for the period 2002–2011 is used for this study. TSM concentration is observed to be the highest during summer monsoon season along the north-eastern region, off the Ganges–Brahmaputra river mouth. The variability of the TSM concentration depicts the role of river runoff associated with the summer monsoon in influencing the same. It is observed that the sediment concentration tapers away towards the offshore regions with the maximum extent observed up to 21.5°N latitude. Interannual variability is also observed with highest TSM concentrations occurring in the years 2003, 2008 and 2011 and least during 2004. Time series analysis performed at three major river discharge regions illustrated the distinct and highly variable nature of TSM dynamics prevailing in the northern BoB.     

Understanding the Spatial Variability of Chlorophyll a and Total Suspended Matter Distribution Along the Southwest Bay of Bengal Using In-Situ and OCM-2 & MODIS-Aqua Measurements

Spatial and temporal distribution of chlorophyll a (chl a) and Total Suspended Matter (TSM) and inter comparison of Ocean Color Monitor-2 (OCM-2) and Moderate Resolution Imaging Spectro-radiometer (MODIS-Aqua) derived chlorophyll a and TSM was made along the southwest Bay of Bengal (BoB). The in-situ chl a and TSM concentration measured during different seasons were ranged from 0.09 to 10.63 μgl^?1 and 11.04–43.75 mgl^?1 respectively. OCM-2 and MODIS derived chl a showed the maximum (6–8 μgl^?1) at nearshore waters and the minimum (0–1 μgl^?1) along the offshore waters. OCM-2 derived TSM imageries showed the maximum (50–60 mgl^?1) along the nearshore waters of Palk Strait and the moderate concentration (2–5 mgl^?1) was observed in the offshore waters. MODIS derived minimum TSM concentration (13.244 mgl^?1) was recorded along the offshore waters, while the maximum concentration of 15.78 mgl^?1 was found along the Kodiakarai region. The inter-comparison of OCM-2 and MODIS chl a data (R ² ?=?0.549, n?=?49, p?<?0.001, SEE?=?±0.117) indicate that MODIS data overestimates chl a concentration in the nearshore waters of the southern BoB compared to the OCM-2. The correlation between OCM-2 and MODIS-Aqua TSM data (R ² ?=?0.508, N?=?53, P?<?0.001 and SEE?=?±0.024) confirms that variation in the range of values measured by OCM-2 (2–60 mgl^?1) and the MODIS (13–16 mgl^?1) derived TSM values. Despite problems in range of measurements, persistent cloud cover etc., the launch of satellites like OCM-2 with relatively high spatial resolutions makes job easier and possible to monitor chl a distribution and sediment discharges on day to day basis in the southwest BoB.     

Satellite and In Situ Observations of a Phytoplankton Bloom from Coastal Bay of Bengal: Role in pCO<Subscript>2</Subscript> Modulation

A phytoplankton bloom was monitored in coastal waters of Bay of Bengal and its influence in water column properties was investigated. Significant draw down of CO₂ was noted within the vicinity of the bloom associated with high chlorophyll biomass. Microscopic analysis revealed diatoms as the dominant population. Skeletonema costatum a diatom, reached cell density of 36,898 cells l^?1 within the bloom. The lowest surface pCO₂ observed was 287 µatm at the southern end of the transect covarying with surface chlorophyll of 1.090 µg l^?1. At the northern end the surface pCO₂ went as low as 313 µatm. The pCO₂ levels below the mixed layer increased twice of that of surface value (~600 µatm). The chlorophyll values observed by Ocean Colour Monitor-2 were modestly related with the in situ measurements. The primary productivity derived from growth rate, assimilation number and maximum surface chlorophyll was 160.6 mg C m^?2 day^?1 leading to a modest sequestration ~of 0.08 Gg of carbon per day by the surface waters. Our observations reflects the potential role of diatom blooms on coastal carbon dynamics therefore should be carefully monitored in realm of anthropogenic changes.     

利用卫星测高资料反演中国近海海洋重力

本文利用Topex/Poseidon卫星测高资料,从快速Hartley变换(FHT)基本概念入手,给出了Hotine公式在平面近似、球面近似、Molodenskii近似下,反演中国近海海洋重力的数学模型。另对FHT处理中所需的坐标转换以及边缘效应等问题进行了讨论。同时,为了改善长波特性的重力场信息,引入了M阶次的OSU91A参考重力场对上述Molodenskii模型进行了改化。     

Super Cyclone Induces a Mesoscale Phytoplankton Bloom in the Bay of Bengal

Tropical cyclones when on land create havoc, but over the oceans they can trigger a very strong biological response, giving rise to phytoplankton blooms. The Super Cyclone (TC) 05B that occurred during October 25–29, 1999, in the Bay of Bengal over the tropical Indian ocean was one of the most significant tropical cyclones on record to affect India, with maximum winds of 240 km/h, and the worst since 1971. Using satellite data, it is found that this tropical super cyclone helped spawn a notable mesoscale phytoplankton bloom in the domain (17 $^{circ}$–20$^{circ} hbox{N}$; 87$^{circ}$–90 $^{circ} hbox{E}$), which persisted for over a month. The bloom spanned 440 km zonally and 330 km meridonally, enhanced the chlorophyll-$a$ concentrations to a maximum of 10 $hbox{mg/m}^{3}$ and the net primary productivity by 200%. Furthermore, a cyclonic eddy over the bloom region is revealed from an ocean general circulation model simulation, helping the bloom to last for over month.      

基于环境因素的沿岸水域叶绿素遥感探测研究

海洋叶绿素的调查一直是海洋生物资源与海洋生态学的研究重点。遥感技术是海洋叶绿素调查的有力工具。利用遥感光谱信号探测海水叶绿素在大洋水体(一类水体)中十分成功,但在沿岸水体(二类水体)的精度却不高,主要是由于悬浮泥沙、黄色物质等的干扰太大。从叶绿素的生存条件入手,提出了利用海水叶绿素环境生存因子作为辅助因素的方法建立叶绿素遥感探测模型。并以珠江口海域作为研究对象展开试验,结果表明:环境因子的引用,使模型的误差从32·48%降为17·96%,精度大大提高,从而证明该方法的可行性和有效性。     

我国近海平均海面及其变化的研究

建立了计算平均海面及其变化的动态抗差模型,并把它与计算平均海面的平均值法、抗差法和动态模型法作了实测数据的计算和比较,表明动态抗差模型不仅能顾及海面动态变化效应,而且能削弱海面异常变化的影响,其结果更稳定可靠,优于其他方法。最后应用动态抗差模型,计算了我国42个验潮站的平均海面及其变化,结果表明,从50年代到70年代,我国近海的海面平均以0.621mm/a的速率上升。     

Implementing a Spatial Model to Derive Potential Fishing Zones in the Northern Bay of Bengal Lying Adjacent to West Bengal Coast,India

Potential fishing zones (PFZ’s) are those regions where the fishes aggregate due to an abundance of food and they are demarcated by tracing those regions in the ocean, where a sharp sea surface temperature (SST) gradient along with optimal chlorophyll (Chl) concentration co-exists at a given time. In this regard, Indian National Centre for Ocean Information Services (INCOIS) disseminates the daily PFZ forecasts in Bay of Bengal and Arabian Sea to aid the fishermen community. The present study is an endeavor to develop a local spatial model derived Potential Fishing Zone (PFZ) in the northern Bay of Bengal (nBoB) lying adjacent to the West Bengal coast. Satellite derived SST and chlorophyll data obtained for two consecutive winter seasons of 2010–11 and 2011–12 were used to generate line density (LD) raster. Shapefiles of INCOIS predicted PFZs were overlaid on these LD raster to extract the corresponding pixel values. Histogram ranges of the extracted pixels were fixed and same values lying in the LD raster of both SST and chlorophyll other than INCOIS PFZs were detected by a spatial model in ERDAS. The PFZs thus derived were validated against the ground fish catch data and it was observed that good fish catch was seen in the model derived additional PFZs also. The catch per unit effort (CPUE) values was found to be very close to that of the CPUE value of PFZ advisories of INCOIS. However, the CPUE in the non PFZ areas were significantly lower than the former two categories.     

Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

基于GIS的环渤海湾海岸带环境压力分析

本文从景观格局、海洋资源、植被资源三个角度,探讨了2000年至2013环渤海湾海岸带不透水面空间格局变化对环境压力的影响。研究结果发现:(1)2000年至2013年,不透水面斑块面积增加,斑块密度减少,呈现团聚状分布格局;(2)2000年至2013年,滩涂、0-5m海域和5-10m海域空间资源的损失量分别为464.32km2、151.76km2和12.81km2;(3)研究区内城市建成区的植被减少和不透水面扩张之间呈现明显的此消彼长趋势。     

Phytoplankton Variability in the Bay of Bengal During Winter Monsoon Using Oceansat-1 Ocean Colour Monitor Data

Quantitative assessment of chlorophyll-a concentration and its variability is an important input for the oceanic primary productivity modeling and also a key parameter in the global carbon cycle studies. This present work is focused to understand the spatial and temporal variability of phytoplankton in the Bay of Bengal (BOB) during winter monsoon season of October 1999 to March 2000 using Ocean Colour Monitor (OCM) sensor onboard OCEANSAT-1 satellite. Daily chlorophyll-a images from OCM sensor were used in the study. Efforts were also put to study the correlation between chlorophyll-a concentrations; NOAA-AVHRR derived Sea Surface Temperature (SST) and QuickSCAT scatterometer derived wind stress data. Analysis of the chlorophyll-a images shows the presence of extensive phytoplankton blooms during mid December 1999 to early January 2000 in the western part of BOB. The bloom dominated regions also exhibit reduced SST (∼24–27°C) and enhanced wind stress indicating upwelling processes leading nutrient entrainment in the upper column of the sea surface. Apart from this, higher phytoplankton biomass associated with the fresh water reverine plumes has also been observed. During October 1999 a super cyclone was active in the BOB, as increase in the productivity was observed in the early November 1999 images of OCM data due to the cyclone induced churning of the water column.     

遥感在悬浮物质浓度提取中的应用—以福建闽江口为例

介绍了悬浮物质浓度遥感监测的一般原理，重点介绍光谱混合分析法，利用TM遥感影像，分别用光谱混合分析法和回归分析的经验模型计算了闽江口悬浮物质浓度的分布，对结果进行了分析和对比，结果表明，光谱混合分析法可以充分利用多波段的数据，不需要大量的实验数据，有较好的实用性，能用于缺乏实测数据的区域。     

Coastal Validation of Ultra-high Resolution Wind Vector Retrieval From QuikSCAT in the Gulf of Maine

An experimental 2.5-km ultrahigh-resolution (UHR) wind product provided by NASA's QuikSCAT scatterometer offers the potential for new access to coastal surface wind dynamics at the mesoscale level and below. To give future users the best indication of the value of these data, the UHR wind retrievals must be fully validated in nearshore areas. Comparison with meteorological buoys and standard QuikSCAT products allows detailed investigation of UHR winds. Speed and direction residuals are calculated between all scatterometer products and collocated buoys. An ambiguity selection routine improves wind direction agreement between the UHR winds and the other products. Magnitude residuals follow the patterns of the standard QuikSCAT winds, with a 1–2 m/s positive bias in light winds (below 4 m/s) and high winds (above 16 m/s) and standard deviations consistently below 3 m/s. After application of a land contamination removal algorithm, the UHR product provides extended coverage near the coast. An example of a specific wind event illustrates the potential benefits of improved resolution measurements for examining ocean–atmosphere dynamics.      

胶州湾海岸带土地利用/覆被变化研究

对影像进行大气校正等处理后,引入地学知识构建决策树,运用决策树法对1989年及2000年Landsat TM/ETM影像进行分类,得到这两个时期的土地利用类型图,然后对这两期土地利用类型进行叠加分析,得到土地利用变化矩阵。1989～2000年胶州湾土地利用变化过程表现为:城镇等建成地、养殖及盐田用地、城镇的建设裸地或待建地等迅速扩增,而耕地、内陆滩涂迅速减少,海域减少,林、草地变化不大;耕地主要向城镇等建成地、养殖及盐田用地转化,海域向养殖及盐田用地、城镇建成地转化。十余年间,人类活动对环境的影响增强,城市"三废"增加,陆源营养盐和其他污染物大量排放入海,引起胶州湾海水水质恶化、沉积物重金属含量增加,浮游生物减少,生物多样性降低,赤潮频发。     

Total Suspended Sediments Mapping by Using ALOS Imagery Over the Coastal Waters of Langkawi Island, Malaysia

Remote sensing has been extensively used for water delineation and has played an important role in water quality evaluation and environmental management strategies. Suspended sediments are important determinants of water quality in coastal zones. Remote sensing enables the effective monitoring of total suspended sediments (TSS) and the detection of areas with critical water quality issues. This study aims to develop and implement regression models for estimating and mapping TSS concentrations from Advanced Land Observation Satellite (ALOS) images over the coastal waters of Langkawi Island, Malaysia. The algorithm was developed based on the water reflectance model, which is a function of the inherent optical properties of water. Such properties can then be linked to TSS concentration. In this study, an ALOS Advanced Visible and Near Infrared Radiometer type 2 device was used as the imaging sensor system. Concurrent complementary in-situ water samples were collected within the area coverage of the sensor, and digital numbers (DN) for each band corresponding to the sea-truth locations were determined. The extracted DN values were converted into reflectance values and then regressed with their respective sea-truth data. An algorithm was proposed to obtain the regression coefficient. This algorithm can estimate TSS concentrations with a high correlation coefficient (R²?=?0.96) and low root-mean-square error (RMSE?=?1.98 mg/l). Finally, a map of the TSS concentration was generated by using the proposed algorithm. This study found that TSS mapping can be conducted by using ALOS data over the coastal waters of Langkawi Island, Malaysia.     

杭州湾滨海湿地时空格局变化遥感调查研究

采用TM、SPOT-5卫星遥感数据,通过建立杭州湾滨海湿地分类体系和解译标志并进行人机交互解译,完成了杭州湾1987、1995、2003和2009年滨海湿地提取和分类。研究了4期杭州湾滨海湿地的利用状况、面积,以及时空格局变化情况,研究显示:1987~2009年期间,杭州湾滨海湿地主要表现为滩涂湿地的逐年减少和库塘湿地的逐年增加;1995年之前湿地变化以自然驱动力为主,之后人类活动影响明显,对近海域、滩涂不断开发,尤以南岸为主;以杭州湾湿地公园为典型的沼泽草甸湿地得到了较好的保护。     

View on Bay of Bengal upwelling area on the basis of 19-years of satellite sea surface temperature

Abstract

The seasonal and interannual variation of upwelling along the east coast of India between 6°N and 22°N was studied for the period 1985–2003 using NOAA–AVHRR sea surface temperature data. The seasonal migration of pronounced upwelling, which follows the seasonal shift of the winds in transition period and northeast monsoon, was confined. The temporal mean sea surface temperature images clearly show the upwelling season, as does the seasonal sea surface temperature anomaly. These dominant features of the upwelling system are also the most variable, with most of the variance being explained by the seasonal cycle. Quasi-cyclic behaviour of sea surface temperature on interannual scales has also been observed.