A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China

Accurate estimation of phytoplankton chlorophyll a (Chla) concentration from remotely sensed data is particularly challenging in turbid, productive waters. The objectives of this study are to validate the applicability of a semi-analytical three-band algorithm in estimating Chla concentration in the highly turbid, widely variable waters of Taihu Lake, China, and to improve the algorithm using a proposed four-band algorithm. The improved algorithm is expressed as [Rrs(λ₁)^− 1 − Rrs(λ₂)^− 1][Rrs(λ₄)^− 1 − Rrs(λ₃)^− 1]^− 1. The two semi-analytical algorithms are calibrated and evaluated against two independent datasets collected from 2007 and 2005 in Taihu Lake. Strong linear relationships were established between measured Chla concentration and that derived from the three-band algorithm of [Rrs^− 1(660) − Rrs^− 1(692)]Rrs(740) and the four-band algorithm of [Rrs^− 1(662) − Rrs^− 1(693)][Rrs^− 1(740) − Rrs^− 1(705)]^− 1. The first algorithm accounts for 87% and 80% variation in Chla concentration in the 2007 and 2005 datasets, respectively. The second algorithm accounts for 97% of variability in Chla concentration for the 2007 dataset and 87% of variation in the 2005 dataset. The three-band algorithm has a mean relative error (MRE) of 43.9% and 34.7% for the 2007 and 2005 datasets. The corresponding figures for the four-band algorithm are 26.7% and 28.4%. This study demonstrates the potential of the four-band model in estimating Chla even in highly turbid case 2 waters.     

Developing a two-step retrieval method for estimating total suspended solid concentration in Chinese turbid inland lakes using Geostationary Ocean Colour Imager (GOCI) imagery

Total suspended solid (TSS) concentration is an important water quality parameter. Mapping its varying distribution using satellite images with high temporal resolution is valuable for studying suspended sediment transportation and diffusion patterns in inland lakes. A total of 255 sites were used to make remote-sensing reflectance measurements and surface water sampling at four Chinese inland lakes, i.e. Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Gorges Reservoir, at different seasons. A two-step retrieval method was then developed to estimate TSS concentration for contrasting Chinese inland lakes, which is described in this article. In the first step, a cluster method was applied for water classification using eight Geostationary Ocean Colour Imager (GOCI) channel reflectance spectra simulated by spectral reflectance measured by an Analytical Spectral Devices (ASD) Inc. spectrometer. This led to the classification of the water into three classes (1, 2, and 3), each with distinct optical characteristics. Based on the water quality, spectral absorption, and reflectance, the optical features in Class 1 were dominated by TSS, while Class 3 was dominated by chl-a and the optical characteristics of Class 2 were dominated jointly by TSS and chl-a. In the second step, class-specific TSS concentration retrieval algorithms were built. We found that the band ratio Band 8/Band 4 was suitable for Class 1, while the band ratio of Band 7/Band 4 was suitable for both Class 2 and Class 3. A comprehensive determination value, combining the spectral angle mapper and Euclidean distance, was adopted to identify the classes of image pixels when the method was applied to a GOCI image. Then, based on the pixel’s class, the class-specific retrieval algorithm was selected for each pixel. The accuracy analysis showed that the performance of this two-step method was improved significantly compared to the unclassed method: the mean absolute percentage error decreased from 38.9% to 24.3% and the root mean square error decreased from 22.1 to 16.5 mg l^–1. Finally, the GOCI image acquired on 13 May 2013 was used as a demonstration to map the TSS concentration in Taihu Lake with a reasonably good accuracy and highly resolved spatial structure pattern.     

长江口及邻近海域悬浮颗粒物对叶绿素a遥感反演算法的影响分析

利用水介质光辐射传输数值模型Hydrolight,结合前人对长江口及邻近海域水体的生物—光学模型研究,模拟不同光学水体的遥感反射率,并分析遥感反射率对悬浮颗粒物(SPM)的敏感性以及SPM对4种叶绿素a(Chla)反演算法(二波段、三波段、荧光基线高度(FLH)和综合叶绿素指数(SCI)算法)的影响。结果表明:由Hydrolight模拟得到的遥感反射率与现场同步实测的遥感反射率的均方根误差小于0.01sr-1,其中可实现遥感反射率在550~725nm波段较精确的模拟。遥感反射率对SPM的敏感性随着Chla浓度的升高而降低。二波段、三波段算法适合低SPM浓度水体的Chla浓度反演,FLH算法反演Chla浓度时易受SPM的影响,而SCI算法在中、高SPM浓度水体中消除SPM的影响进而反演Chla的潜力较好。     

Remote sensing of phycocyanin pigment in highly turbid inland waters in Lake Taihu,China

Cyanobacterial blooms are an environmental issue that can cause health hazards by toxins and malodorous compounds. The pigment phycocyanin is indicative of cyanobacterial presence. In eutrophic inland waters in which nitrogen is not a limiting nutrient, the phycocyanin concentration (PC) is closely related to cyanobacterial biomass. This study proposes a simple semi-analytical four-band algorithm for PC estimation to overcome the deficiency of existing algorithms. This algorithm was calibrated using a data set collected from Lake Taihu in 2007. Optimal reference wavelengths for the algorithm were located through model tuning and accuracy optimization. The algorithm was evaluated for its accuracy against an independent data set collected in 2008. The performance of the algorithm was also compared with that of the nested band-ratio algorithm, which was developed for PC estimation in turbid waters.

Although both algorithms enabled the establishment of a linear relationship between measured and predicted PC, the nested band-ratio algorithm did not have a satisfactory performance with either data set, having a high level of uncertainty. Its mean relative error stands at 51.07% and 51% for the 2007 and 2008 data sets, respectively. It accounted for 68% and 74% of the variation in PC in the 2007 and 2008 data sets, respectively. The four-band algorithm worked well in PC estimation. It accounted for 87% of the variation in PC for the 2007 data set and 86% of the variation in the 2008 data set. Furthermore, it decreased estimation uncertainty, compared with the nested band-ratio algorithm, by more than 20%. The values of mean relative error for the correspondence data sets are 29.1% and 30%. Therefore, the proposed four-band algorithm holds great potential in estimating PC in highly turbid waters.     

Modeling spectral reflectance of optically complex waters using bio-optical measurements from Tokyo Bay

This study presents an approach for optimally parameterizing a reflectance model. A parameterization scheme is realized based on a comprehensive bio-optical data set, including subsurface downwelling and upwelling irradiance spectra, absorption spectra of particle and dissolved substances, as well as chlorophyll and total suspended matter concentrations at 45 stations near Tokyo Bay between 1982 and 1984. The irradiance reflectance model is implemented with three-component inherent optical property submodels.In this parameterization scheme, an unsupervised classification was applied in the hyper-spectral space of reflectance, leading to three spectrally distinct optical water types. The reflectance model was parameterized for the entire data set, and then parameterized for each of the water types. The three sets of type-specific model parameters, which define corresponding IOP submodels, are believed to accommodate differences in the optical properties of the in-water constituents. The parameterized reflectance model was evaluated by both reconstructing measured reflectance spectra and solving for the nonlinear inverse problem to retrieve in-water constituent concentrations. The model accuracy was significantly improved in the forward direction for classified waters over that of non-classified waters, but no significant improvement was achieved in the retrieval accuracy (inverse direction). A larger data set with greater resolution of constituent inherent optical properties would likely improve the modeling results.     

An assessment of chlorophyll-a algorithms available for SeaWiFS in coastal and open areas of the Bay of Bengal and Arabian Sea

Three ocean colour algorithms, OC4v6, Carder and OC5 were tested for retrieving Chlorophyll-a (Chla) in coastal areas of the Bay of Bengal and open ocean areas of the Arabian Sea. Firstly, the algorithms were run using ~ 80 in situ Remote Sensing Reflectance, (R_rs(λ)) data collected from coastal areas during eight cruises from January 2000 to March 2002 and the output was compared to in situ Chla. Secondly, the algorithms were run with ~ 20 SeaWiFS R_rs(λ) and the results were compared with coincident in situ Chla. In both cases, OC5 exhibited the lowest log₁₀-RMS, bias, had a slope close to 1 and this algorithm appears to be the most accurate for both coastal and open ocean areas. Thirdly the error in the algorithms was regressed against Total Suspended Material (TSM) and Coloured Dissolved Organic Material (CDOM) data to assess the co-variance with these parameters. The OC5 error did not co-vary with TSM and CDOM. OC4v6 tended to over-estimate Chla > 2 mg m⁻³ and the error in OC4v6 co-varied with TSM. OC4v6 was more accurate than the Carder algorithm, which over-estimated Chla at concentrations > 1 mg m⁻³ and under-estimated Chla at values < 0.5 mg m⁻³. The error in Carder Chla also co-varied with TSM. The algorithms were inter-compared using > 5500 SeaWiFS R_rs(λ) data from coastal to offshore transects in the Northern Bay of Bengal. There was good agreement between OC4v6 and OC5 in open ocean waters and in coastal areas up to 2 mg m⁻³. There was a strong divergence between Carder and OC5 in open ocean and coastal waters. OC4v6 and Carder tended to over-estimate Chla in coastal areas by a factor of 2 to 3 when TSM > 25 g m⁻³. We strongly recommend the use of OC5 for coastal and open ocean waters of the Bay of Bengal and Arabian Sea. A Chla time series was generated using OC5 from 2000 to 2003, which showed that concentrations at the mouths of the Ganges reach a maxima (~ 5 mg m⁻³) in October and November and were 0.08 mg m⁻³ further offshore increasing to 0.2 mg m⁻³ during December. Similarly in early spring from February to March, Chla was 0.08 to 0.2 mg m⁻³ on the east coast of the Bay.     

基于分区暗像元和Spline插值方法估算太湖气溶胶光学厚度

传统暗像元大气校正算法认为研究区域上空的气溶胶光学厚度呈均匀分布状态。对于Ⅱ类水体,尤其是气溶胶类型复杂的内陆湖区,暗像元算法的均匀性假设将不再适用。针对传统暗像元算法的不合理性,本研究将太湖湖区划分为9个子区域,每个子区域利用传统暗像元算法估算其气溶胶光学厚度,然后结合Spline插值算法获取整个太湖的气溶胶光学厚度信息,并以传统暗像元大气校正算法作为参照,探讨与分析分区暗像元算法的精度状况。通过本文的研究可知：气溶胶光学厚度是遥感大气校正的关键参数;在2003年10月28日,受西北风的影响,太湖上空的气溶胶光学厚度呈湖南低,湖北高的分布模式;分区暗像元大气校正算法获取的气溶胶光学厚度平均值为0.79,标准偏差为0.099,标准偏差与平均值的比值为12.58%,与传统暗像元算法相比,分区暗像元算法综合考虑了水体上空气溶胶光学厚度空间分布的不均匀性,进而有利于改善大气校正的精度。     

New strategy to improve estimation of diffuse attenuation coefficient for highly turbid inland waters

The diffuse attenuation coefficient, K_d(λ), is an important water optical property. Detection of K_d(λ) by means of remote sensing can provide significant assistance in understanding water environment conditions and many biogeochemical processes. Even when existing algorithms exhibit good performance in clear open ocean and turbid coastal waters, accurate quantification of highly turbid inland water bodies can still be a challenge due to their bio-optical complexity. In this study, we examined the performance of two typical pre-existing K_d(490) models in inland water bodies from Lake Taihu, Lake Chaohu, and the Three Gorges Reservoir in China. On the basis of water optical classification, new K_d(490) models were developed for these waters by means of the support vector machine approach. The obtained results showed that the two pre-existing K_d(490) models presented relatively large errors by comparison with the new models, with mean absolute percentage error (MAPE) values above ~30%. More importantly, among the new models, type-specific models generally outperformed the aggregated model. For water classified as Type 1 + Type 2, the type-specific model produced validation errors with MAPE = 16.8% and RMSE = 0.98 m^?1. For water classified as Type 3, the MAPE and RMSE of the type-specific model were found to be 18.8% and 1.85 m^?1, respectively. The findings in this study demonstrate that water classification (prior to algorithm development) is needed for the development of excellent K_d(490) retrieval algorithms, and the type-specific models thus developed are an important supplement to existing K_d(490) retrieval models for highly turbid inland waters.     

HYDROPT: A fast and flexible method to retrieve chlorophyll-a from multispectral satellite observations of optically complex coastal waters

We present a generic innovative algorithm for remote sensing of coastal waters that can deal with a large range of concentrations of chlorophyll-a, SPM and CDOM and their inherent optical properties. The algorithm is based on the exact solutions of the HYDROLIGHT numerical radiative transfer model to support retrieval in optically complex waters with varying sensor wide swath viewing geometry. The algorithm estimates the concentrations by minimizing the difference between observed and modeled reflectance spectra. The use of a look-up table and polynomial interpolation greatly reduces computation time, allowing operational and near-real time processing of large sets of satellite imagery. Because the remote sensing reflectance was tabulated as a function of in-water light absorption and scattering, rather than actual constituents concentrations, the algorithm can be applied with any definition of the specific inherent optical properties of CHL, SPM and CDOM. A statistical measure for the goodness-of-fit and the formal standard errors in the fitted concentrations are provided, thus producing error maps with each thematic chlorophyll image, often lacking in most applications of innovative algorithms. The performance of the algorithm is demonstrated for multispectral observations of the North Sea, a shallow coastal sea with large concentration gradients in SPM (due to resuspension) and CDOM (from riverine influx). The standard errors of estimated chlorophyll-a concentrations ranged between 0.5 and 3 (mg m^− 3) for mean concentrations between 2 and 20 (mg m^− 3), quite acceptable results for these optically complex waters.     

Water property monitoring and assessment for China's inland Lake Taihu from MODIS-Aqua measurements

We provide results of quantitative measurements and characterization for inland freshwater Lake Taihu from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. China's Lake Taihu, which is located in the Yangtze River delta in one of the world's most urbanized and heavily populated areas, contains consistently highly turbid waters in addition to frequent large seasonal algae blooms in various lake regions. Thus, satellite data processing requires use of the shortwave infrared (SWIR) atmospheric correction algorithm. Specifically for Lake Taihu, an iterative SWIR-based atmospheric correction algorithm has been developed and proven to provide reasonably accurate water-leaving radiance spectra data. Using MODIS-Aqua measurements, the blue-green algae bloom in Lake Taihu in 2007 has been studied in detail, demonstrating the importance and usefulness of satellite water color remote sensing for effectively monitoring and managing a bloom event.Seasonal and interannual variability, as well as spatial distributions, of lake water properties were studied and assessed using the MODIS-Aqua measurements from 2002 to 2008. Results show that overall waters in Lake Taihu are consistently highly turbid all year round, with the winter and summer as the most and least turbid seasons in the lake, respectively. Extremely turbid waters in the winter are primarily attributed to strong winter winds that lead to significant amounts of total suspended sediment (TSS) in the water column. In addition, MODIS-Aqua-measured water-leaving radiance at the blue band is consistently low in various bay regions in Lake Taihu, indicating high algae concentration in these regions. Climatological water property maps, including normalized water-leaving radiance spectra nL_w(λ), chlorophyll-a concentration, and water diffuse attenuation coefficient at the wavelength of 490 nm (K_d(490)), are derived from all MODIS-Aqua data from 2002 to 2008 for Lake Taihu, showing overall spatial distribution features for the lake water property.     

Measurements and analysis of in situ multi-angle reflectance of turbid inland water: a case study in Meiliang Bay,Taihu Lake,China

The bidirectional reflectance properties of the anisotropic light field above the water surface are important for a range of applications. The bidirectional reflectance distribution function of oceanic waters has been well characterized but there is a lack of information for turbid inland waters. In addition, there is a lack of bidirectional reflectance data measured in turbid inland waters partially due to the difficulty in collecting in situ water-surface multi-angle remote-sensing reflectance data. To facilitate bidirectional reflectance studies of turbid inland waters using in situ multi-angular reflectance data, we have designed and developed a simple hand-held 3D positioning pole to position the spectrometer optical fibre probe and a specific method to collect the multi-angular reflectance data above the water surface with this pole. Using this device, we collected multi-angular reflectance data in Meiliang Bay, Taihu Lake, China, and analysed the uncertainties in this method. We analysed the bidirectional distribution characteristics of the data, and compared the findings to those in the literature. Both uncertainty analysis and bidirectional distribution characteristics analysis showed that our method is effective in collecting multi-angular reflectance above the water surface and can be applied to validate bidirectional correction models in the future.     

A neural-network model to retrieve CDOM absorption from in situ measured hyperspectral data in an optically complex lake: Lake Taihu case study

Coloured dissolved organic matter (CDOM) is an important water component that affects water colour and ecological environment under water. The remote estimation of CDOM is always a challenge in the field of water-colour remote sensing owing to its weak signal. To further study the CDOM-retrieval approach, field experiments, including water-quality analysis and spectral measurements, were carried out in Lake Taihu waters from 8 to 21 November 2007. On the foundation of analysing water-inherent optical properties, sensitive spectral factors were selected, and then neural-network models were established for retrieving CDOM. The results show that the model with 10 nodes in the hidden layer performs best, yielding a correlation coefficient (R) of 0.887 and a root-mean-square error of 0.156 m^?1. Meanwhile, the predictive errors of the model developed here and the previously proposed algorithms were compared with each other. The mean value of the relative error of the former is 12.8% (standard deviation of 29.9%), and is much lower than its counterpart of other models, which indicates that the developed model has a higher accuracy for CDOM retrieval in Lake Taihu waters. Meanwhile, other datasets collected at different times were also imported into the model for applicability analysis; the derived errors suggest a relatively good performance of the model. This research firstly explores the CDOM retrieval in optically complex lake waters, and the corresponding findings support a technical framework for accurately extracting CDOM information in Lake Taihu waters, based on an adequate understanding of water optical properties.     

Multispectral remote-sensing algorithms for particulate organic carbon (POC): The Gulf of Mexico

To greatly increase the spatial and temporal resolution for studying carbon dynamics in the marine environment, we have developed remote-sensing algorithms for particulate organic carbon (POC) by matching in situ POC measurements in the Gulf of Mexico with matching SeaWiFS remote-sensing reflectance. Data on total particulate matter (PM) as well as POC collected during nine cruises in spring, summer and early winter from 1997-2000 as part of the Northeastern Gulf of Mexico (NEGOM) study were used to test algorithms across a range of environments from low %POC coastal waters to high %POC open-ocean waters. Finding that the remote-sensing reflectance clearly exhibited a peak shift from blue-to-green wavelengths with increasing POC concentration, we developed a Maximum Normalized Difference Carbon Index (MNDCI) algorithm which uses the maximum band ratio of all available blue-to-green wavelengths, and provides a very robust estimate over a wide range of POC and PM concentrations (R² = 0.99, N = 58). The algorithm can be extrapolated throughout the region of shipboard sampling for more detailed coverage and analysis.     

Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands

Bio-optical algorithms for remote estimation of chlorophyll-a concentration (Chl) in case-1 waters exploit the upwelling radiation in the blue and green spectral regions. In turbid productive waters other constituents, that vary independently of Chl, absorb and scatter light in these spectral regions. As a consequence, the accurate estimation of Chl in turbid productive waters has so far not been feasible from satellite sensors. The main purpose of this study was to evaluate the extent to which near-infrared (NIR) to red reflectance ratios could be applied to the Sea Wide Field-of-View Sensor (SeaWiFS) and the Moderate Imaging Spectrometer (MODIS) to estimate Chl in productive turbid waters. To achieve this objective, remote-sensing reflectance spectra and relevant water constituents were collected in 251 stations over lakes and reservoirs with a wide variability in optical parameters (i.e. 4 ≤ Chl ≤ 240 mg m^− 3; 18 ≤ Secchi disk depth ≤ 308 cm). SeaWiFS and MODIS NIR and red reflectances were simulated by using the in-situ hyperspectral data. The proposed algorithms predicted Chl with a relative random uncertainty of approximately 28% (average bias between − 1% and − 4%). The effects of reflectance uncertainties on the predicted Chl were also analyzed. It was found that, for realistic ranges of R_rs uncertainties, Chl could be estimated with a precision better than 40% and an accuracy better than ± 35%. These findings imply that, provided that an atmospheric correction scheme specific for the red-NIR spectral region is available, the extensive database of SeaWiFS and MODIS images could be used to quantitatively monitor Chl in turbid productive waters.     

Evaluation of four MERIS atmospheric correction algorithms in Lake Kasumigaura,Japan

Accurate atmospheric correction for turbid inland waters remains a significant challenge. Several atmospheric correction algorithms have been proposed to address this issue, but their performance is unclear in regard to Asian lakes, some of which have extremely high turbidity and different inherent optical properties from lakes in other continents. Here, four existing atmospheric correction algorithms were tested in Lake Kasumigaura, Japan (an extremely turbid inland lake), using in situ water-leaving reflectance and concurrently acquired medium resolution imaging spectrometer (MERIS) images. The four algorithms are (1) GWI (the standard Gordon and Wang algorithm with an iterative process and a bio-optical model) (2) MUMM (Management Unit of the North Sea Mathematical Models); (3) SCAPE-M (Self-Contained Atmospheric Parameters Estimation for MERIS Data) and (4) C2WP (Case-2 Water Processor). The results show that all four atmospheric correction algorithms have limitations in Lake Kasumigaura, even though SCAPE-M and MUMM gave acceptable accuracy for atmospheric correction in several cases (relative errors less than 30% for the 2006 and 2008 images). The poor performance occurred because the conditions in Lake Kasumigaura (i.e. the atmospheric state and/or turbidity) did not always meet the assumptions in each atmospheric correction algorithm (e.g. in 2010, the relative errors ranged from 42% to 83%). These results indicate that further improvements are necessary to address the issue of atmospheric correction for turbid inland waters such as Lake Kasumigaura, Japan.     

Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters

Mapping of total suspended matter concentration (TSM) can be achieved from space-based optical sensors and has growing applications related to sediment transport. A TSM algorithm is developed here for turbid waters, suitable for any ocean colour sensor including MERIS, MODIS and SeaWiFS. Theory shows that use of a single band provides a robust and TSM-sensitive algorithm provided the band is chosen appropriately. Hyperspectral calibration is made using seaborne TSM and reflectance spectra collected in the southern North Sea. Two versions of the algorithm are considered: one which gives directly TSM from reflectance, the other uses the reflectance model of Park and Ruddick (2005) to take account of bidirectional effects.Applying a non-linear regression analysis to the calibration data set gave relative errors in TSM estimation less than 30% in the spectral range 670-750 nm. Validation of this algorithm for MODIS and MERIS retrieved reflectances with concurrent in situ measurements gave the lowest relative errors in TSM estimates, less than 40%, for MODIS bands 667 nm and 678 nm and for MERIS bands 665 nm and 681 nm. Consistency of the approach in a multisensor context (SeaWiFS, MERIS, and MODIS) is demonstrated both for single point time series and for individual images.     

Spectral variations in the near-infrared ocean reflectance

The optical properties of natural waters beyond the visible range, in the near-infrared (NIR, 700-900 nm), have received little attention because they are often assumed to be mostly determined by the large absorption coefficient of pure water, and because of methodological difficulties. It is now growingly admitted that the NIR represents a potential optical source of unambiguous information about suspended sediments in turbid waters, thence the need for better understanding the NIR optical behaviour of such waters. It has recently been proposed (Ruddick et al., Limnology and Oceanography. 51, 1167-1179, 2006) that the variability in the shape of the surface ocean reflectance spectrum in the NIR is negligible in turbid waters. In the present study, we show, based on both in situ and remote sensing data, that the shape of the ocean reflectance spectrum in the NIR does vary in turbid to extremely turbid waters. Space-borne ocean reflectance data were collected using 3 different sensors (SeaWiFS, MODIS/Aqua and MERIS) over the Amazon, Mackenzie and Rio de la Plata turbid river plumes during extremely clear atmospheric conditions so that reliable removal of gas and aerosol effects on reflectance could be achieved. In situ NIR reflectance data were collected in different European estuaries where extremely turbid waters were found. In both data sets, a flattening of the NIR reflectance spectrum with increasing turbidity was observed. The ratio of reflectances at 765 nm and 865 nm, for instance, varied from ca. 2 down to 1 in our in situ data set, while a constant value of 1.61 had been proposed based on theory in a previous study. Radiative transfer calculations were performed using a range of realistic values for the seawater inherent optical properties, to determine the possible causes of variations in the shape of the NIR reflectance spectrum. Based on these simulations, we found that the most significant one was the gradual increase in the contribution of suspended sediments to the color of surface waters, which often leads to the flattening of the reflectance spectrum. Changes in the scattering and absorption properties of particles also contributed to variations in the shape of the NIR surface ocean reflectance spectrum. The impact of such variations on the interpretation of ocean color data is discussed.     

Bio‐optical model with optimal parameter suitable for Taihu Lake in water colour remote sensing

Sixty‐seven samplings were collected, almost covering all over the Taihu Lake, in one campaign in October 2004. At each station, the backscattering coefficients and the field spectra were measured in situ, respectively, with a HS‐6 and a FieldSpec 931 spectroradiometer (ASD Inc.). Almost concurrently, water samples were fetched with Niskin water‐fetching equipment and then returned to the laboratory for concentration and absorption measurement. The whole lake was divided into different areas according to some indexes. Three models were used to calculate remote sensing reflectance R _rsc for the waters where the in situ remote sensing reflectance R _rsm was beyond the bottom effect, which was considered as optically deep waters. By comparison of R _rsc and R _rsm, the best model suitable for optically deep waters in Taihu Lake, together with its optimal experiential parameter, were selected and developed, which was very important and helpful to develop a universal model to estimate accurately remote sensing reflectance for the whole lake in the next step.     

Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager

An aerosol retrieval algorithm for the first Geostationary Ocean Color Imager (GOCI) to be launched in March 2010 onboard the Communication, Ocean, and Meteorological Satellite (COMS) is presented. The algorithm retrieves aerosol optical depth (AOD), fine-mode fraction (FMF), and aerosol type in 500 m × 500 m resolution. All the products are retrieved over clear water which is defined by surface reflectance ratio between 640 nm and 860 nm (SRR) less or equal to 2.5, while only AOD is retrieved over turbid water (SRR > 2.5) due to high surface reflectance. To develop optimized algorithm for the target area of GOCI, optical properties of aerosol are analyzed from extensive observation of AERONET sunphotometers to generate lookup table. Surface reflectance of turbid water is determined from 30-day composite of Rayleigh- and gas corrected reflectance. By applying the present algorithm to MODIS top-of-the atmosphere reflectance, three different aerosol cases dominated by anthropogenic aerosol contains black carbon (BC), dust, and non-absorbing aerosol are analyzed to test the algorithm. The algorithm retrieves AOD, and size information together with aerosol type which are consistent with results inferred by RGB image in a qualitative way. The comparison of the retrieved AOD with those of MODIS collection 5 and AERONET sunphotometer observations shows reliable results. Especially, the application of turbid water algorithm significantly increases the accuracy in retrieving AOD at Anmyon station. The sensitivity study between MODIS and GOCI instruments in terms of relative sensitivity and scattering angle shows promising applicability of the present algorithm to future GOCI measurements.     

Two-component modeling of the optical properties of a diatom bloom in the Southern Ocean

Diatom cells have distinctive optical characteristics, originating from their relatively large cell size, fucoxanthin content and silica cell wall. It has been proposed that diatom-dominated phytoplankton blooms can be identified by optical remote sensing and that specifically tuned chlorophyll and primary production algorithms should be applied in regions where these blooms are present. However there have been few studies on how the optical properties of diatom blooms change as they progress from active growth to senescence, and it is unlikely that measurements on laboratory cultures encompass the full range of physiological states found in natural waters. We have therefore examined the inherent optical properties (IOPs) of the waters around the island of South Georgia at the end of the spring diatom bloom. Considerable variability was found in the relationships between the inherent optical properties and analytically determined chlorophyll a concentrations even in the surface layer, which meant that the usual bio-optical assumptions for Case 1 waters did not apply. To account for this variability, phytoplankton absorption and scattering were modeled as a two-component mixture, with the components representing actively growing and senescent material. The specific inherent optical properties of the two components were derived by linear regression of total IOPs against chlorophyll concentration and a fraction of the suspended mineral concentration. These specific IOPs were used to develop radiative transfer models of diatom blooms in varying stages of growth and senescence. Remote sensing reflectances calculated using this technique confirmed the tendency of the standard algorithms employed in SeaWiFS, MODIS and MERIS data processing to under-estimate near-surface chlorophyll concentrations in diatom blooms. However the inclusion of increasing proportions of senescent material had a significant effect on algorithm performance only at chlorophyll concentrations below 10 mg m^− 3. Optical depths predicted by the model around South Georgia were 9 +/− 2 m at 512 nm, indicating that a large fraction of the phytoplankton biomass was located below the depth from which the remote sensing signals originated.