滩海水深遥感反演模型应用研究

研究船舶准确探测滩海水深的信息问题。由于海水深度不同,散射特征和输入均有信号衰减,影响准确探测定位。传统探测方法利用船载声纳测深方法测量难度大,更新速度慢。为了弥补不足,提出了遥感探测滩海水深的方法,利用遥感的水深探测技术可快速获取大面积的滩海水深信息,根据遥感反演水深的基本原理,采用我国渤海滩海光学数据和实测水深资料,结合半理论半经验模型和统计相关模型,进行水深探测模型仿真,得到平均标准误差为2.7m。实验结果表明了方法的快速有效,可为渤海湾滩浅海水深地形研究提供可靠的依据。     

Assessment of different models for bathymetry calculation using SPOT multispectral images in a high-turbidity area: the mouth of the Guadiana Estuary

Periodic calculation of coastal bathymetries can show the evolution of geomorphological features in active areas such as mesotidal estuary mouths. Bathymetries in shallow coastal areas have been addressed mainly by two technologies, lidar and optical remote sensing. Lidar provides good accuracy, but is an expensive technique, requiring planned flights for each region and dates of interest. Optical remote sensing acquires images periodically but its results are limited by water turbidity. Here we use a lidar bathymetry to compare different bathymetry computation methods using a SPOT optical image from a nearby date. Three statistical models (green-band, PCA correlations, and GLM) were applied to obtain mathematical expressions to estimate bathymetry from that image: all gave errors lower than 1 m in an area with depths ranging from 0 to 6 m. These algorithms were then applied to images from three different dates, correcting the effects caused by different tidal and atmospheric conditions. We show how this allows the study of morphological changes. We discuss the accuracy obtained with respect to the reference bathymetry (0.9 m on average, but less than 0.5 m in low-turbidity areas), the effects of the turbidity on our estimations, and compare both with previously published results. The results show that this approach is effective and allows identification of known features of coastal dynamics, and thus it would be an important step towards short-term bathymetry monitoring based on optical satellite remote sensing.     

An airborne sensor for primary productivity and related parameters of coastal waters and large water bodies

In this letter, details of an active airborne sensor designed primarily for remote sensing of ocean productivity and related parameters like bathymetry of coastal waters and large water bodies are presented. The sensor complements passive oceanographic sensors on satellites by its capability of detailed mesoscale, regional coverage. This is the first payload combining the sensing of bathymetry and fluorescence together in one package. The design of the sensor based on theory, the optics and electronics are described. An Nd:YAG laser with l000mJ at 1064nm and 450 mJ at 532 nm is used. Photomultipliers for the bathymetry channels and a position sensitive microchannel plate photomultiplier tube array for the fluorosensor channels form part of the detection system. RC coupling networks are used to separate the active signal from passive ambient light. The maximum depth resolvable with an accuracy of 0-5m is 948m at night in clear water and 66-3 m in moderately turbid water at a flight height of 150 m. The minimum detectable concentration of chlorophyll a is 01 mgm^-3. The auxiliary sensors required are also listed. The payload is in the 500 kg class and can be flown at an altitude of 150 m on an HS-748 aircraft modified for ‘ditching’ at sea.     

Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems

Large areas of the world's coastal marine environments remain poorly characterized because they have not been mapped with sufficient accuracy and at spatial resolutions high enough to support a wide range of societal needs. Expediting the rate of seafloor mapping requires the collection of multi-use datasets that concurrently address hydrographic charting needs and support decision-making in ecosystem-based management. While active optical and acoustic sensors have previously been compared for the purpose of hydrographic charting, few studies have evaluated the performance and cost effectiveness of these systems for providing benthic habitat maps. Bathymetric and intensity data were collected in shallow water (< 50 m depth) coral reef ecosystems using two conventional remote sensing technologies: (1) airborne Light Detection and Ranging (LiDAR), and (2) ship-based multibeam (MBES) Sound Navigation and Ranging (SoNAR). A comparative assessment using a suite of twelve metrics demonstrated that LiDAR and MBES were equally capable of discriminating seafloor topography (r = > 0.9), although LiDAR depths were found to be consistently shallower than MBES depths. The intensity datasets were not significantly correlated at a broad 4 × 5 km spatial scale (r = − 0.11), but were moderately correlated in flat areas at a fine 4 × 500 m spatial scale (r = 0.51), indicating that the LiDAR intensity algorithm needs to be improved before LiDAR intensity surfaces can be used for habitat mapping. LiDAR cost 6.6% less than MBES and required 40 fewer hours to map the same study area. MBES provided more detail about the seafloor by fully ensonifying high-relief features, by differentiating between fine and coarse sediments and by collecting data with higher spatial resolutions. Surface fractal dimensions and fast Fourier transformations emerged as useful methods for detecting artifacts in the datasets. Overall, LiDAR provided a more cost effective alternative to MBES for mapping and monitoring shallow water coral reef ecosystems (< 50 m depth), although the unique advantages of MBES may make it a more appropriate choice for answering certain ecological or geological questions requiring very high resolution data.     

The role of digital bathymetry in mapping shallow marine vegetation from hyperspectral image data

Hyperspectral remote sensing is a proven technology for measurement of coastal ocean colour, including sea‐bed mapping in optically shallow waters. Using hyperspectral imagery of shallow (<15 m deep) sea bed acquired with the Compact Airborne Spectrographic Imager (CASI‐550), we examined how changes in the spatial resolution of bathymetric grids, created from sonar data (echosounding) and input to conventional image classifiers, affected the accuracy of distributional maps of invasive (Codium fragile ssp. tomentosoides) and native (kelp) seaweeds off the coast of Nova Scotia, Canada. The addition of a low‐resolution bathymetric grid, interpolated from soundings by the Canadian Hydrographic Service, improved the overall classification accuracies by up to ～10%. However, increasing the bathymetric resolution did not increase the accuracy of classification maps produced with the supervised (Maximum Likelihood) classifier as shown by a slightly lower accuracy (2%) when using an intermediate‐resolution bathymetric grid interpolated from soundings with a recreational fish finder. Supervised classifications using the first three eigenvectors from a principal‐components analysis were consistently more accurate (by at least 27%) than unsupervised (K‐means classifier) schemes with similar data compression. With an overall accuracy of 76%, the most reliable scheme was a supervised classification with low‐resolution bathymetry. However, the supervised approach was particularly sensitive, and variations in accuracy of 2% resulted in overestimations of up to 53% in the extent of C. fragile and kelp. The use of a passive optical bathymetric algorithm to derive a high‐resolution bathymetric grid from the CASI data showed promise, although fundamental differences between this grid and those created with the sonar data limited the conclusions. The bathymetry (at any spatial resolution) appeared to improve the accuracy of the classifications both by reducing the confusion among the spectral classes and by removing noise in the image data. Variations in the accuracy of depth estimates and inescapable positional inaccuracies in the imagery and ground data largely accounted for the observed differences in the classification accuracies. This study provides the first detailed demonstration of the advantages and limitations of integrating digital bathymetry with hyperspectral data for the mapping of benthic assemblages in optically shallow waters.     

Determination of shallow water depth using optical satellite images

The Penghu archipelago comprises 64 basaltic volcanic isles lying on the Taiwan Strait between mainland China and Taiwan. The water around and within these isles is shallow and poses considerable difficulty in echo sounding detection for bathymetry. Most existing bathymetry data around such areas are in water depths of greater than 5 m. Therefore, when the water depth is less than 5 m the data tend to be over-extrapolated. In this study, a remote sensing method provides a more effective approach to recording shallow water depths compared to traditional soundings using multitemporal images collected by optical/near-infrared sensors from SPOT satellites. This method employs optical energy reflections to obtain the water depth. In this study, we made several improvements wherein a relative atmosphere correction technique was used to calibrate two images within a similar atmospheric condition. We then compared the satellite images acquired from different dates to obtain the local water attenuation coefficient of sunlight. Finally, we developed a means to estimate the water attenuation coefficient and bottom reflectance which will satisfy the two parameters across the study area. Our results show a high-resolution map of shallow bathymetry for the Penghu archipelago and revealed a maximum depth of about 20 m. This study provides an efficient approach for shallow bathymetry retrieval. Many detailed features revealed by this approach may contribute to further geological research and developments in harbour and coastal engineering.     

Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters—a Baltic Sea case study

Quantitative analysis of coastal marine benthic communities enables to adequately estimate the state of coastal marine environment, provide better evidence for environmental changes and describe processes that are conditioned by anthropogenic forces. Remote sensing could provide a tool for mapping bottom vegetation if the substrates are spectrally resolvable. We measured reflectance spectra of green (Cladophora glomerata), red (Furcellaria lumbricalis), and brown (Fucus vesiculosus) macroalgae and used a bio-optical model in estimating whether these algae distinguish optically from each other, from sandy bottom or deep water in turbid water conditions of the Baltic Sea. The simulation was carried out for three different water types: (1) CDOM-rich coastal water, (2) coastal waters not directly impacted by high CDOM discharge from rivers but with high concentration of cyanobacteria, (3) open Baltic waters. Our modelling results indicate that the reflectance spectra of C. glomerata, F. lumbricalis, F. vesiculosus differ from each other and also from sand and deep water reflectance spectra. The differences are detectable by remote sensing instruments at spectral resolution of 10 nm and SNR better than 1000:1. Thus, the lowest depth limits where the studied macroalgae grow do not exceed the depth where such remote sensing instruments could potentially detect the spectral differences between the studied species.     

Quantitative mapping of arid alluvial fan surfaces using field spectrometer and hyperspectral remote sensing

Mapping and dating of arid and semi-arid alluvial fans are of great importance in many Quaternary studies. Yet the most common mapping method of these features is based on visual, qualitative interpretation of air-photos. In this study we examine the feasibility of mapping arid alluvial surfaces by using airborne hyperspectral reflective remote sensing methodology. This technique was tested on Late Pleistocene to Holocene alluvial fan surfaces located in the hyperarid southern Arava valley, Israel. Results of spectral field measurements showed that the surface reflectance is controlled by two main surficial processes, which are used as relative age criteria: the degree of desert pavement development (gravel coverage %) controls the absorption feature depths, while the rock coating development influences significantly the overall reflectance of the surface, but its effect on the absorption feature depths is limited. We show that as the percent of the surface covered by gravels increases, the absorption feature depth of the common gravels, in this case carbonate at 2.33 μm, increases as well; whereas the absorption features depth of the fine particle in-between the gravels, decrease (hydroxyl and ferric absorption features at 2.21 μm, and 0.87 μm, respectively), as the fines are removed from the surface. Using these correlations we were able to map the surface gravel coverage (%) on the entire alluvial fan, by calculating the gravel coverage (%) in each pixel of the hyperspectral image. The prediction of gravel coverage (%) is with accuracy of ± 15% (e.g. gravel coverage of 50% can be predicted to be 35% to 65%). Using extensive accuracy assessment data, we show that the spectral based mapping maintained high accuracy degree (R² = 0.57 to 0.83). The quantitative methodology developed in this study for mapping alluvial surfaces can be adapted for other surfaces and piedmonts throughout the arid regions of the world.     

Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia)

Understanding, monitoring and modelling attributes of seagrass biodiversity, such as species composition, richness, abundance, spatial patterns, and disturbance dynamics, requires spatial information. This work assessed the accuracy of commonly available airborne hyper-spectral and satellite multi-spectral image data sets for mapping seagrass species composition, horizontal horizontal-projected foliage cover and above-ground dry-weight biomass. The work was carried out on the Eastern Banks in Moreton Bay, Australia, an area of shallow and clear coastal waters, containing a range of seagrass species, cover and biomass levels. Two types of satellite image data were used: Quickbird-2 multi-spectral and Landsat-5 Thematic Mapper multi-spectral. Airborne hyper-spectral image data were acquired from a CASI-2 sensor using a pixel size of 4.0 m. The mapping was constrained to depths shallower than 3.0 m, based on past modelling of the separability of seagrass reflectance signatures at increasing water depths. Our results demonstrated that mapping of seagrass cover, species and biomass to high accuracy levels (> 80%) was not possible across all image types. For each parameter mapped, airborne hyper-spectral data produced the highest overall accuracies (46%), followed by Quickbird-2 and then Landsat-5 Thematic Mapper. The low accuracy levels were attributed to the mapping methods and difficulties in matching locations on image and field data sets. Accurate mapping of seagrass cover, species composition and biomass, using simple approaches, requires further work using high-spatial resolution (< 5 m) and/or hyper-spectral image data. Further work is required to determine if and how the seagrass maps produced in this work are suitable for measuring attributes of seagrass biodiversity, and using these data for modelling floral and fauna biodiversity properties of seagrass environments, and for scaling-up seagrass ecosystem models.     

Mapping bottom depth and albedo in coastal waters of the South China Sea islands and reefs using Landsat TM and ETM+ data

Optical models for the retrieval of shallow water bottom depth and albedo using multispectral data usually require in situ water depth data to tune the model parameters. In the South China Sea (SCS), however, such in situ data are often lacking or obsolete (perhaps from half a century ago) for most coastal waters around its islands and reefs. Here, we combine multispectral data collected by MODIS and Landsat to estimate bottom depth and albedo for four coral reef regions in the SCS, with results partially validated by some scarce in situ data. The waters in these remote regions are oligotrophic whose optical properties can be well derived from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements when the waters are optically deep. The MODIS-derived optical properties are used to estimate the water column attenuation to the Landsat measurements over shallow waters, thus eliminating the requirement of model tuning using field measured water depths. The model is applied to four Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images covering Pratas Atoll, Woody Island, Scarborough Shoal, and North Danger Reefs. The retrieved bathymetry around Pratas Atoll and North Danger Reefs are validated with some in situ data between 1 and 25 m. The relative difference and root mean square difference between the two measurements were 17% and 1.6 m, for Pratas Atoll and 11% and 1.1 m for North Danger Reefs, respectively. These results suggest that the approach developed here may be extended to other shallow, clear waters in the SCS.     

Mapping coloured dissolved organic matter concentration in coastal waters

Optical properties of the Baltic Sea are dominated by coloured dissolved organic matter (CDOM). High concentration of CDOM is probably one of the reasons why standard chlorophyll-retrieval algorithms fail badly in the Baltic Sea. Our aim was to test can CDOM be mapped by remote sensing instruments in coastal waters of relatively CDOM-rich environments like the Baltic Sea. The results show that sensors with high radiometric resolution, such as Advanced Land Imager (ALI), can be used for mapping CDOM in a wide concentration range. The ALI image also showed that optical properties of coastal waters are extremely variable. CDOM concentration may vary 4–5-fold in two adjacent 30 m pixels. This indicates a need for relatively high spatial resolution (30 m or less) remote sensing data in monitoring coastal environments.     

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.     

Bottom influence on the estimation of chlorophyll concentration in water from remotely sensed data

Remote sensing algorithms that provide for the computation of chlorophyll concentration in surface waters are an important tool for the synoptic mapping of ocean optics and primary productivity. The remote sensing of chlorophyll concentration in Case 1 coastal waters ( waters whose optics are primarily controlled by biologic content) could present a problem due to the presence of the bottom itself and the possible contribution of bottom reflected light to the upwelling light stream ultimately ‘ seen’ by a remote sensor. The present work analyzes the impact on the estimation of chlorophyll concentration when a bottom is present. Multiple plots are used to exhibit and quantify the effects the presence of a bottom and bottom reflected light have on chlorophyll computation algorithms. Further, a new water type definition is proposed and threshold optical depths are calculated that provide values at which the presence of a benthic interface and bottom reflected light no longer contribute to the remotely observed upwelling light stream.     

Shallow water bathymetry using integrated airborne multi-spectral remote sensing

An integrated low cost airborne multi-spectral remote sensing system is described and evaluated for remote sensing for shallow water bathymetry. The system consists of: two 35mm motor driven reconnaissance cameras using colour and colour infrared film. Three optically filtered (including removable internal IR cut-off filters), electronically shuttered CCD progressive scan cameras (Sony XC-7500) integrated into an airborne direct digital recording system using a PC processor, a 32-bit RGB analogue to digital conversion card and Zip disk storage. Two CCD based imaging spectrometers providing approximately 10nm bandwidth spectral data across the CCD spectrum (400nm to 1000nm). These CCD cameras were used with a variable interference filter fixed in front of the sensor surface. This provided a 'rainbow' image of the ground varying across the image from 400nm to 700nm (visible) and 700nm to 1000nm. Field studies were undertaken to evaluate the performance of digital multi-spectral (DMV) imagery, supplementary reconnaissance photography (SRP) and VIFIS imaging spectrometry for mapping shallow water bathymetry. The results indicate good performance in shallow water and suggest that with further refinement the system could be used to give a quick comprehensive estimate of shallow water depths     

Remote sensing techniques in estuaries and coastal zones an update

This paper reviews briefly the current state of the use of remote sensing in estuaries and coastal waters. This covers the established uses, the recent developments in relation to hyperspectral imaging systems (imaging spectrometers), interferometric SAR and lidar bathymetry. The opportunities that are expected to occur soon, as 1-m resolution space-flown systems become available, are also discussed.     

Chlorophyll retrieval with MERIS Case-2-Regional in perialpine lakes

Semi-analytical remote sensing applications for eutrophic waters are not applicable to oligo- and mesotrophic lakes in the perialpine area, since they are insensitive to chlorophyll concentration variations between 1 and 10 mg/m³. The neural network based Case-2-Regional algorithm for MERIS was developed to fill this gap, along with the ICOL adjacency effect correction algorithm. The algorithms are applied to a collection of 239 satellite images from 2003-2008, and the results are compared to experimental and official water quality data collected in six perialpine lakes in the same period. It is shown that remote sensing estimates can provide an adequate supplementary data source to in situ data series of the top 5 m water layer, provided that a sufficient number of matchups for a site specific maximum temporal offset are available.     

Detecting the red tide algal blooms from satellite ocean color observations in optically complex Northeast-Asia Coastal waters

Over the last few decades, the coastal regions throughout the world have experienced incidences of algal blooms, which are harmful or otherwise toxic because of their potential threat to humans as well as marine organisms, owing to accelerated eutrophication from human activities and certain oceanic processes. Previous studies have found that correct identification of these blooms remains a great challenge with the standard bio-optical algorithms applied to satellite ocean color data in optically complex coastal waters containing high concentrations of the interfered dissolved organic and particulate inorganic materials. Here a new method called the red tide index (RI) is presented which is capable of identifying potential areas of harmful algal blooms (HABs) from SeaWiFS ocean color measurements representing the typical Case-2 water environments off the Korean and Chinese coasts. The RI method employs the water-leaving radiances (L_w), collected from in-situ radiometric measurements of three SeaWiFS bands centered at 443 nm, 510 nm and 555 nm, to achieve derivation of indices that are then related to absorbing characteristics of harmful algae (i.e., L_w at 443 nm) from which a best fit with a cubic polynomial function with correlation coefficient of R² = 0.91 is obtained providing indices of higher ranges for HABs and lower and slightly reduced ranges for turbid and non-bloom waters. Similar indices derived from the use of remote sensing reflectance (R_rs), normalized water-leaving radiance (nL_w) and combination of both are found rather inadequate to characterize the variability of the encountered bloom. In order to quantify the HABs in terms of chlorophyll (Chl), an empirical relationship is established between the RI and in-situ Chl in surface waters from about 0.4-71 mg m^− 3, which yields a Red tide index Chlorophyll Algorithm (RCA) based on an exponential function with correlation coefficient R² = 0.92. The established methods were extensively tested and compared with the performances of standard Ocean Chlorophyll 4 (OC4) algorithm and Local Chlorophyll Algorithm (LCA) using SeaWiFS images collected from typical red tide waters of Korean South Sea (KSS), East China Sea (ECS), Yellow Sea (YS) and Bohai Sea (BS) during 1999-2002. The standard spectral ratio algorithms, the OC4 and LCA, yielded large errors in Chl retrievals for coastal areas, besides providing false information about the encountered HABs in KSS, ECS, YS and BS waters. On the contrary, the RI coupled with the standard spectral ratios yielded comprehensive information about various ranges of algal blooms, while RCA Chl showing a good agreement with in-situ data led to enhanced understanding of the spatial and temporal variability of the recent HAB occurrences in high scattering and absorbing waters off the Korean and Chinese coasts.     

Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters

Increased frequency and extent of potentially harmful blooms in coastal and inland waters world-wide require the development of methods for operative and reliable monitoring of the blooms over vast coastal areas and a large number of lakes. Remote sensing could provide the tool. An overview of the literature in this field suggests that operative monitoring of the extent of some types of blooms (i.e. cyanobacteria) is relatively straightforward. Operative monitoring of inland waters is currently limited to larger lakes or using airborne and hand-held remote sensing instruments as there are no satellite sensors with sufficient spatial resolution to provide daily coverage. Extremely high spatial and vertical variability in biomass during blooms of some phytoplankton species and the strong effects of this on the remote sensing signal suggest that water sampling techniques and strategies have to be redesigned for highly stratified bloom conditions, especially if the samples are collected for algorithm development and validation of remote sensing data. Comparing spectral signatures of different bloom-forming species with the spectral resolution available in most satellites and taking into account variability in optical properties of different water bodies suggests that developing global algorithms for recognizing and quantitative mapping of (harmful) algal blooms is questionable. On the other hand some authors cited in the present paper have found particular cases where satellites with coarse spectral and spatial resolution can be used to recognize phytoplankton blooms even at species level. Thus, the algorithms and methods to be used depend on the optical complexity of the water to which they will be applied. The aim of this paper is to summarize different methods and algorithms available in an attempt to assist in selecting the most appropriate method for a particular site and problem under investigation.     

Carbon dioxide of Pu`u`O`o volcanic plume at Kilauea retrieved by AVIRIS hyperspectral data

A remote sensing approach permits for the first time the derivation of a map of the carbon dioxide concentration in a volcanic plume. The airborne imaging remote sensing overcomes the typical difficulties associated with the ground measurements and permits rapid and large views of the volcanic processes together with the measurements of volatile components exolving from craters. Hyperspectral images in the infrared range (1900-2100 nm), where carbon dioxide absorption lines are present, have been used. These images were acquired during an airborne campaign by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over the Pu`u` O`o Vent situated at the Kilauea East Rift zone, Hawaii. Using a radiative transfer model to simulate the measured up-welling spectral radiance and by applying the newly developed mapping technique, the carbon dioxide concentration map of the Pu`u` O`o Vent plume were obtained. The carbon dioxide integrated flux rate were calculated and a mean value of 396 ± 138 t d^− 1 was obtained. This result is in agreement, within the measurements errors, with those of the ground measurements taken during the airborne campaign.     

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.