Estimating phytoplankton biomass in coastal waters of Alaska using airborne remote sensing

Empirical airborne remote-sensing relationships were examined to estimate chlorophyll a concentration in the first optical depth (chl_FOD) of coastal waters of Afgonak/Kodiak Islands during July-August 2002. Band-ratio and spectral-curvature models were tested using satellite remote-sensing reflectance (R_rs(λ)) measurements. Additional shipboard and airborne R_rs(λ) data were also analysed to evaluate consistency of proposed chl_FOD-R_rs(λ) relationships. Validation of chlorophyll algorithms was performed using data collected in the northern-part of the Gulf of Alaska and Bering Sea during 1996, 2002, and 2003 cruises. Likewise, oceanographic conditions during the surveys were typified to interpret variability of chl_FOD fields. The SeaWiFS band-ratio algorithm OC2d was the most sensitive R_rs combination (R_rs(509)/R_rs(553)) to detect chl_FOD variability. Conversely, OC2a (R_rs(412)/R_rs(553)) had the lowest performance to derive chl_FOD values. No valid statistical regressions were established for spectral-curvature relationships in the blue spectrum (< 500 nm). Fertile waters (> 5 mg m^− 3) were preferentially located over shallow banks (∼50 m) and at the entrance of the bays. The approach used in this study to derive chl_FOD values could be universal for Alaskan coastal waters. However, chl_FOD-R_rs(λ) relationships must be calibrated locally for a given season.     

A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters

The empirical approach of remote sensing has a proven capability to provide timely and accurate information on inland and near-coastal transitional waters. This article gives a thorough review of empirical algorithms for quantitatively estimating a variety of parameters from space-borne, airborne and in situ remote sensors in inland and transitional waters, including chlorophyll-a, total suspended solids, Secchi disk depth (z _SD), turbidity, absorption by coloured dissolved organic matter (a _CDOM) and other parameters, for example, phycocyanin. Current remote-sensing instruments are also reviewed. The theoretical basis of the empirical algorithms is given using fundamental bio-optical theory of the inherent optical properties (IOPs). Bands, band ratios and band arithmetic algorithms that could be used to produce common biogeophysical products for inland/transitional waters are identified. The article discusses the potential role that empirical algorithms could play alongside more advanced model-based algorithms in the future of water remote sensing, especially for near real-time operational monitoring systems. The article aims to describe the current status of empirical remote sensing in inland and near-coastal transitional waters and provide a useful reference to workers. It does not cover ‘inversion’ algorithms.     

A multispectral remote sensing study of coastal waters off Vancouver Island

In March 1996, a multispectral aircraft survey of the coastal waters off Vancouver Island was carried out using a Compact Airborne Spectrographic Imager (CASI). This survey was combined with in situ measurements of water properties (phytoplankton composition, phytoplankton pigments, absorption spectra of phytoplankton, and concentration of dissolved organic carbon, or DOC). Comparison of the phytoplankton absorption data from this experiment with similar data from other regions shows that phytoplankton community has a significant impact on the spectral form and magnitude of absorption spectra, when normalized to unit chlorophyll-a. Concurrent measurements of in situ properties and aircraft data were obtained at eight stations. The in situ measurements of phytoplankton absorption and estimates of downwelling irradiance based on a clear-sky atmospheric-transmission model are used as inputs to a model of water-leaving irradiance. The modelled irradiances are compared with the remotely sensed values of water-leaving radiances. The observed differences between model and observation are used to evaluate the potential influence of DOC on water-leaving radiance. Practical difficulties of separating the phytoplankton signal from that of the coloured component of DOC (also known as yellow substance) are examined. Algorithms for estimation of the concentration of chlorophyll-a (the major phytoplankton pigment) can be based on their absorption or fluorescence properties. The distribution of chlorophyll-a in the study area is estimated using both these approaches, and possible causes for the observed discrepancies are discussed.     

Satellite remote sensing of surface oceanic fronts in coastal waters off west-central Florida

Two algorithms designed to detect deepwater oceanic features and arbitrary edge profiles were tuned to automatically delineate fronts in coastal waters off west-central Florida using satellite-derived sea surface temperature (SST), chlorophyll-a concentration (Chl), normalized water-leaving radiance (nL_w), and fluorescence line height (FLH) images during select periods in the spring and fall of 2004 and 2005. The dates correspond to recreational king mackerel, Scomberomorus cavalla, tournaments. A histogram-based algorithm was useful to detect coastal surface SST, nL_w, and FLH fronts, specifically. A gradient-based algorithm, with a smaller kernel box of 3 × 3 pixels, best identified nearshore (< 10 m depth) features in Chl images at the mouth of Tampa Bay, but was less effective for fronts farther offshore where gradients were weaker. Local winds and tide levels estimated from a coastal observing buoy, and bathymetric gradients were examined to help understand the factors that influenced front formation and stability. Periods of strong and variable winds led to front movement of up to 10 km per day or dissipation within 2-3 days in over 80% of the fronts detected in SST, Chl, nL_w, and FLH imagery. Short episodes of less variable wind velocities typically led to more stable and stationary fronts, within 3-5 km, for up to four days. The occurrence of fronts closely associated with the coastal bathymetry, namely at the 20 m and 30 m isobaths, was significantly higher in the fall SST imagery and in the spring Chl imagery. Fall SST fronts related to bathymetric gradients likely resulted from progressive cooling of the water with depth. Stronger Chl and nL_w443 gradients at the mouths of estuaries in the fall compared to the spring were attributed to increased precipitation and periods of stronger winds or tides. The FLH imagery was most useful in delineating coastal algal blooms. The automatic front detection techniques applied here can be an important tool for resource managers to track coastal oceanographic features daily, over synoptic spatial scales.     

Evaluation of RapidEye data for mapping algal blooms in inland waters

The high spatial resolution multispectral imaging sensor onboard RapidEye (RE) has a red-edge band centred at 710 nm, which can be used to produce a product equivalent to the Maximum Chlorophyll Index (MCI) that was developed to detect algal blooms with Medium Resolution Imaging Spectrometer (MERIS) data. The RapidEye system, with five satellites, offers a greater repeat frequency than other high-resolution satellites. In this study, we compared RapidEye and MERIS derived MCI products for the Harris Chain of Lakes in central Florida, USA, to determine if RapidEye can produce an equivalent product similar to MERIS. Data from two RapidEye satellites (RapidEye-2 and RapidEye-5) were used. Band-by-band matchups used RapidEye Top of the Atmosphere (TOA) reflectance and MERIS ρ_s (reflectance corrected only for Raleigh scattering and molecular absorption). The RapidEye TOA reflectance data differed from MERIS, but when the bands were calibrated to the MERIS, the MCI products matched between the two RapidEye satellites and the MERIS MCI. Estimated chlorophyll-a concentrations using a relationship established for Lake Erie matched in situ chlorophyll-a concentrations with a median error of 1.09 mg m^?3. The results indicate that RapidEye is useful for this purpose, which also suggests that other high-resolution satellites with similar red-edge bands may also provide MCI-type products that would allow estimation of chlorophyll-a. RapidEye provides a context for applying future constellation of small satellites for monitoring water quality issues. Lake water quality managers and environmental agencies could effectively use such high-resolution products to assess and manage algal bloom events.     

Variability in measured and modelled remote sensing reflectance for coastal waters at LEO-15

A large database of in situ bio-optical measurements were collected at the LEO-15 (Long-term Ecosystem Observatory) off the southern coast of New Jersey, USA. The data were used to quantify the impact of coastal upwelling on near-shore bulk apparent (AOP) and inherent (IOP) optical properties. There was good qualitative agreement between the AOPs and IOPs in space and time. The measured IOPs were used as inputs to the Hydrolight radiative transfer model (RTE). Estimated spectral AOPs from the RTE were strongly correlated (generally R²>0.80) to measured AOPs. If optical closure between in-water measurements was achieved then the RTE was used to construct the spectral remote sensing reflectance. The modelled remote sensing reflectances were compared to satellite-derived reflectance estimates from four different algorithms. Quantitative agreement between the satellite-measured and in-water modelled remote sensing reflectance was good but results were variable between the different models. The strength of the correlation and spectral consistency was variable with space and time. Correlations were strongest in clear offshore waters and lowest in the near-shore turbid waters. In the near-shore waters, the correlation was strongest for blue wavelengths (400–555?nm) but lower for the red wavelengths of light.     

Passive microwave remote sensing of soil moisture

Results of radiometric measurements over bare soil obtained with a horizontally polarized microwave radiometer at 19·1 GHz frequency are presented. Radiometer measurements were made with incidence angles varying from 10 to 50°. Ground-truth acquisition of soil moisture in the 0–1 cm layer and of soil temperature near the surface was made concurrently with radiometer measurements. The measured brightness temperatures over a bare field are higher than those calculated from an emmissivity model.     

Role of sensor noise in hyperspectral remote sensing of natural waters: Application to retrieval of phytoplankton pigments

An algorithm is derived to retrieve the concentration of optically active materials, e.g., phytoplankton pigments, etc., from remotely measured spectra of up welled oceanic light. The algorithm takes into account sensor noise in deriving equations for the best linear estimate of concentration mean and residual variance. The algorithm is applied to the problem of phytoplankton concentration retrieval using a modeled hyperspectral sensor based roughly on the LASH imager. The algorithm requires knowing the joint distribution of radiance spectra and concentration. This joint distribution is obtained by simulation using ocean radiance models. It is shown that sensor noise (both shot and dark current) markedly decreases the accuracy of concentration retrieval. However, accuracy is greatly improved if a priori information about observation conditions is known and included in the algorithm. Thus accounting for sensor noise improves retrieval accuracy and affects the choice of observation method.     

Passive optical remote sensing of river channel morphology and in-stream habitat: Physical basis and feasibility

Successful monitoring of ecologically significant, vulnerable fluvial systems will require improved quantitative techniques for mapping channel morphology and in-stream habitat. In this study, we assess the ability of remote sensing to contribute to these objectives by (1) describing the underlying radiative transfer processes, drawing upon research conducted in shallow marine environments; (2) modeling the effects of water depth, substrate type, suspended sediment concentration, and surface turbulence; (3) quantifying the limitations imposed by finite detector sensitivity and linear quantization; and (4) evaluating two depth retrieval algorithms using simulated and field-measured spectra and archival imagery. The degree to which variations in depth and substrate can be resolved depends on bottom albedo and water column optical properties, and scattering by suspended sediment obscures substrate spectral features and reduces the resolution of depth estimates. Converting continuous radiance signals to discrete digital numbers implies that depth estimates take the form of contour intervals that become wider as depth increases and as bottom albedo and detector sensitivity decrease. Our results indicate that a simple band ratio can provide an image-derived variable that is strongly linearly related to water depth across a broad range of stream conditions. This technique outperformed the linear transform method used in previous stream studies, most notably for upwelling radiance spectra [R²=0.79 for the ln(560 nm/690 nm) ratio]. Applied to uncalibrated multispectral and hyperspectral images of a fourth-order stream in Yellowstone National Park, this flexible technique produced hydraulically reasonable maps of relative depth. Although radiometric precision and spatial resolution will impose fundamental limitations in practice, remote mapping of channel morphology and in-stream habitat is feasible and can become a powerful tool for scientists and managers.     

Scaling of coastal phytoplankton features by optical remote sensors: comparison with a regional ecosystem model

Different scales of hydrological and biological patterns of the Bay of Biscay are assessed using space‐borne and airborne optical remote sensing data, field measurements and a 3‐dimensional biophysical model. If field measurements provide accurate values on the vertical dimension, ocean colour data offer frequent observations of surface biological patterns at various scales of major importance for the validation of ecosystem modelling. Although the hydro‐biological model of the continental margin reproduces the main seasonal variability of surface biomass, the optical remote sensing data have helped to identify low grid resolution, input inaccuracies and neglect of swell‐induced erosion mechanism as model limitations in shallow waters. Airborne remote sensing is used to show that satellite data and field measurements are unsuitable for comparison in the extreme case of phytoplankton blooms in patches of a few hundred metres. Vertically, the satellite observation is consistent with near surface in situ measurements as the sub‐surface chlorophyll maximum usually encountered in summer is not detected by optical remote sensing. A mean error (δC/C) of 50.5% of the chlorophyll‐a estimate in turbid waters using the SeaWiFS‐OC5 algorithm allows the quantitative use of ocean colour data by the coastal oceanographic community.     

Optical properties of inorganic suspended solids and their influence on ocean colour remote sensing in highly turbid coastal waters

The influence of the optical properties of inorganic suspended solids (ISS) on in-water algorithms was evaluated using an optical model in highly turbid coastal water, whose ISS concentration reached several hundred grams per cubic metre. The measurements were conducted in the upper Gulf of Thailand. The backscattering coefficient of the ISS was calculated using the Lorenz–Mie scattering theory. On the basis of the measurement, the ISS size distribution was parameterized as a function of ISS concentration, and both the spherical and non-spherical particle shape models were evaluated. For ISS concentrations of 10 g m^?3, an estimate of the chlorophyll-a (chl-a) concentration within a factor of 2 on a logarithmic scale is possible in a [chl-a] range of 4–30 mg m^?3. The differential coefficient of remote sensing reflectance was calculated to evaluate its respective sensitivities for chl-a and ISS concentrations. The use of radiometric data at 670 nm (700–900 nm) is valid for in-water algorithms used to estimate chl-a (ISS) concentration in highly turbid coastal waters.     

Passive and active airborne microwave remote sensing of snow cover

The presence of snow cover affects the regional energy and water balance, thus having a significant impact on the global climate system. Temporal knowledge of the onset of snow melt and snow water equivalent (SWE) values are important variables in the prediction of flooding, as well as water resource applications such as reservoir management and agricultural activities. Microwave remote sensing techniques have been effective for monitoring snow pack parameters (snow extent, depth, water equivalent, wet/dry state). Coincident ground data, airborne polarimetric C-band (5.3 GHz) Synthetic Aperture Radar (SAR) and passive microwave radiometer data (19, 37 and 85 GHz) were collected on four dates (1 December 1997, 6 March 1998, 12 March 1998 and 9 March 1999) over two flight lines in Eastern Ontario, Canada. The multitemporal, multi-sensor data were analysed for changes in SAR polarimetric signatures and microwave brightness temperatures as a function of changing snow pack parameters. Results indicate that certain parameters such as linear polarizations and pedestal height are sensitive to changes in snow pack parameters, and respond differently to various snow conditions. SWE values derived from the passive microwave brightness temperatures compare well with ground measurements, with the exception of low snow volume and in the presence of significant ice layers.     

Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass

An extensive field campaign was carried out for the validation of a previously published reflectance ratio-based algorithm for quantification of the cyanobacterial pigment phycocyanin (PC). The algorithm uses band settings of the Medium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, and should accurately retrieve the PC concentration in turbid, cyanobacteria-dominated waters. As algae and cyanobacteria often co-occur, the algorithm response to varying phytoplankton composition was explored. Remote sensing reflectance and reference pigment measurements were obtained in the period 2001-2005 in Spain and the Netherlands using field spectroradiometry and various pigment extraction methods. Additional field data was collected in Spain in May 2005 to allow intercalibration of spectroradiometry and pigment assessment methods. Two methods for extraction of PC from concentrated water samples, and in situ measured PC fluorescence, compared well. Reflectance measurements with different field spectroradiometers used in Spain and the Netherlands also gave similar results. Residual analysis of PC predicted by the algorithm showed that overestimation of PC mainly occurred in the presence of chlorophylls b and c, and phaeophytin. The errors were strongest at low PC relative to Chl a concentrations. A correction applied for absorption by Chl b markedly improved the prediction. Without such a correction, the quality of the PC prediction still increased markedly with estimates > 50 mg PC m^− 3, allowing monitoring of the cyanobacterial status of eutrophic waters. The threshold concentration may be lowered when a high intracellular PC:Chl a ratio or cyanobacterial dominance is expected. Below the limit, predicted PC concentrations should be considered as the highest estimate. We evaluated that remote sensing of both PC and Chl a would allow assessment of cyanobacterial risk to water quality and public health in over 70% of our cases.     

Orbital remote sensing of phytoplankton functional types: a new review

Phytoplankton functional types (PFTs) are groups of species that have specific roles in the biogeochemical cycles and trophic flow. These groups have been pointed out as keys to improve the knowledge on ecosystem dynamics and effects of climate and anthropogenic changes in the marine environment. Orbital remote sensing is stated as the only means capable of analysing spatio-temporal distribution of PFTs, over regional and global scales. Many research teams have focused their efforts on the development of PFT bio-optical models, presenting a great variety of approaches. Considerable advances have been achieved in a few years of research; however, challenges are still great. Further developments rely on improvements in the knowledge of PFTs' bio-optical characteristics and the possibility of integrating different bio-optical and ecological information. This article presents a new review of the state of the art of PFTs' characterization from space, with examples of the main models and a discussion of achievements, gaps and future perspectives.     

SeaWiFS validation in European coastal waters using optical and bio-geochemical measurements

The National Aeronautics & Space Administration (NASA) Sea viewing Wide Field of view Sensor (SeaWiFS) began operational measurement of ocean colour in September 1997. Upgrades to the SeaWiFS data processing system (SeaDAS) have occurred frequently and the effects of these revisions on the remotely sensed estimates of chlorophyll-a concentration (chl-a) have been significant. Measurements of chl-a from research work in the Bay of Biscay and Gulf of Cadiz during 1998–1999 are used to validate the SeaWiFS chl-a product generated using the current version of SeaDAS (version 4.1). The validation data cover coastal and offshore waters, including those dominated by inorganic suspended sediment, and an intense dinoflagellate bloom where shipboard chl-a measurements exceeded 50?mg?m^?3. The standard SeaWiFS chlorophyll algorithm (OC4v4) generally performed well, but significantly over-estimated chl-a where inorganic suspended sediment was present. The algorithm is only applicable to chl-a values up to 64?mg?m^?3, which was less than chl-a at the centre of the bloom. A novel algorithm for chl-a, which first estimates the inherent optical properties of the water, was applied to the SeaWiFS measurements but failed on over 90% of the pixels, perhaps because SeaWiFS is under-estimating water reflectance at the extreme blue end of the visible spectrum.     

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.     

Diurnal variability of ocean optical properties during a coastal algal bloom: implications for ocean colour remote sensing

Understanding the diurnal variability of ocean optical properties is critical for better interpretation of satellite ocean colour data and characterizing biogeochemical processes. The daytime variability of ocean optical properties throughout an algal bloom event is analysed in this article based on in situ observations from dawn to dusk at a fixed coastal site in the South China Sea. Diurnal variability during the sunlit period of the ocean optical properties is found to be significant. During the 6 hours around noon, the temporal variability (defined by the coefficient of variation) of phytoplankton absorption, coloured dissolved organic matter and non-algal particle absorption, and particle backscattering at 443 nm can reach 21% ± 15%, 12% ± 9%, and 17% ± 9%, respectively. The diurnal variability during the bloom is much more pronounced than that of the non-bloom phase. With atmospheric radiative transfer modelling, it is further demonstrated that the geostationary satellite detection of within-day optical variability in algae-dominated waters depends on the reliability of the aerosol retrieval. The implications of the diurnal bio-optical variability for the retrieval, validation, and interpretation of satellite ocean colour products are also discussed.     

The integrated radiometric correction of optical remote sensing imageries

Abstract Environmental analysis, management and modelling require detailed and precise land‐use/land‐cover discrimination as initial conditions of land surface characteristics. With the ultimate goal of accurate land surface classification analysis, we devised a fully image‐based and physically based correction method (the Integrated Radiometric Correction (IRC) method) considering both the atmospheric and the topographic effects simultaneously, using the information deduced from the satellite images and 5 m resolution DEM data. The overall process is carried out in four steps: (i) calculation of the radiance/irradiance relational expression for horizontal surfaces, (ii) devising the radiance/irradiance relational expression for inclined surfaces, (iii) derivation of solar and land geometric parameters from DEM data, as well as the calculation of the topographic correction factor (A‐factor) and the atmospheric transmittance functions, and (iv) retrieval of the corrected surface reflectance and radiance. Using Landsat/ETM+ satellite data, the performance of the formulated IRC method is evaluated visually and statistically. Visual evaluation of radiometrically corrected images shows significant improvements for each band as well as for various bands composites, while the independence between the corrected surface reflectance and radiance, and the topography (incidence angle (i) or solar illumination (cos i)) is shown by very weak correlation coefficients as compared with non‐corrected data.     

Application of eigenvector analysis to remote sensing of coastal water quality

Abstract

The use of algorithms incorporating radiance information from one or a number of wavelengths is a standard technique for detecting the concentration and distribution of water quality parameters in coastal and open ocean waters. It has become clear, however, that in a turbid dynamic coastal environment there is no one algorithm applicable for all times, seasons or area because the composition of the suspended material variescontinually. Consequently site specific algorithms have been proposed. Results of an eigenvector analysis of radiance spectra and sea-truth data collected as part of airborne remote sensing campaigns in 1984 and 1985 are presented. The eigenvectors of radiance data are shown to be dependent on the type and relative concentrations of material in suspension. The technique is shown to have great potential for the identification of the composition of material in suspension without recourse to sea-truth data. This information could be used as a criterion for selection of an appropriate algorithm.     

Numerical modelling of transspectral processes in natural waters: Implications for remote sensing

Based on a previously developed and thoroughly validated hydrooptical model, numerical simulations of the spectral composition of water leaving radiance are presented. These simulations take into account absorption, elastic scattering, water Raman (inelastic) scattering as well as the fluorescence of chlorophyll ( chl ) and dissolved organics ( doc ). The results obtained for forward modelling were also used for the inverse problem: retrieval of water quality parameters from water volume reflectance ( R ) spectra. The Levenberg-Marquardt multivariate optimization procedure was used for this purpose. Unlike water Raman scattering, the chl and doc fluorescence has an impact on R, so the retrieval results can change substantially for waters rich in chl or doc . Suspended minerals ( sm ) suppress both the chl and doc fluorescence influence on R . The retrieval results indicate that chl can be accurately assessed if the concentration of sm is not low and the doc concentration is < 2 mgCl -1 . For waters devoid of doc, the concentration of chl can be accurately retrieved even if the sm concentration is very low. Retrieval errors prove to be strongly dependent on the fluorescence yield value of both chl and doc .