Abstract:Eutrophication is an increasing problem in coastal waters of the Baltic Sea. Moreover, algal blooms, which occur every summer in the Gulf of Gdansk can deleteriously impact human health, the aquatic environment, and economically important fisheries, tourism, and recreation industries. Traditional laboratory-based techniques for water monitoring are expensive and time consuming, which usually results in limited numbers of observations and discontinuity in space and time. The use of hyperspectral radiometers for coastal water observation provides the potential for more detailed remote optical monitoring. A statistical approach to develop local models for the estimation of optically significant components from in situ measured hyperspectral remote sensing reflectance in case 2 waters is presented in this study. The models, which are based on empirical orthogonal function (EOF) analysis and stepwise multilinear regression, allow for the estimation of parameters strongly correlated with phytoplankton (pigment concentration, absorption coefficient) and coloured detrital matter abundance (absorption coefficient) directly from reflectance spectra measured in situ. Chlorophyll a concentration, which is commonly used as a proxy for phytoplankton biomass, was retrieved with low error (median percent difference, MPD = 17%, root mean square error RMSE = 0.14 in log 10 space) and showed a high correlation with chlorophyll a measured in situ (R = 0.84). Furthermore, phycocyanin and phycoerythrin, both characteristic pigments for cyanobacteria species, were also retrieved reliably from reflectance with MPD = 23%, RMSE = 0.23, R 2 = 0.77 and MPD = 24%, RMSE = 0.15, R 2 = 0.74, respectively. The EOF technique proved to be accurate in the derivation of the absorption spectra of phytoplankton and coloured detrital matter (CDM), with R 2 (λ) above 0.83 and RMSE around 0.10. The approach was also applied to satellite multispectral remote sensing reflectance data, thus allowing for improved temporal and spatial resolution compared with the in situ measurements. The EOF method tested on simulated Medium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI) data resulted in RMSE = 0.16 for chl-a and RMSE = 0.29 for phycocyanin. The presented methods, applied to both in situ and satellite data, provide a powerful tool for coastal monitoring and management.
An equation for the rate of photosynthesis as a function of irradiance introduced by T. T. Bannister included an empirical parameter b to account for observed variations in curvature between the initial slope and the maximum rate of photosynthesis. Yet researchers have generally favored equations with fixed curvature, possibly because b was viewed as having no physiological meaning. We developed an analytic photosynthesis-irradiance equation relating variations in curvature to changes in the degree of connectivity between photosystems, and also considered a recently published alternative, based on changes in the size of the plastoquinone pool. When fitted to a set of 185 observed photosynthesisirradiance curves, it was found that the Bannister equation provided the best fit more frequently compared to either of the analytic equations. While Bannister's curvature parameter engendered negligible improvement in the statistical fit to the study data, we argued that the parameter is nevertheless quite useful because it allows for consistent estimates of initial slope and saturation irradiance for observations exhibiting a range of curvatures, which would otherwise have to be fitted to different fixed-curvature equations. Using theoretical models, we also found that intra-and intercellular self-shading can result in biased estimates of both curvature and the saturation irradiance parameter. We concluded that Bannister's is the best currently available equation accounting for variations in curvature precisely because it does not assign inappropriate physiological meaning to its curvature parameter, and we proposed that b should be thought of as the expression of the integration of all factors impacting curvature.Key index words: connectivity; curvature; models of photosynthetic rate versus irradiance; pigment packaging; saturation irradiance Abbreviations: ETC, electron transport chain; PQ, photosynthetic quotient; PSU, photosynthetic unit; PUR, photosynthetically utilizable irradiance; WNS, Webb, Newton, and StarrThe aquatic food web is based on the conversion of inorganic carbon into organic compounds by photosynthetic organisms, including benthic microalgae, macroalgae, seagrasses, and phytoplankton. While the focus here is on phytoplankton, in part because of the relative simplicity of the optical fields that can be assumed for dilute samples of photosynthetic microbes, our findings are based on generally applicable principles and have some relevance to all photosynthetic systems.Like all photosynthetic organisms, phytoplankton depends on light and nutrients for growth. Accurate estimation of the productive potential of an assemblage of phytoplankton is predicated on accurate quantification of the efficiency with which these resources are utilized, and can be crucial in the assessment of the structure and functioning of an aquatic ecosystem, estimation of the rate of carbon sequestration in climate models, and determination of the commercial feasibility of algal culture for the production of fuel, protein or other ...
The Canadian Forces Meteorology and Oceanography Center produces a near-daily ocean feature analysis, based on sea surface temperature (SST) images collected by spaceborne radiometers, to keep the fleet informed of the location of tactically important ocean features. Ubiquitous cloud cover hampers these data. In this paper, a methodology for the identification of SST front signatures in cloud-independent synthetic aperture radar (SAR) images is described. Accurate identification of ocean features in SAR images, although attainable to an experienced analyst, is a difficult process to automate. As a first attempt, the authors aimed to discriminate between signatures of SST fronts and those caused by all other processes. Candidate SST front signatures were identified in Radarsat-2 images using a Canny edge detector. A feature vector of textural and contextual measures was constructed for each candidate edge, and edges were validated by comparison with coincident SST images. Each candidate was classified as being an SST front signature or the signature of another process using logistic regression. The resulting probability that a candidate was correctly classified as an SST front signature was between 0.50 and 0.70. The authors concluded that improvement in classification accuracy requires a set of measures that can differentiate between signatures of SST fronts and those of certain atmospheric phenomena and that a search for such measures should include a wider range of computational methods than was considered. As such, this work represents a step toward the goal of a general ocean feature classification algorithm.
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