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A new range of multi-plankton biogeochemical models have recently been developed, designed to advance our understanding of the ocean carbon cycle to improve predictions of its future influence on climate. Synoptic measurements of the different phytoplankton communities are required to validate and ultimately improve such models. Measuring ocean colour from satellite is the only method currently available for synoptically monitoring wide-area properties of ocean ecosystems, such as phytoplankton chlorophyll biomass. Recently, a variety of bio-optical methods have been established that use satellite data to identify and differentiate between either phytoplankton functional types (PFTs) or phytoplankton size classes (PSCs). In this thesis, several of these techniques were evaluated against in situ observations (6504 samples) to determine their ability to detect dominant phytoplankton size classes (micro-, nano- and picoplankton). Results show that spectral-response, ecological and abundance-based approaches can all perform with similar accuracy. However, abundance-based approaches provide better spatial retrieval of PSCs. Based on insights into the abundance-based models, and by utilising a large pigment database, a new three-component model was developed which calculates the fractional contributions of three phytoplankton size classes (micro-, nano- and picoplankton) to the overall chlorophyll-a concentration. Using a globally representative, independent, coupled pigment and satellite dataset the model estimates fractional contributions with a mean accuracy of 9.2 % for microplankton, 17.1 % for nanoplankton and 16.1 % for picoplankton. The effect of optical depth on the model parameters was also investigated and explicitly incorporated into the model. Using the three-component model, the two-component absorption model of Sathyendranath et al. (2001) and Devred et al. (2006) was extended to three-component populations of phytoplankton, namely, pico-, nano- and microplankton. The new model infers total and size-dependent phytoplankton absorption as a function of the total chlorophyll-a concentration. A main characteristic of the model is that all the parameters that describe it have biological or optical interpretation. The three-component model performs better than the two-component model, at retrieving total phytoplankton absorption. Accounting for the contribution of pico- and nanoplankton, rather than the combination of both used in the two-component model, improved significantly the retrieval of phytoplankton absorption at low chlorophyll-a concentrations. The three-component model was applied to a decade of ocean colour observations. In the equatorial region of the Pacific and Indian Oceans, phytoplankton size class anomalies (% total chlorophyll-a) were highly correlated with indices of both the El Niño (La Niña) Southern Oscillation and the Indian Ocean Dipole. Furthermore, in these regions, micro- and nanoplankton size class anomalies were negatively correlated with anomalies of the sea surface temperature, sea surface height and stratification. Whereas, the picoplankton size class anomalies were positively correlated with these physical variables. Results from this thesis indicate that phytoplankton size class can be retrieved from Earth Observation with reasonable accuracy. It is recommended that such information can now be assimilated into multi-plankton biogeochemical models, or alternatively, verify them.

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Ecological indicators are defined as quantifiable metrics that can be used to monitor the state of ecosystems and their response to environmental perturbations. In the global oceans, commonly used indicators are typically based on the presence and distribution of phytoplankton (as indexed by the concentration of chlorophyll-a [Chl-a]), which form the base of oceanic food webs. Phytoplankton phenology (the timing of phytoplankton growth) and phytoplankton size structure are particularly important ecological indicators that can be derived via ocean colour remote sensing. Phytoplankton phenology has a direct control on food availability, which subsequently impacts the survival of higher trophic levels and the structure of marine ecosystems. Meanwhile, phytoplankton size structure can be used to define the major functional groups that ultimately influence marine food web structure, biogeochemical cycling and carbon export. The Red Sea is a relatively unexplored tropical marine ecosystem, particularly in relation to its large-scale biological dynamics. In light of recent evidence of rapid regional warming, the need to monitor the response of the Red Sea to potential future ecosystem modifications is becoming more imminent. Using a combination of contemporary oceanographic tools, with an emphasis on ocean colour remote sensing, this PhD thesis attempts to validate the retrieval of phytoplankton ecological indicators in the Red Sea - specifically phytoplankton abundance, phenology and size structure. The interannual variability of both indicators and their linkages with the regional physical environment are also explored.

One of the most difficult issues when dealing with optical water remote-sensing is its acceptance as a useful application for environmental research. This problem is, on the one hand, concerned with the optical complexity and variability of the investigated natural media, and therefore the question arises as to the plausibility of the parameters derived from remote-sensing techniques. Detailed knowledge about the regional bio- and chemico-optical properties is required for such studies, however such information is seldom available for the sites of interest. On the other hand, the primary advantage of remote-sensing information, which is the provision of a spatial overview, may not be exploited fully by the disciplines that would benefit most from such information. It is often seen in a variety of disciplines that scientists have been primarily trained to look at discrete data sets, and therefore have no experience of incorporating information dealing with spatial heterogeneity. <br><br> In this thesis, the opportunity was made available to assess the potential of Ocean Colour data to provide spatial and seasonal information about the surface waters of Lake Baikal (Siberia). While discrete limnological field data is available, the spatial extension of Lake Baikal is enormous (ca. 600 km), while the field data are limited to selected sites and expedition time windows. Therefore, this remote-sensing investigation aimed to support a multi-disciplinary limnological investigation within the framework of the paleoclimate EU-project ‘High Resolution CONTINENTal Paleoclimate Record in Lake Baikal, Siberia (CONTINENT)’ using spatial and seasonal information from the SeaWiFS satellite (NASA). From this, the SeaWiFS study evolved to become the first efficient bio-optical satellite study of Lake Baikal. <br><br> During the course of three years, field work including spectral field measurements and water sampling, was carried out at Lake Baikal in Southern Siberia, and at the Mecklenburg and Brandenburg lake districts in Germany. The first step in processing the SeaWiFS satellite data involved adapting the SeaDAS (NASA) atmospheric-correction processing to match as close as possible the specific conditions of Lake Baikal. Next, various Chl-<i>a</i> algorithms were tested on the atmospherically-corrected optimized SeaWiFS data set (years 2001 to 2002), comparing the CONTINENT pigment ground-truth data with the Chl-<i>a</i> concentrations derived from the satellite data. This showed the high performance of the global Chl-<i>a</i> products OC2 and OC4 for the oligotrophic, transparent waters (bio-optical Case 1) of Lake Baikal. However, considerable Chl-<i>a</i> overestimation prevailed in bio-optical Case 2 areas for the case of discharge events. High-organic terrigenous input into Lake Baikal could be traced and information extracted using the SeaWiFS spectral data. Suspended Particulate Matter (SPM) was quantified by the regression of the SeaDAS attenuation coefficient as the optical parameter with SPM field data. <br><br> Finally, the Chl-<i>a</i> and terrigenous input maps derived from the remote sensing data were used to assist with analyzing the relationships between the various discrete data obtained during the CONTINENT field work. Hence, plausible spatial and seasonal information describing autochthonous and allochthonous material in Lake Baikal could be provided by satellite data.<br>Lake Baikal, with its bio-optical complexity and its different areas of Case 1 and Case 2 waters, is a very interesting case study for Ocean Colour analyses. Proposals for future Ocean Colour studies of Lake Baikal are discussed, including which bio-optical parameters for analytical models still need to be clarified by field investigations. / Die Gewässerfernerkundung entwickelte sich seit den 70ern vor allem aus der Ozeanographie und der Atmosphärenforschung, und wird inzwischen als anerkannte Methode genutzt, um global die Phytoplanktonverteilung in den Weltmeeren erfassen zu können, u.a. für CO₂-Haushaltsmodellierungen. Atmosphärenkorrigierte Multi- und Hyperspektralscannerdaten ermöglichen die Qualifizierung bio-optischer Gewässertypen und die Quantifizierung optisch sichtbarer Wasserinhaltsstoffe und bieten gerade auch für dynamische und heterogene Küsten- und Binnengewässer das große Potential des räumlichen Informationsgewinnes.<br>Im Rahmen des Paläoklimaprojektes CONTINENT wurde in dieser Arbeit das Oberflächenwasser des Baikalsees mit Gewässerfernerkungsmethoden analysiert. Wichtig für die Interpretation von Klima-Proxies sind v.a. auch Hinweise auf die Verteilung des autochthonen Materials im Baikalsee (Fernerkundungsparameter: Chlorophyll-<i>a</i>), ebenso wie Hinweise auf allochthone Einträge an den Bohrungsstellen (Fernerkundungsparameter ‚Terrigener Eintrag’). Auf den Geländekampagnen in den Sommern 2001, 2002, 2003 in Sibirien und in Deutschland wurden Feldspektrometermessungen mit gleichzeitiger Wasserprobenahme auf die optisch sichtbaren Wasserinhaltsstoffe Phytoplankton, Schwebstoff, und DOC durchgeführt. Dabei konnten Messtechniken für Geländespektrometer evaluiert, und grundlegende Aussagen über die spektrale Verteilung des In-Wasser Lichtfeldes im Baikalsee gemacht werden. <br><br> Die Ocean Colour Satellitendaten des NASA-Instrumentes SeaWiFS und die Möglichkeiten der komplexen NASA Software SeaDAS wurden genutzt. Für die Ableitung des am Baikalsee anzutreffenden organikreichen terrigenen Eintrages, wurde ein vorläufiger Algorithmus aus den Geländedaten generiert. Verschiedene Algorithmen für den Parameter ‚Chlorophyll-<i>a</i>’ wurden mit dem Geländedatensatz der Projektpartnerin S. Fietz (Institut für Gewässerökologie und Binnenfischerei, IGB) evaluiert. Als geeignetester etablierte sich der auf oligotrophe Gewässer optimierte NASA Chlorophyll Algorithmus ‚Ocean Colour (OC) 2’. Die Quantifizierungen und Ergebnisse werden diskutiert. <br><br> Als Endergebnis wird der Überblick über Sedimenteintrag und Phytoplanktondynamik im Baikalsee für den Zeitraum 2001-2002 zur Verfügung gestellt und die autochthonen versus allochthonen Einflüsse an den Projektlokationen werden beschrieben. Der Baikalsee erwies sich als bio-optisch ein sehr komplexes und interessantes Studienobjekt. Ein wichtiger Punkt, der in dieser Arbeit angesprochen wird, ist die Atmosphärenkorrektur, die wesentliche Einflüsse auf die Qualifizierungen und Quantifizierungen hat, aber als Standardprogramm nur für den pelagialen Wasserkörper in Meeresspiegelhöhe mit marinen, bzw. Küstenatmosphären konditioniert ist. Ein weiterer bedeutender Punkt, der in dieser Arbeit diskutiert wird, ist der relevante spektrale Einfluss des organikreichen terrigenen Eintrages auf die Gewässerfarbe und dadurch auf die Qualität der Chlorophyll-Ableitung. Somit boten sich die Möglichkeiten, das räumliche Ausmaß und die Dynamik rezenter terrigener Einträge zu erfassen. Auch die Entwicklung des Phytoplankton von Frühsommer bis Spätsommer im Baikalsee konnte mit den SeaWiFS Daten nachvollzogen werden. Die hier vorgestellte Studie stellte sich als die erste grundlegende optische Gewässerfernerkundungsstudie mit Satellitendaten am Baikalsee heraus, und konnte erfolgreich abgeschlossen werden.

A major limitation for remote sensing analyses of oceanographic variables is loss of spatial data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated effectiveness for filling spatial gaps in remote sensing datasets, making them more easily implemented in further applications. However, dataset reconstructions with this method are sensitive to the characteristics of the input data used. The spatial and temporal coverage of the input imagery can heavily impact the reconstruction outcome, and thus, further metrics derived from these datasets, such as phytoplankton bloom phenology. In this study, the DINEOF method was applied to a three-year time series of MODIS-Aqua chlorophyll-a of the Salish Sea, Canada. Spatial reconstructions were performed on an annual and multi-year basis at daily and week- composite time resolutions, and assessed relative to the original, clouded chla datasets and a set of extracted in situ chla measurements. A sensitivity test was performed to assess stability of the results with variation of cross-validation data and simulated scenarios of lower temporal data coverage. Daily input time series showed greater accuracy reconstructing chla (95.08-97.08% explained variance, RMSExval 1.49 - 1.65 mg m-3) than week-composite counterparts (68.99-76.88% explained variance, RMSExval 1.87 – 2.07 mg m-3), with longer time series of both types producing a better relationship to original chla pixel concentrations (R 0.95 over 0.94, RMSE 1.29 over 1.35 mg m-3, slope 0.88 over 0.84). Original daily chla achieved a better relationship to in situ matchups than DINEOF gap-filled chla, with annual DINEOF-processed data performing better than the multi-year. The results of this study are of interest to those who require spatially continuous satellite-derived products, particularly from short time series, and encourage processing consistency in future DINEOF studies to allow unification for global purposes such as climate change studies (Mélin et al., 2017). / Graduate

Remote sensing is a powerful tool for studying the marine environment; however, many images are contaminated by sun glint, the specular reflection of light from the water surface. Improved radiative transfer modelling could lead to better methods for estimating and correcting sunglint. This thesis explores the effect of using detailed numerical models of the sea surface when investigating the transfer of light through the atmosphere-ocean system. New numerical realisations that model both the shape and slope of the sea surface have been created; these contrast with existing radiative transfer models, where the air-water interface has slope but not elevation. Surface realisations including features on a scale from 3 mm to 200 m were created by a Fourier synthesis method, using up to date spectra of the wind-blown sea surface. The surfaces had mean square slopes and elevation variances in line with those of observed seas, for wind speeds up to 15 m/s. Ray-tracing using the new surfaces gave estimates of reflected radiance that were similar to those made using slope statistics methods, but significantly different in 41% of cases tested. The mean difference in the reflected radiance at these points was 19%, median 7%. Elevation-based surfaces give increased sideways scattering and reduced forward scattering of light incident on the sea surface. The elevation-based models have been applied to estimate pixel-pixel variation in ocean colour imagery and to simulate scenes viewed by three types of sensor. The simulations correctly estimated the size and position of the glint zone. Simulations of two ocean colour images gave a lower peak reflectance than the original values, but higher reflectance at the edge of the glint zone. The use of the simulation to test glint correction methods has been demonstrated, as have global Monte Carlo techniques for investigating sensitivity and uncertainty in sun glint correction. This work has shown that elevation-based sea surface models can be created and tested using readily-available computer hardware. The new model can be used to simulate glint in a variety of situations, giving a tool for testing glint correction methods. It could also be used for glint correction directly, by predicting the level of sun glint in a given set of conditions.