Regional Bio-optical Relationships and Algorithms for the Adriatic Sea, the Baltic Sea and the English Channel/North Sea Suitable for Ocean Colour Sensors

Regional bio-optical relationships and empirical algorithms were developed on the basis of measurements collected during the CoASTS 1995-2005 bio-optical time-series in the northern coastal Adriatic Sea as well as during ship campaigns performed in coastal regions of the Adriatic Sea, the Baltic Sea and the English Channel/North Sea between 2000 and 2005. The empirical algorithms aim at the retrieval from ocean colour data of the Chlorophyll a and Total Suspended Matter concentrations, of the absorption coefficient of the Coloured Dissolved Organic Matter, of the diffuse attenuation coefficient of downwelling irradiance and of the Secchi depth. Bio-optical relationships relating the marine optically significant components to their absorption or scattering properties are also presented for the investigated coastal areas.JRC.H.3-Global environement monitorin

Spatial variability assessment of local chlorophyll-A estimation using satellite data

The estimation of Chlorophyll-a (Chl-a) for optically complex water from satellite is challenging. Moderate Resolution Imaging Spectroradiometer (MODIS) is an ocean colour satellite which has low spatial resolution and this has led to bias estimate and scale effect that eventually induced errors in Chl-a retrieval using local ocean colour algorithm. Studies on Chl-a variation, assessment of MODIS data and development of local ocean colour algorithm are less for Malacca Straits water. The aim of this study is to locally calibrate and validate the Chl-a derived from MODIS standard Chl-a algorithm (OC3M) on the latest R2013 data within the acceptable error tolerance at the Absolute Percentage Difference (APD) below 35% and to test the algorithm’s applicability. Iterative regression method with weighted function (WFd) namely Iterative Conditional Regression Model (ICRM) is introduced to reduce the spatial bias in the Chl-a estimate. Locally calibrated OC3M algorithm with in-situ data taken at two static stations and kernel 7×7 size named as OCms1 (calibrated with in-situ Case-1 water) and OCms2 (calibrated with in-situ Case-2 water) remarkably reduced the Chl-a bias with APD of 37% and 30% from 54% and 116% respectively. Then, using the ICRM, the APD of OCms1 WFd and OCms2 WFd is 26% and 29% respectively. Results of OCms WFd and OCms (with and without weighted function respectively) are combined for mapping the Chl-a in Case-1 and Case-2 waters. Result of applicability test and statistical analysis shows that OCms WFd ocean colour algorithm provides statistically highest accuracy for Chl-a estimation. The development of local Chl-a algorithm is essential for accurate Chl-a retrieval and it is significant to other marine studies such as in primary production and algal bloom in Malacca Strait water

Mediterranean ocean colour Level 3 operational multi-sensor processing

The Mediterranean near-real-time multi-sensor processing chain has been set up and is operational in the framework of the Copernicus Marine Environment Monitoring Service (CMEMS). This work describes the main steps operationally performed to enable single ocean colour sensors to enter the multi-sensor processing applied to the Mediterranean Sea by the Ocean Colour Thematic Assembly Centre within CMEMS. Here, the multi-sensor chain takes care of reducing the inter-sensor bias before data from different sensors are merged together. A basin-scale in situ bio-optical dataset is used both to fine tune the algorithms for the retrieval of phytoplankton chlorophyll and the attenuation coefficient of light, Kd, and to assess the uncertainty associated with them. The satellite multi-sensor remote sensing reflectance spectra agree better with the in situ observations than those of the single sensors. Here, we demonstrate that the operational multi-sensor processing chain compares sufficiently well with the historical in situ datasets to also confidently be used for reprocessing the full data time series.</p

Use of the Novelty Detection Technique to Identify the Range of Applicability of Empirical Ocean Color Algorithms.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Surface water quality estimation using remote sensing in the Gulf of Finland and the Finnish Archipelago Sea

This thesis deals with surface water quality estimation using remote sensing in the Gulf of Finland and the Archipelago Sea. Satellite remote sensing of water and empirical algorithms for surface water quality variables in coastal waters in the Gulf of Finland and the Archipelago Sea are explained and results from the studies in the area are presented. Concurrent in situ surface water measurements, AISA data, Landsat TM data, ERS-2 SAR data, AVHRR and MODIS data were obtained for selected locations in the Gulf of Finland and the Archipelago Sea in August 1997 and from April to May 2000, respectively. The AISA, TM, SAR, AVHRR and MODIS data from locations of water samples were extracted and digital data were examined. Significant correlations were observed between digital data and surface water quality variables. Semi-empirical, simple and multivariate regression analyses, and neural network algorithms were developed and applied in the study area. Application of neural networks appears to yield a superior performance in modelling radiative transfer functions describing the relation between satellite observations and surface water characteristics. The results show that the estimated accuracy for major characteristics of surface waters using the neural network method is much better than retrieval by using regression analysis. Since radar observations of water are strongly affected by surface geometry but not by water quality, radar data should be useful to eliminate the effects of surface roughness from the results when combined with optical observations. However, our results suggest that microwave data improve estimation of water quality very little or not at all. The technique, however, should be examined with new data sets obtained under various weather and water quality conditions in order to estimate its feasibility for estimating surface water quality parameters in the Finnish coastal waters.reviewe

Ocean colour off the portuguese coast:chlorophyll α products validation and applicability

Tese de doutoramento, Ciências do Mar, Universidade de Lisboa, Faculdade de Ciências, 2013Ocean colour is an invaluable tool to monitor temporal and spatial distribution of phytoplankton biomass. Chlorophyll a (Chl) is the main biomass proxy for phytoplankton, and ocean colour sensors allow for a synoptic and quasi-permanent following of this pigment concentration in surface waters. However, algorithms that are designed for use at global scales may be less accurate at local and regional scales, namely in coastal areas. These optically complex areas are of upmost importance to monitor phytoplankton blooms as they are subject to major anthropogenic pressures. Therefore, regional evaluation of products accuracy is needed to ensure correct data analysis and interpretation. It is important to understand the limitations of the different products in reference to specific areas and to validate the ocean-colour standard products with in situ data, in order to satisfy the quality requirements for monitoring purposes. In this thesis, Chl product validation is undertaken by directly comparing remote sensing data with in situ surface data. Water samples collected during 2 monitoring programmes and on board 13 cruises off the Portuguese coast during the period 2005 – 2012 were processed by reversed phase High-Performance Liquid Chromatography (HPLC) for pigment determination, and the Chl concentration compared with coincident MERIS and MODIS sensors data. The performance of standard MERIS (algal1 and algal2) and OC3M MODIS products, as well as novel products generated by ESA projects (i.e., CoastColour and Climate Chnage Initiative, CCI, products) and a regionally adjusted algorithm were evaluated using match-up data sets. In general, satellite products were found to overestimate Chl concentrations in comparison to in situ values. Best results were determined for the regionalized algorithm (MLP_ATLP) and the standard products with best results were the MODIS OC3M and the algal 2 MERIS, the former having lower RMS, but the latter revealing lower bias. Statistical differences were verified for the various cruises, and Nazaré region was identified as an area of interest for validation activities due to its complex oceanographic dynamic. Optical in situ data collected in one cruise revealed the presence of CDOM dominated waters, however more comprehensive analysis is needed. The use of remote sensing data for water-type classification revealed the need for improved atmospheric correction in the blue part of the spectrum. Nonetheless, classification scheme applied revealed a strong seasonal component in the spatial distribution of non-case 1 water types, which were more relevant along the northern coast (i.e., north of Cape Espichel) during winter.Factors influencing Chl products accuracy varied according to product under analysis. Biomass, first optical depth and water-leaving radiance at 555 nm were found to be significantly related to the percent error of MODIS Chl product. For MERIS standard products, algal 1 percent error was significantly related to the phytoplankton size index, to the water-leaving radiance at 555 nm and to the water-leaving radiance ratio 412/443 nm, which was also significantly related to the algal 2 product error. Biomass and the first optical depth were the other factors identified to be significantly related to algal 2 product percent error.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/24245/2005 e projeto habspot PTDC/MAR/100348/2008); European Space Agency projects DUE Coast Colour (ESRIN/AO/1-6141/09/l-EC); Climate Change Iniciative – Ocean Colour (AO-1/6207/09/I-LG); European Space Agency within the framework of the MERIS Validation Activities under contract n. 12595/09/I-OL; projetos HERMES (GOCE-CT-2005-511234) e Hermione (EC contract 226354