Consistent merging of satellite ocean color data sets using a bio-optical model

While many (and more on the way) ocean color satellite sensors presently provide routine observations of ocean biological processes, limited concrete effort has taken place to demonstrate how these data can be used together in any systematic way. One obvious way is to merge these data streams together to provide robust merged climate data records with measurable uncertainty bounds. Here, we present and implement a formalism for merging global satellite ocean color data streams to produce uniform data products. Normalized water-leaving radiances (L_wN(λ)) from SeaWiFS and MODIS are used together in a semianalytical ocean color merging model to produce global retrievals of 3 biogeochemically relevant variables (chlorophyll, combined dissolved and detrital absorption coefficient, particulate backscattering coefficient). The model-based merging approach has various benefits over techniques that blend end products, such as chlorophyll concentrations; (1) merging at the level of water-leaving radiance ensures simultaneity and consistency of the retrievals, (2) it works with single or multiple data sources regardless of their specific bands, (3) it exploits band redundancies and band differences, (4) it can account for the uncertainties of the incoming L_wN(λ) data streams and, (5) it provides confidence intervals for the derived products. These features are illustrated through several examples of ocean color data merging using SeaWiFS and MODIS Terra and Aqua L_wN(λ) imagery. Compared to each of the original data source, the products derived from the merging procedure show enhanced global daily coverage and lower uncertainties in the retrieved variables.     

Inter-comparison of OC-CCI chlorophyll-a estimates with precursor data sets

Ocean colour is the only essential climate variable that targets a biological variable (chlorophyll-a concentration (chl-a)) and is also amenable to remote sensing at the global scale. However, the finite lifetime of individual ocean-colour sensors, and the differences in their characteristics increase the difficulty of creating a long-term, consistent, ocean-colour time series that meets the requirements of climate studies. The Ocean Colour Climate Change Initiative (OC-CCI), a European Space Agency programme, has recently produced a time series of satellite-based ocean-colour products at the global scale, merging data from three sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer on the Aqua Earth Observing System (MODIS-Aqua), and Medium Resolution Imaging Spectrometer (MERIS), while attempting to reduce inter-sensor biases.In this work we present a comparison between the OC-CCI chlorophyll-a product and precursor satellite-derived data sets, from both single missions (SeaWiFS, MODIS-Aqua, and MERIS) and multi-mission products (global ocean colour (GlobColour) and Making Earth Science Data Records for Use in Research Environments (MEaSUREs)). To this end, OC-CCI global monthly composites are compared to the similar products offered by single-mission and multi-mission records. Our results indicate that the OC-CCI product provides a higher number of observations. Comparing the observations that match with precursors, the OC-CCI product was generally most similar to the single-mission products. Relationships between OC-CCI and other precursors did not change significantly during a common and continuous period, and, on average the root-mean-square differences between log-transformed chlorophyll-a concentration are below or equal to 0.11. Further, when considering variability that could arise when merging data from different sources, it is shown that the OC-CCI product is a longer term constant than those from other multi-mission initiatives studied here.     

An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation

Global satellite ocean color instruments provide the scientific community a high-resolution means of studying the marine biosphere. Satellite data product validation and algorithm development activities both require the substantial accumulation of high-quality in-situ observations. The NASA Ocean Biology Processing Group maintains a local repository of in-situ marine bio-optical data, the SeaWiFS Bio-optical Archive and Storage System (SeaBASS), to facilitate their ocean color satellite validation analyses. Data were acquired from SeaBASS and used to compile a large set of coincident radiometric observations and phytoplankton pigment concentrations for use in bio-optical algorithm development. This new data set, the NASA bio-Optical Marine Algorithm Data set (NOMAD), includes over 3400 stations of spectral water-leaving radiances, surface irradiances, and diffuse downwelling attenuation coefficients, encompassing chlorophyll a concentrations ranging from 0.012 to 72.12 mg m^− 3. Metadata, such as the date, time, and location of data collection, and ancillary data, including sea surface temperatures and water depths, accompany each record. This paper describes the assembly and evaluation of NOMAD, and further illustrates the broad geophysical range of stations incorporated into NOMAD.     

Intercomparison of ocean colour band-ratio algorithms for chlorophyll concentration in the Subtropical Front east of New Zealand

This study intercompared the performance of eight band-ratio chlorophyll-a algorithms which together can be used to process measurements from the ocean colour satellite sensors CZCS, OCTS, SeaWiFS, MODIS, MERIS, and GLI. The study area included Subtropical, Subtropical Front and Subantarctic waters east of New Zealand, and Case 1 waters of the New Zealand northeast continental shelf. Over 170 co-incident measurements of spectral normalised water-leaving radiance and near-surface concentration of chlorophyll-a were made on nine research voyages between 1998 and 2000. The studentised bootstrap method was used to identify statistically significant bias in algorithm products relative to in situ measurements. The band-ratio algorithms used by CZCS, OCTS and SeaWiFS missions systematically underestimated chlorophyll-a concentration in the offshore regions by between 21% and 45%, but showed no systematic bias in the continental shelf waters. The band-ratio algorithms applicable to the MODIS and MERIS sensors had no clear bias with respect to in situ measurements in offshore waters, but had a positive bias of 20% over the continental shelf. The proposed GLI band-ratio algorithm led to estimates that were negatively biased with respect to in situ measurement offshore (− 30%), and positively biased over the continental shelf (20%). The results were consistent with unusually high values of absorption in the blue part of the spectrum (443-490 nm) compared to the green part (∼ 550 nm) by phytoplankton pigments in the offshore waters, and high chlorophyll-specific absorption over the continental shelf.     

Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach

During its lifetime, a space-borne ocean color sensor provides world-wide information about important biogeochemical properties of the upper ocean every 2 to 4 days in cloudless regions. Merging simultaneous or complementary data from such sensors to obtain better spatial and temporal coverage is a recurring objective, but it can only be reached if the consistency of the sensor-specific products, as delivered by the various Space Agencies, has first been carefully examined. The goal of the present study is to provide a procedure for establishing a coherency of open ocean (Case-1 waters) data products, for which the various data processing methods are sufficiently similar. The development of the procedure includes a detailed comparison of the marine algorithms used (after atmospheric corrections) by space agencies for the production of standard products, such as the chlorophyll concentration, [Chl], and the diffuse attenuation coefficient, K_d. The MODIS-Aqua, SeaWiFS and MERIS [Chl] products agree over a wide range, between ∼ 0.1 and 3 mg m^− 3, whereas increasing divergences occur for oligotrophic waters ([Chl] (from 0.02 to 0.09 mg m^− 3). For the K_d(490) coefficient, different algorithms are in use, with differing results. Based on a semi-analytical reflectance model and hyperspectral approach, the present work proposes a harmonization of the algorithms allowing the products of the various sensors to be comparable, and ultimately, meaningfully merged (the merging procedures themselves are not examined). Additional potential products, obtained by using [Chl] as an intermediate tool, are also examined and proposed. These products include the thickness of the layer heated by the sun, the depth of the euphotic zone, and the Secchi disk depth. The physical limitations in the predictive skill of such downward extrapolations, made from information concerning only the upper layer, are stressed.     

Validation of SeaWiFS and MODIS aerosol products with globally distributed AERONET data

The validation of aerosol products derived from ocean color missions is required for the assessment of their uncertainties and as a diagnostic for the atmospheric correction schemes used for determining the ocean apparent optical properties. A comprehensive validation of the aerosol products obtained from the ocean color missions SeaWiFS and MODIS is presented; it relies on the field observations collected at 85 AERONET sites and is completed by preliminary results obtained with the data of the maritime AERONET component. A robust match-up selection protocol yields approximately 7000 match-ups for each sensor. The median absolute relative difference for the aerosol optical thickness τ_a increases from 20-22% at 443 nm to 45-48% in the near-infrared. The validation statistics are comparable for both sensors but MODIS results appear degraded particularly for sites located on isolated islands. The median absolute difference is approximately 0.03 at all wavelengths. Results are further analyzed for specific geographic regions or groups of sites selected to represent oceanic, continental, or desert dust conditions. Importantly, the match-up sets appear generally representative of the regional natural variability in τ_a amplitude and spectral shape, with the notable exception of high τ_a conditions that are excluded. An important finding is the underestimate by the atmospheric correction of the Ångström exponent α, with a median bias of − 0.52. This underestimate is apparent even at low α values and regularly increases with α. This discrepancy in τ_a spectral shape might result from an inappropriate set of candidate aerosol models and/or uncertainties in the calibration at the near-infrared bands. As the validation data base is expanded and updated in relation to new versions of the processing chains, this work provides a benchmark for the assessment of the aerosol products derived from the SeaWiFS and MODIS ocean color missions.     

Detection of blue-absorbing aerosols using near infrared and visible (ocean color) remote sensing observations

An algorithm is presented, which is designed to identify blue-absorbing aerosols from near infrared and visible remote-sensing observations, as they are in particular collected by satellite ocean color sensors. The technique basically consists in determining an error budget at one wavelength around 510 nm, based on a first-guess estimation of the atmospheric path reflectance as if the atmosphere was of a maritime type, and on a reasonable hypothesis about the marine signal at this wavelength. The budget also includes the typical calibration uncertainty and the natural variability in the ocean optical properties. Identification of blue-absorbing aerosols is then achieved when the error budget demonstrates a significant over-correction of the atmospheric signal when using non-absorbing maritime aerosols. Implementation of the algorithm is presented, and its application to real observations by the MERIS and SeaWiFS ocean color sensors is discussed. The results demonstrate the skill of the algorithm in various regions of the ocean where absorbing aerosols are present, and for two different sensors. A validation of the results is also performed against in situ data from the AERONET, and further illustrates the skill of the algorithm and its general applicability.     

Technical note The SeaWiFS Automatic Data Processing System (SeaAPS)

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) was designed to measure ocean colour, the spectral variation of water-leaving radiance that can be related to the concentrations of phytoplankton pigments, coloured dissolved organic material and suspended particulate matter. The Dundee Satellite Receiving Station records and archives 1-km imagery covering the European shelf-seas, north-east Atlantic Ocean and Mediterranean Sea, which is subsequently processed in near-real time by Plymouth Marine Laboratory using SeaWiFS Automatic Processing System (SeaAPS). SeaWiFS imagery is combined with contemporary Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data to provide products, supplied via the World Wide Web, that are used within many areas of oceanographic research.     

The ESA Medium Resolution Imaging Spectrometer MERIS a review of the instrument and its mission

Following the successful operations of the ERS-1 and 2 satellites which are mainly dedicated to physical oceanography and ice observations from space, the European Space Agency (ESA) developed a multidisciplinary Earth observation instrument within its polar Earth Observation Programme with a focus on biological ocean observations. The Medium Resolution Imaging Spectrometer (MERIS) will be launched onboard Envisat-1 in the 1999-2000 time frame, providing a European remote sensing capability for observing for example oceanic biology and marine water quality through observations of water colour. MERIS will have a medium spectral and high radiometric resolution and a dual spatial resolution, within a global mission, covering open ocean and coastal zone waters, important aspects of the atmosphere, and large ecosystems over land. The global mission of MERIS will have a major contribution to scientific projects aimed at greater understanding of the role of oceans and ocean productivity in the climate system and our ability to forecast change through models. Secondary objectives of the MERIS mission will be directed to the measurement of atmospheric parameters associated with clouds, water vapour and aerosols in addition to land surface parameters, important in particular for the understanding of vegetation processes. In advance of the launch of MERIS, algorithms are being developed for the interpretation of MERIS observations and dedicated studies are ongoing to establish the means of validating the data products. The aim of this paper is to provide a comprehensive overview of the MERIS concept, its mission and data products in context of the driving scientific requirements.