Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues (original) (raw)
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Consistent merging of satellite ocean color data sets using a bio-optical model
Remote Sensing of Environment, 2005
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 (k)) 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 (k) 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 (k) 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.
Merger of Ocean Color Data from Multiple Satellite Missions within the SIMBIOS Project
Storage and Retrieval for Image and Video Databases, 2000
The purpose of data merger activities undertaken by the National Aeronautic and Space Administration's (NASA) Sensor Intercomparison and Merger for Biological and Interdisciplinary Studies (SIMBIOS) Project is to create scientific quality ocean color data encompassing measurements from multiple satellite missions. The fusion of data from multiple satellites will improve the quality of ocean color products over single-mission data sets by
Quality and Consistency of the NASA Ocean Color Data Record
The NASA Ocean Biology Processing Group (OBPG) recently reprocessed the multi-mission ocean color time-series from SeaWiFS, MODIS-Aqua, and MODIS-Terra using common algorithms and improved instrument calibration knowledge. Here we present an analysis of the quality and consistency of the resulting ocean color retrievals, including spectral water-leaving reflectance, chlorophyll a concentration, and diffuse attenuation. Statistical analysis of satellite retrievals relative to in situ measurements will be presented for each sensor, as well as an assessment of consistency in the global time-series for the overlapping periods of the missions. Results will show that the satellite retrievals are in good agreement with in situ measurements, and that the sensor ocean color data records are highly consistent over the common mission lifespan for the global deep oceans, but with degraded agreement in higher productivity, higher complexity coastal regions.
Inter-Sensor Comparison of Satellite Ocean Color Products from GOCI and MODIS
The Geostationary Ocean Color Imager (GOCI) was launched by the Republic of Korea on 27 June 2010 and is the first geostationary ocean color sensor in orbit that provides coastal bio-optical properties (such as chlorophyll concentration, absorption and backscattering coefficients) at unprecedented high spatial and temporal resolution. GOCI has 8 spectral bands covering 2,500 km × 2,500 km (centered 130E, 36N) at 500 m spatial resolution. Unlike polar-orbiting satellites which provide only one or two images of the same geographic area per day, GOCI collects images every hour from 10am to 5pm (eight images per day). This high temporal resolution can lead to improved understanding of short time scale bio-optical variability in the ocean surface. However, retrieving ocean color products accurately can be challenging particularly in turbid coastal waters due to imperfect atmospheric correction. In this study, we process GOCI data through US Naval Research Lab's Automated Processing S...
Earth System Science Data
A global compilation of in situ data is useful to evaluate the quality of ocean-colour satellite data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018. Observations of the following variables were compiled: spectral remotesensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties, spectral diffuse attenuation coefficients and total suspended matter. The data were from multi-project archives acquired via open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenization, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) was propagated throughout the work and made available in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. This paper also describes the changes that were made to the compilation in relation to the previous version (Valente et al., 2016
A compilation of global bio-optical in situ data for ocean-colour satellite applications
Earth System Science Data Discussions, 2016
A compiled set of in situ data is important to evaluate the quality of ocean-olour satellite data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GEPCO), spans between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll-a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and sp...
Sensors, 2019
Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.