Sentinel-3 coverage-driven mission design: Coupling of orbit selection and instrument design

Cornara, Stefania; Pirondini, Fabrizio; Palmade, J. L.

doi:10.1016/j.actaastro.2017.08.017

Cited by 2 publications

(1 citation statement)

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“…The land products are disseminated at two levels: In the case of OLCI, RR products are distributed in the form of stripes (stretching from pole to pole) and FR products are disseminated as frames [16] (pieces of those stripes). Unlike fixed tiles of Sentinel-2 products, OLCI stripes and frames correspond to one of 365 orbits [17]. In this way, a single point can be viewed from up to 27 different orbits (according to the repeat cycle).…”

Section: Products Overviewmentioning

confidence: 99%

Google Earth Engine Sentinel-3 OLCI Level-1 Dataset Deviates from the Original Data: Causes and Consequences

2021

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In this study, we demonstrate that the Google Earth Engine (GEE) dataset of Sentinel-3 Ocean and Land Color Instrument (OLCI) level-1 deviates from the original Copernicus Open Access Data Hub Service (DHUS) data by 10–20 W m−2 sr−1μμm−1 per pixel per band. We compared GEE and DHUS single pixel time series for the period from April 2016 to September 2020 and identified two sources of this discrepancy: the ground pixel position and reprojection. The ground pixel position of OLCI product can be determined in two ways: from geo-coordinates (DHUS) or from tie-point coordinates (GEE). We recommend using geo-coordinates for pixel extraction from the original data. When the Sentinel Application Platform (SNAP) Pixel Extraction Tool is used, an additional distance check has to be conducted to exclude pixels that lay further than 212 m from the point of interest. Even geo-coordinates-based pixel extraction requires the homogeneity of the target area at a 700 m diameter (49 ha) footprint (double of the pixel resolution). The GEE OLCI dataset can be safely used if the homogeneity assumption holds at 2700 m diameter (9-by-9 OLCI pixels) or if the uncertainty in the radiance of 10% is not critical for the application. Further analysis showed that the scaling factors reported in the GEE dataset description must not be used. Finally, observation geometry and meteorological data are not present in the GEE OLCI dataset, but they are crucial for most applications. Therefore, we propose to calculate angles and extraterrestrial solar fluxes and to use an alternative data source—the Copernicus Atmosphere Monitoring Service (CAMS) dataset—for meteodata.

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Section: Products Overviewmentioning

confidence: 99%