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

Abstract

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.

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

“…It must also be remarked that when using the OLCI L1B collection through GEE directly, a mismatch on values over specific locations relative to original data provided by Copernicus Open Data Hub Services has been reported due to use of a different coordinate system (i.e., tie points vs. geo-coordinates) [ 106 ]. In our case, these local mismatches are not measurable when working at large scale.…”

Simulation of ecosystem fluxes with the SCOPE model: Sensitivity to parametrization and evaluation with flux tower observations

“…This allows for the retrieval of time series of vegetation traits -LAI and leaf chlorophyll content (𝐶𝑎𝑏) -with numerical, physical or hybrid approaches (Verrelst et al, 2018). Such time series carry uncertainty in representativeness due to varying viewing geometry among images, varying atmospheric conditions, and differences in pixel geo-location among orbits (Prikaziuk et al, 2021). As a result, additional filters and quality control metrics are required to obtain a realistic time series of retrieved surface properties.…”

“…Most fields around the sites in this study are small (an average European Union field is 16 ha (Eurostat) of greatly varying shape). Since we were using Sentinel-3 OLCI sensor with the nominal resolution of 300 m and the effective resolution of 700 m (Prikaziuk et al, 2021), we did an additional check that the pattern of the Sentinel-3-retrieved LAI coincides with the pattern of Sentinel-2 LAI from Copernicus high resolution vegetation phenology (HR-VPP) product (Tian et al, 2021) (Fig. 7).…”

“…(1980) model, whereas Light Use Efficiency (LUE) model by Monteith (1972) is also widely used for satellite products, for example by the MODIS team . Spatial resolution in this study was determined by the Sentinel-3 OLCI pixel size with the nominal value of 300 m and the actual footprint of 700 m (Prikaziuk et al, 2021), the temporal resolution was daily and annual. Acknowledging that these model representations vary, we compare SCOPE performance metrics to that of other models.…”