The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI): Algorithm Improvements, Spatiotemporal Consistency and Continuity with the MERIS Archive

Pastor-Guzman, Julio; Brown, Luke A.; Morris, Harry; Bourg, Ludovic; Goryl, Philippe; Dransfeld, Steffen; Dash, Jadunandan

doi:10.3390/rs12162652

Cited by 20 publications

(12 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The OLCI instrument includes 21 spectral bands (400-1020 nm), 15 taken from the precursor sensor MERIS on board the ENVISAT satellite, and 6 extra channels that were included to improve the atmospheric and aerosol corrections (Table 1). OLCI was initially designed for ocean monitoring, but it has been successfully used in several land applications [42,43]. MERIS data were the basis of the first global BA dataset within the FireCCI [44], and therefore, it was expected that OLCI would show similar BA detection capabilities, although with better temporal resolution (3-day revisit time for MERIS and 1 day for OLCI, when the two S3s are available).…”

Section: Olci Surface Directional Reflectancesmentioning

confidence: 99%

Implementation of the Burned Area Component of the Copernicus Climate Change Service: From MODIS to OLCI Data

et al. 2021

View full text Add to dashboard Cite

This article presents the burned area (BA) product of the Copernicus Climate Change Service (C3S) of the European Commission. This product, named C3SBA10, is based on the adaptation to Sentinel-3 OLCI images of a BA algorithm developed within the Fire Climate Change Initiative (FireCCI) project, which used MODIS data. We first reviewed the adaptation process and then analysed the results of both products for common years (2017–2019). Comparisons were performed using four different grid sizes (0.05°, 0.10°, 0.25°, and 0.50°). Annual correlations between the two products ranged from 0.94 to 0.99. Global BA estimates were found to be more similar when the two Sentinel-3 satellites were active (2019), as the temporal resolution was closer to that of the MODIS sensor. Global validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 16 and 21% and omission errors from 48 to 50%, similar to those found in the FireCCI product. The temporal reporting accuracy was also validated using 19 million active fires. In total, 87% of the detections were made within 10 days after the fire by both products. The high consistency between both products ensures global BA data provision from 2001 to the present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository.

show abstract

Section: Olci Surface Directional Reflectancesmentioning

confidence: 99%

Implementation of the Burned Area Component of the Copernicus Climate Change Service: From MODIS to OLCI Data

et al. 2021

View full text Add to dashboard Cite

show abstract

“…We found only three studies that used the time series of OLCI so far. Pastor-Guzman et al [40] demonstrated that OTCI is a robust successor of MTCI: the seasonal cycles of both indices in various ecosystems were identical. Rather short (30 days) time series of level-1 data were used for burned area estimation with OLCI-derived NDVI [41].…”

Section: Time Series Applications For Landmentioning

confidence: 99%

“…Overall, OLCI (mostly MERIS) data have been widely used for land monitoring in time. In studies where mapping was not an objective of the study, a spatial smoothing of 3-by-3 pixel area has been used [32,40,52].…”

Section: Time Series Applications For Landmentioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

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.

show abstract

“…Regarding the retrieval of these variables from satellite data streams, most traditional methods relied on parametric regression approaches. These methods assume an explicit relationship between spectral observations (or vegetation indices) and in-field measured variables [24][25][26]. Parametric regression approaches require only low computational resources.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.

show abstract

The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI): Algorithm Improvements, Spatiotemporal Consistency and Continuity with the MERIS Archive

Cited by 20 publications

References 58 publications

Implementation of the Burned Area Component of the Copernicus Climate Change Service: From MODIS to OLCI Data

Implementation of the Burned Area Component of the Copernicus Climate Change Service: From MODIS to OLCI Data

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

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

Contact Info

Product

Resources

About