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RSS FeedsRemote Sensing, Vol. 10, Pages 926: Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe (Remote Sensing)

 
 

18 june 2018 11:00:47

 
Remote Sensing, Vol. 10, Pages 926: Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe (Remote Sensing)
 


Sentinel-2 is a constellation of two satellites launched by the European Space Agency (ESA), respectively on 23 June 2015 and 7 March 2017, to map geophysical parameters over land surfaces. ESA provides Level 2 bottom-of-atmosphere reflectance (BOA) products (ESA-L2A) for Europe, with plans for operational global coverage, as well as the Sen2Cor (S2C) offline processor. In this study, aerosol optical thickness (AOT), precipitable water vapour (WVP) and surface reflectance from ESA-L2A products are compared with S2C output when using identical input Level 1 radiance products. Additionally, AOT and WVP are validated against reference measurement. As ESA and S2C share the same input and atmospheric correction algorithm, it was hypothesized that they should show identical validation performance and that differences between products should be negligible in comparison to the uncertainty of retrieved geophysical parameters due to radiometric uncertainty alone. Validation and intercomparison was performed for five clear-sky growing season dates for each of three ESA-L2A tiles selected to span a range of vegetation and topography as well as to be close to the AERONET measurement site. Validation of S2C (ESA) products using AERONET site measurements indicated an overall root mean square error (RMSE) of 0.06 (0.07) and a bias of 0.05 (0.09) for AOT and 0.20 cm (0.22 cm) and the bias was −0.02 cm (−0.10 cm) for WVP. Intercomparison of S2C-L2A and ESA-L2A showed an overall agreement higher than 99% for scene classification (SCL) maps and negligible differences for WVP (RMSE under 0.09 and R2 above 0.99). Larger disagreement was observed for aerosol optical thickness (AOT) (RMSE up to 0.04 and R2 as low as 0.14). For BOA reflectance, disagreement between products depends on vegetation cover density, topography slope and spectral band. The largest differences were observed for red-edge and infrared bands in mountainous vegetated areas (RMSE up to 4.9% reflectance and R2 as low as 0.53). These differences are of similar magnitude to the radiometric calibration requirements for the Sentinel 2 imager. The differences had minimal impact of commonly used vegetation indices (NDVI, NDWI, EVI), but application of the Sentinel Level 2 biophysical processor generally resulted in proportional differences in most derived vegetation parameters. It is recommended that the consistency of ESA and S2C products should be improved by the developers of the ESA and S2C processors.


 
41 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 10, Pages 927: Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine (Remote Sensing)
Remote Sensing, Vol. 10, Pages 925: Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers (Remote Sensing)
 
 
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