# Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
## 1 Introduction  
Sentinel-2 is a constellation of satellites, Sentinel-2A and Sentinel-2B, launched by the European Space Agency (ESA) on 23 June 2015 and 7 March 2017, respectively. They carry a virtually identical decametric resolution multi-spectral imager (MSI) having four bands at 10 m, six bands at 20 m, and three bands at 60 m spatial resolution. The latter are dedicated to atmospheric corrections and cloud screening [1]. Together, the Sentinel 2 imagers provide better than 5-day revisits of the Earth's land surfaces. As a part of the Global Monitoring for Environment and Security (GMES) space segment, they provide satellite data that can be used to map widely used land surface variables including global land cover (GLC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), leaf chlorophyll A+B concentration (Cab), and leaf water content (Cw) [2]. Sentinel-2 Level 1C Top-Of-Atmosphere reflectance products (L1C-TOA), acquired since July 2015, are provided by ESA on the Copernicus Open Access Hub [3]. L1C-TOA products satisfy or exceed specifications for radiometric performance with typical radiometric calibration uncertainty under \(\sim\)3% and worst-case uncertainties of \(\sim\)5% [4; 5]. ESA provides bottom of atmosphere (BOA) reflectance products (ESA-L2A) based on an implementation of the Sen2 Cor algorithm [6], for estimation of vegetation biophysical parameters. ESA also provides an offline version of the Sen2 Cor L2A processor (S2C-L2A) to produce BOA reflectance (S2C-L2A) products [7]. Both ESA-L2A and S2C-L2A products include BOA reflectance as well as maps of aerosol optical thickness at 550 nm (AOT), precipitable water vapor (WVP), scene classification (SCL), quality indicators for cloud (CLW) and snow cover probability (SNW). S2C-L2A products have been used for a variety of studies, including vegetation related studies [8; 9], forestry [10; 11], geological remote sensing [5], glassier remote sensing [12], land use classification [13], and assessing water quality [14; 15]. ESA-L2A products are currently available for Europe with plans for worldwide coverage [16]. In terms of the quality of Level 2 bottom of atmosphere reflectance, [PERSON] [17] assessed S2C-L2A products with reference measurements from fifteen Aerosol Robotic Network (AERONET, [18]) monitoring stations. They found an overall accuracy of 81% for SLC maps, and a root mean square error (RMSE) of 0.035, 0.29 cm and up to 0.04 respectively for AOT, WVP and BOA reflectance. [PERSON] [19] reported an RMSE of under \(\sim\)0.05 when comparing S2C-L2A BOA reflectance to in situ multispectral measurements at four dates at each of 10 agricultural plots. [PERSON] [20] reported an overall RMSE of 0.013, 0.147 and 0.346 cm respectively for AOT, WVP and BOA reflectance when comparing clear-sky S2C-L2A products to in situ estimates using AERONET measurements over 16 sites worldwide. For BOA reflectance, RMSE was below 0.005 + 0.05 xBOA reflectance across all comparisons. They also intercompared eight atmospheric correction algorithms applied to the same L1C-TOA scenes. The average difference between SEN2 COR and the other seven algorithms ranged from \(+0.016/-0.013\), \(+0.018/-0.051\), and \(+0.177/-0.065\) cm for BOA reflectance, AOT and WVP, respectively. Quality assessment of downstream Level 2 and 3 products has been reported by some producers [21; 22] and third-party quality assessment is ongoing [see this issue xx]. In most cases, these assessments have been performed using S2C-L2A products as input. However, the forthcoming availability of global ESA-L2A products suggests that it is important to validate both products and to intercompare them to quantify differences that may impact downstream products. Simultaneous validation and intercomparison ESA-L2A and S2C-L2A products has not been performed. While one may assume that the ESA-L2A products and S2C-L2A products are identical, this has yet to be verified in a systematic manner.