Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples

Abstract

This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency's (ESA) Sen2Cor algorithm, the platform processes ESA's Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data . Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R 2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R 2 = 0.83) and a RMSE of 0.32 m 2 /m 2 (12% of mean value).

Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

“…In some cases, correlation coefficients appear slightly lower for band 1, likely because of the lower spatial resolution (60 m for MSI). In general, however, the presented results are also in good agreement with the ones presented by Vuolo et al, [20], who found determination coefficients ranging from 0.90 to 0.96 for the six homologous bands (B1 was not considered). The performed tests highlight that one issue is the choice of the near-infrared band.…”

Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy

“…The results of the inter-comparison of official products were not unexpected, as the use of different radiometric correction codes, which are specifically adapted to each sensor, provided good outcomes for their corrected imagery, but not the best correlations between inter-sensor data. Vuolo et al [90] evaluated the Sen2Cor-SNAP and 6S-LaSRC coherence at six european test sites, obtaining an r 2 = 0.9, similar to the r 2 = 0.92 obtained in the present study. On the other hand, PIA-MiraMon showed a good inter-sensor correlation (r 2 = 0.96), providing robustly corrected data, because it is based on common radiometric areas.…”

Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan

“…Remote sensing is an effective tool for detecting damaged areas because it can be used to document damage to large areas without direct access to the affected area (Yamazaki and Matsuoka, 2007;Rathje and Adams, 2008;Dell'Acqua and Gamba, 2012). Immense improvement to the accessibility of remote-sensing imagery data and geospatial data processing tools has been achieved over the last several years (Vuolo et al, 2016;Korosov et al, 2016). A dramatic increase in the number of satellite, aircraft, and unmanned aerial vehicle (UAV) sensors has been observed as well.…”