Assessment of the Geometric Quality of Sentinel-2 Data

Abstract

ABSTRACT:High resolution (10 m and 20 m) optical imagery satellite Sentinel-2 brings a new perspective to Earth observation. Its frequent revisit time enables monitoring the Earth surface with high reliability. Since Sentinel-2 data is provided free of charge by the European Space Agency, its mass use for variety of purposes is expected. Quality evaluation of Sentinel-2 data is thus necessary. Quality analysis in this experiment is based on comparison of Sentinel-2 imagery with reference data (orthophoto). From the possible set of features to compare (point features, texture lines, objects, etc.) line segments were chosen because visual analysis suggested that scale differences matter least for these features. The experiment was thus designed to compare long line segments (e.g. airstrips, roads, etc.) in both datasets as the most representative entities. Edge detection was applied to both images and corresponding edges were manually selected. The statistical parameter which describes the geometrical relation between different images (and between datasets in general) covering the same area is calculated as the distance between corresponding curves in two datasets. The experiment was conducted for two different test sites, Austria and Serbia. From 21 lines with a total length of ca. 120 km the average offset of 6.031 m (0.60 pixel of Sentinel-2) was obtained for Austria, whereas for Serbia the average offset of 12.720 m (1.27 pixel of Sentinel-2) was obtained out of 10 lines with a total length of ca. 38 km.

Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)

“…The images were downloaded directly and free of charge from the ESA server ( ). ESA provides Level-1C images, being geometrically corrected [ 56 ], with top-of-atmosphere (TOA) reflectance; and Level-2A images, being geometrically and atmospherically corrected, with top-of-canopy (TOC) reflectance. We downloaded 11 available cloud-free Level-1C acquisitions of Sentinel-2 over Valencia and Foggia study areas ( Table 2 ).…”

Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment

“…Additionally, inaccurate georeferencing might affect the precision of local GAI predictions. Gascon et al [ 47 ], expect a geolocation uncertainty of the Sentinel-2 data in most cases between 4.5–8 m (2-sigma), Pandžic et al [ 48 ] describe deviations between 6–12.7 m. Such spatial shifts may already alone put into question the potential of the Sentinel-2 data to achieve an adequate spatial resolution for precision farming. This problem could be minimized via further georeferencing steps, but we restricted our analysis to the data quality made available for public use.…”

Establishment of the Baseline for the IWRM in the Ecuadorian Andean Basins: Land Use Change, Water Recharge, Meteorological Forecast and Hydrological Modeling

“…Using remote sensing techniques, satellite images were processed to analyze LUCC. Three images were obtained: Landsat7 ETM (11 September 2009), Landsat8 OLI_TIRS (20 September 2017), and Sentinel 2B Level 1C (31 July 2019), which were ortho-rectified [31,32]. The satellite's geometric coincidence with two temporarily invariable control points was verified, taking as reference an aerial photograph obtained from the SigTierras portal [33].…”

“…For the Landsat7 ETM and Landsat8 OLI_TIR images, the conversion to reflectance was performed to top of atmosphere (TOA) and to brightness temperature according to L. Congedo (2016). For the Sentinel 2B Level 1C image, the conversion was not performed as it is already scaled in TOA [32]. In the three images, the atmospheric correction Dark Object Subtraction (DOS) was performed [34].…”