Sentinel-2 imagery of Switzerland

We processed Sentinel-2 images from 2017 to 2023 for Switzerland with the Software FORCE (Frantz 2019). The respective parameter files can be found here: Github. All the images consist of several TB and therefore, access will be granted upon request. The available bands (in spatial reference system EPSG 3035) are the following: Red, Green, Blue, NIR, Red-Edge-1, Red-Edge-2, Red-Edge-3, SWIR-1, SWIR-2 The available indices (in spatial reference system EPSG 3035) are the following: CCI, CIRE, NDWI/NDMI, NDVI, EVI Further indices might be provided upon request.

Processing

After downloading Level 1 data for the 14 Sentinel-2 tiles covering Switzerland (T31TGN, T32TLT, T32TMT, T32UMU, T32TNT, T32TPT, T31TGM, T32TLS, T32TMS, T32TNS, T32TPS, T32TLR, T32TMR, T32TNR) with an estimated cloud cover of less than 80 \%, we processed the data further with FORCE v. 3.7.8-12. FORCE was used for radiometric correction, including atmospheric and topographic corrections using the digital elevation model from Copernicus Land Monitoring Service (EU-DEM v. 1.1), with a 25 m spatial resolution, a bidirectional reflectance distribution function (BRDF) correction, and an adjacency effect correction. For cloud masking, the improved Fmask algorithm with buffers of 300 m for clouds, 90 m for shadows, and 30 m for snow were used (Frantz et al. 2018; Zhu et al. 2015). All bands were resampled using cubic convolution to 10 m spatial resolution. Shadows, low and poor illumination as well as saturation were masked out. The co-registration of all images was made with monthly composites of near-infrared Landsat Collection 2 images from 2014 to 2021 (Rengarajan et al. 2020;Rufin et al. 2021). With this the geometric consistency improved. The processed images are available in tiles of 30 by 30 km.

Example images

Uploaded is an example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The date of the image is 24.07.2018.

• Swiss National Science Foundation (Grant/Award: 200021_184605)

• D. Frantz, “FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond,” Remote Sensing, vol. 11, no. 9, 2019.

• D. Frantz, E. Haß, A. Uhl, J. Stoffels, and J. Hill, “Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects,” Remote Sensing of Environment, vol. 215, pp. 471–481, 2018.

• R. Rengarajan, M. Choate, J. Storey, S. Franks, E. Micijevic, J. J. Butler, X. Xiong, and X. Gu, “Landsat Collection-2 geometric calibration updates,” 2020.

• P. Rufin, D. Frantz, L. Yan, and P. Hostert, “Operational Coregistration of the Sentinel-2A/B Image Archive Using Multitemporal Landsat Spectral Averages,” IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 4, pp. 712–716, 2021.

• Z. Zhu, S. Wang, and C. E. Woodcock, “Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images,” Remote Sensing of Environment, vol. 159, pp. 269–277, 2015.