Sen2Like: Paving the Way towards Harmonization and Fusion of Optical Data

Abstract

Satellite Earth Observation (EO) sensors are becoming a vital source of information for land surface monitoring. The concept of the Virtual Constellation (VC) is gaining interest within the science community owing to the increasing number of satellites/sensors in operation with similar characteristics. The establishment of a VC out of individual missions offers new possibilities for many application domains, in particular in the fields of land surface monitoring and change detection. In this context, this paper describes the Copernicus Sen2Like algorithms and software, a solution for harmonizing and fusing Landsat 8/Landsat 9 data with Sentinel-2 data. Developed under the European Union Copernicus Program, the Sen2Like software processes a large collection of Level 1/Level 2A products and generates high quality Level 2 Analysis Ready Data (ARD) as part of harmonized (Level 2H) and/or fused (Level 2F) products providing high temporal resolutions. For this purpose, we have re-used and developed a broad spectrum of data processing and analysis methodologies, including geometric and spectral co-registration, atmospheric and Bi-Directional Reflectance Distribution Function (BRDF) corrections and upscaling to 10 m for relevant Landsat bands. The Sen2Like software and the algorithms have been developed within a VC establishment framework, and the tool can conveniently be used to compare processing algorithms in combinations. It also has the potential to integrate new missions from spaceborne and airborne platforms including unmanned aerial vehicles. The validation activities show that the proposed approach improves the temporal consistency of the multi temporal data stack, and output products are interoperable with the subsequent thematic analysis processes.

Analysing the Relationship between Spatial Resolution, Sharpness and Signal-to-Noise Ratio of Very High Resolution Satellite Imagery Using an Automatic Edge Method

“…The analysis highlighted the complexity of the topic and the importance and added value of relying on automatic and self-consistent methodology. In the future, further analyses will be dedicated to investigating the effect that super-resolution algorithms have on the quality of the resulting product, and also to analyse the performance of Virtual Constellations (VC) such as the Landsat-Sentinel VC proposed by [49]. The AEM may also be exploited to monitor the performance of satellite constellations, in particular those composed of small satellites, in order to determine whether the performance of the different satellites is coherent and whether or not it degrades with the passage of time.…”

Using a crop water stress index to determine water use efficiency in a raspberry crop in the Mediterranean Central Chile

“…In fact, using calculations of albedo from the bidirectional reflectance distribution function (BRDF) could generate an impact change. By utilizing new models such as those described by Franch et al (2019) and Saunier et al (2022), the bias of h modelling can be reduced. The difference in ET a and in situ values occurs especially during the period without vegetation in arid and semi-arid zones or areas that have complex biophysical conditions, with errors greater than 20%-30% in the estimation, which might reduce the performance of the model by 15%-20%.…”

Contribution of Photogrammetry for Geometric Quality Assessment of Satellite Data for Global Climate Monitoring

“…However, challenges still exist due to the different viewing angles, as in the case of SLSTR, spectral differences, and seasonal differences in land cover between the reference and target images. Although research on aligning radiometry of multiple sensors towards building virtual constellations exists [69], more research is needed on this aspect. Moreover, as a frequently used data type in the production of upper-level products (Level 1B or Level 2), DEMs need to be of high quality (dense, accurate, complete, and well-distributed), aligned in the same time frame as the image acquisition, represent similar objects (terrain, surface, etc.…”