Generation of Analysis Ready Dataset - Sentinel-2 Mission category.
The Sen2Like, [1] demonstration processor has been developed by ESA in the framework of the EU Copernicus programme (https://www.copernicus.eu/).
The main goal of Sen2Like is to generate Sentinel-2 like harmonised/fused surface reflectances with higher periodicity by integrating additional compatible optical mission sensors.
It is a contribution to on going worldwide initiatives (*NASA-HLS, Force, CESBIO [2],[3]) undertook to facilitate higher level processing starting from harmonized data.
The Sen2Like framework is a scientific and open source software. In its current implementation version (December 2022), it combines Landsat-8 and Sentinel-2 data products. Level 1 and Level 2 input Landsat 8-9 (LS8-9) products are processed to be harmonized with Sentinel-2 data (S2). The two following ARD product types are generated:
This harmonisation process increases the theoretical number of acquisitions of this virtual constellation (95 products/year) by 30 % with respect to Sentinel-2 (S2A & S2B) only acquisitions (73 products/year) and promotes the pixel-based analysis with the extraction of fit-for-purpose dense time series, essential for bio-geophysical variables monitoring for instance.
Regardless Missions, Product Type, Gridded data are delivered, the S2 tiling system is based on the Military Grid Reference System (MGRS).
Since Sen2Like 4.3.0, in addition to Sentinel-2 and Landsat missions, there have been an effort to integrate the PRISMA Earth Observation mission, which is a medium-resolution hyperspectral imaging satellite, developed, owned and operated by ASI (Agenzia Spaziale Italiana).
Sen2like software supports the PRISMA L1 products through the usage of a pre-processor named "prisma4sen2like" that transforms PRISMA L1 products into an internal format: Sentinel-2 PRISMA (S2P) L1C products, spectrally aggregated into 13 Sentinel-2 bands and projected into L1C Sentinel-2 geometry (without refinement on Sentinel-2 GRI).
The processing workflow is based on following algorithms:
Beside these features, the user specifies the geographic footprint of multi temporal data stack. It is therefore possible, to cover large geographic extent with a seamless image mosaic.
It is worth noting that the overall accuracy of your final ARD product strongly depends on the accuracy of sen2like auxiliary data. Two categories of auxiliary data are important: the raster reference for geometric corrections and the meteorological data for atmospheric corrections. Regarding atmospheric corrections, one possibility is to use data from the Copernicus Atmosphere Monitoring Service [9]. The Sen2Like team prepared a dedicated CAMS monthly dataset for the Year 2020, available from here. Please refer to this short description for additional information.
For further details on the format specification of the harmonized products or the functionalities of the Sen2Like software, please refer to the Product Format Specification, and the User Manual v1.9.
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And the following references :
Learn how to use Sen2Like, have a look at the User Manual.
Get help, contact us at sen2like@telespazio.com.
Follow the Sen2Like project on ResearchGate.
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