Sen2Like

Generation of Analysis Ready Dataset - Sentinel-2 Mission category.

About

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 (LS8) products are processed to be harmonized with Sentinel-2 data (S2). The two following ARD product types are generated:

• Harmonized Surface Reflectance Products (Level 2H) - at 30m of resolution,
• Fused Surface Reflectance Products (Level 2F) - at 10-20m of resolution.

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:

• Geometric Corrections including registration to common reference & the stitching [4],
• Atmospheric Corrections by using SMAC [5] relying on auxiliary meteorological data,
• Application of Spectral Band Adjustment Factor (SBAF) [2],
• Transformation to Nadir BRDF-normalized Reflectance (NBAR) [6],[7],
• Production of LS8 High Resolution 10 m pixel spacing data (Fusion) [8].

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 v1.8.

Publications and Contacts

Yearning to know more ? Check out

• poster Sen2like: A solution for Harmonization and Fusion of Sentinel-2 and Landsat 8/9 data, JACIE 2023
• A Sen2Like Relaxing Video prepared for ESA EO PHI-WEEK 2020
• A Sen2Like Time Lapse including NDVI graphic prepared for QWG#12

And the following research papers :

• [1] S. Saunier, J. Louis, V. Debaecker et al., "Sen2like, A Tool To Generate Sentinel-2 Harmonised Surface Reflectance Products - First Results with Landsat-8," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 5650-5653, doi: 10.1109/IGARSS.2019.8899213.
• [2] Claverie, Martin, Junchang Ju, Jeffrey G. Masek, Jennifer L. Dungan, Eric F. Vermote, Jean-Claude Roger, Sergii V. Skakun, et Christopher Justice. "The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set". Remote Sensing of Environment 219 (15 décembre 2018): 145‑61. (https://doi.org/10.1016/j.rse.2018.09.002).
• [3] Frantz, David. "FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond". Remote Sensing 11, nᵒ 9 (janvier 2019): 1124. (https://doi.org/10.3390/rs11091124).
• [4] S. Kocaman, S., Debaecker, V., Bas, S., Saunier, S., Garcia, K., and Just, D. "Investigation on the Global Image Datasets for the absolute geometric quality assessment of MSG SEVIRI Imagery", in Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1339–1346, 2020 (https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1339-2020)
• [5] Rahman, H., & Dedieu, G. "SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum." REMOTE SENSING, 15(1), 123-143, 1994.
• [6] Claverie, Martin, Eric Vermote, Belen Franch, Tao He, Olivier Hagolle, Mohamed Kadiri, et Jeff Masek. "Evaluation of Medium Spatial Resolution BRDF-Adjustment Techniques Using Multi-Angular SPOT4 (Take5) Acquisitions". Remote Sensing 7, nᵒ 9 (18 septembre 2015): 12057‑75. (https://doi.org/10.3390/rs70912057)
• [7] Roy, David P., Jian Li, Hankui K. Zhang, Lin Yan, Haiyan Huang, et Zhongbin Li. Examination of Sentinel-2A Multi-Spectral Instrument (MSI) Reflectance Anisotropy and the Suitability of a General Method to Normalize MSI Reflectance to Nadir BRDF Adjusted Reflectance". Remote Sensing of Environment 199 (septembre 2017): 25‑38. (https://doi.org/10.1016/j.rse.2017.06.019)
• [8] Sen2Like User Manual
• [9] Copernicus Atmosphere Monitoring Service
• [10] Saunier, S.; Pflug, B.; Lobos, I.M.; Franch, B.; Louis, J.; De Los Reyes, R.; Debaecker, V.; Cadau, E.G.; Boccia, V.; Gascon, F.; Kocaman, S. Sen2Like: Paving the Way towards Harmonization and Fusion of Optical Data. Remote Sens. 2022, 14, 3855. (https://doi.org/10.3390/rs14163855)

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.

You are using Sen2Like? Spread the word, and use the #sen2like hashtag in your tweets!