Sen2like, A Tool To Generate Sentinel-2 Harmonised Surface Reflectance Products - First Results with Landsat-8

Saunier, Sébastien; Louis, Jérôme; Debaecker, Vincent; Beaton, Thomas; Cadau, Enrico; Boccia, Valentina; Gascon, Ferran

doi:10.1109/igarss.2019.8899213

Cited by 14 publications

(15 citation statements)

References 8 publications

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“…The proposed approach is compared with the physical method HLS [28] developed for reducing the reflectance differences between Landsat-8 and Sentinel-2, thus generating smooth spectral TSs. Please note that such method is widely used from the operational view point [29].…”

Section: Resultsmentioning

confidence: 99%

“…In particular, the approach takes into account the differences in spatial resolution, atmospheric correction approaches, view geometry and radiometric characteristics of spectral bands. ESA has considered this approach as a reference work for the definition of the Sen2Like framework [29]. The objective of Sen2Like is to generate Sentinel-2 like harmonised/fused surface reflectances with higher periodicity by integrating additional compatible optical mission sensors.…”

Section: Introductionmentioning

confidence: 99%

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A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images

Sedona¹,

Paris

Cavallaro³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The combination of data acquired by Landsat-8 and Sentinel-2 Earth Observation (EO) missions produces dense Time Series (TSs) of multispectral images that are essential for monitoring the dynamics of land-cover and land-use classes across the Earth's surface with high temporal resolution. However, the optical sensors of the two missions have different spectral and spatial properties, thus they require a harmonization processing step before they can be exploited in Remote Sensing (RS) applications. In this work, we propose a workflow based on a Deep Learning (DL) approach to harmonize these two products developed and deployed on an High-Performance Computing (HPC) environment. In particular, we use a multispectral Generative Adversarial Network (GAN) with a U-Net generator and a PatchGan discriminator to integrate existing Landsat-8 TSs with data sensed by the Sentinel-2 mission. We show a qualitative and quantitative comparison with an existing physical method (National Aeronautics and Space Administration (NASA) Harmonized Landsat and Sentinel (HLS)) and analyze original and generated data in different experimental setups with the support of spectral distortion metrics. To demonstrate the effectiveness of the proposed approach, a crop type mapping task is addressed using the harmonized dense TS of images, which achieved an Overall Accuracy (OA) of 87.83% compared to 81.66% of the state-of-the-art method.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images

Sedona¹,

Paris

Cavallaro³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

Section: Resultsmentioning

confidence: 99%

A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 images

Sedona¹,

Paris²,

Cavallaro³

et al. 2021

Preprint

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“…Frantz [116] developed the FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) for the mass-processing and analysis of Landsat and S2/MSI images, generating highly ARD (hARD) and products with cloud masking, radiometric and topographic or BRDF correction, aiming to solve spatio-temporal inconsistencies caused by cloud cover. The European Space Agency (ESA) funded two programs: Sen2like [117] and Sentinel-2 for Agriculture (Sen2-Agri) [118], to develop open-source systems based on SITS analysis methods for crop mapping and monitoring, which can be globally applied. Providing harmonized L8/OLI and S2/MSI ARD, Sen2-Agri aims to address crop monitoring needs and to support national reports for the "Sustainable Development Goal 2-Zero Hunger" (SDG-2) and the GEOGLAM initiative [119].…”

Section: Landsat 8 and Sentinel-2 Data Integrationmentioning

confidence: 99%

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

2020

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Recent applications of Landsat 8 Operational Land Imager (L8/OLI) and Sentinel-2 MultiSpectral Instrument (S2/MSI) data for acquiring information about land use and land cover (LULC) provide a new perspective in remote sensing data analysis. Jointly, these sources permit researchers to improve operational classification and change detection, guiding better reasoning about landscape and intrinsic processes, as deforestation and agricultural expansion. However, the results of their applications have not yet been synthesized in order to provide coherent guidance on the effect of their applications in different classification processes, as well as to identify promising approaches and issues which affect classification performance. In this systematic review, we present trends, potentialities, challenges, actual gaps, and future possibilities for the use of L8/OLI and S2/MSI for LULC mapping and change detection. In particular, we highlight the possibility of using medium-resolution (Landsat-like, 10–30 m) time series and multispectral optical data provided by the harmonization between these sensors and data cube architectures for analysis-ready data that are permeated by publicizations, open data policies, and open science principles. We also reinforce the potential for exploring more spectral bands combinations, especially by using the three Red-edge and the two Near Infrared and Shortwave Infrared bands of S2/MSI, to calculate vegetation indices more sensitive to phenological variations that were less frequently applied for a long time, but have turned on since the S2/MSI mission. Summarizing peer-reviewed papers can guide the scientific community to the use of L8/OLI and S2/MSI data, which enable detailed knowledge on LULC mapping and change detection in different landscapes, especially in agricultural and natural vegetation scenarios.

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Sen2like, A Tool To Generate Sentinel-2 Harmonised Surface Reflectance Products - First Results with Landsat-8

Cited by 14 publications

References 8 publications

A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images

A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images

A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 images

Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

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