Sentinel-2 Global Surface Reflectance Level-2a Product Generated with Sen2Cor

Louis, Jérôme; Pflug, Bringfried; Main-Knorn, Magdalena; Debaecker, Vincent; Müeller-Wilm, Uwe; Iannone, Rosario; Cadau, Enrico; Boccia, Valentina; Gascon, Ferran

doi:10.1109/igarss.2019.8898540

Cited by 83 publications

(80 citation statements)

References 7 publications

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“…Our primary data source used was Sentinel-2 A/B satellite imagery, accessed from the European Space Agency (https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi, (accessed on 23 November 2020)) via the GEE platform [32]. Among the various Sentinel-2 products, we selected the category of Level 2A because it meets the formal quality of orthorectified Bottom-of-Atmosphere (BOA) reflectance, and thus multi-temporal images can be directly used to construct time series for further analysis [33]. Within the Sentinel-2 Level 2A data, we used mainly the Scene Classification Layer (termed SCL hereafter) band for lake ice-off phenology mapping at a 20 m resolution.…”

Section: Datasetsmentioning

confidence: 99%

Fine-Resolution Mapping of Pan-Arctic Lake Ice-Off Phenology Based on Dense Sentinel-2 Time Series Data

et al. 2021

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The timing of lake ice-off regulates biotic and abiotic processes in Arctic ecosystems. Due to the coarse spatial and temporal resolution of available satellite data, previous studies mainly focused on lake-scale investigations of melting/freezing, hindering the detection of subtle patterns within heterogeneous landscapes. To fill this knowledge gap, we developed a new approach for fine-resolution mapping of Pan-Arctic lake ice-off phenology. Using the Scene Classification Layer data derived from dense Sentinel-2 time series images, we estimated the pixel-by-pixel ice break-up end date information by seeking the transition time point when the pixel is completely free of ice. Applying this approach on the Google Earth Engine platform, we mapped the spatial distribution of the break-up end date for 45,532 lakes across the entire Arctic (except for Greenland) for the year 2019. The evaluation results suggested that our estimations matched well with both in situ measurements and an existing lake ice phenology product. Based on the generated map, we estimated that the average break-up end time of Pan-Arctic lakes is 172 ± 13.4 (measured in day of year) for the year 2019. The mapped lake ice-off phenology exhibits a latitudinal gradient, with a linear slope of 1.02 days per degree from 55°N onward. We also demonstrated the importance of lake and landscape characteristics in affecting spring lake ice melting. The proposed approach offers new possibilities for monitoring the seasonal Arctic lake ice freeze–thaw cycle, benefiting the ongoing efforts of combating and adapting to climate change.

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Section: Datasetsmentioning

confidence: 99%

Fine-Resolution Mapping of Pan-Arctic Lake Ice-Off Phenology Based on Dense Sentinel-2 Time Series Data

et al. 2021

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“…Sen2Cor is the Level 2 processor provided by ESA to correct Level 1 from the atmospheric signal [12,13]. The outputs of the Sen2Cor processor are surface reflectances, aerosol optical thicknesses at 550 nm (AOT550) and water vapor (WV) concentrations.…”

Section: Satellite Level 2 Products Using Sen2cor 209mentioning

confidence: 99%

BRDF Estimations and Normalizations of Sentinel 2 Level 2 Data Using a Kalman-Filtering Approach and Comparisons with RadCalNet Measurements

Saulquin

2021

Remote Sensing

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BRDF estimation aims to characterize the anisotropic behaviour of the observed surface, which is directly related to the type of surface. BRDF theoretical models are then used in the normalization of the satellite-derived observations to virtually replace the sensor at the nadir. Such normalization reinforces the homogeneity within and between satellite-derived time series. Nevertheless, the inversion of the necessary BRDF parameters for the normalization requires the implementation of robust methods to account for the noise in the Level 2 surface reflectances caused by the atmospheric correction process. Here, we compare normalized reflectances obtained with a Kalman filtering approach with i/the classical weighted linear inversion and ii/a normalization performed using the coefficients of the NASA-MODIS BRDF MCD43A1 band 2 product. We show, using the RadCalNet nadir-view reflectances, that the Kalman filtering approach is a more suitable approach for the Sen2Cor level 2 and the selected sites. Using the proposed approach, daily global maps of land surface BRDF coefficients and the derived normalized Sentinel 2 reflectances would be extremely useful to the global and regional climate modelling communities and for the world’s cover monitoring.

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“…A recent review of atmospheric correction approaches for imaging spectrometers can be found in [4], which includes the so-called quick atmospheric correction (QUAC) [5], the TAFKAA [6] and the ACORN [7] approaches, the HATCH model [8], as well as ATCOR [9]. For multispectral sensors, operational approaches include approaches for MODIS [10] as well as the several developed for Sentinel-2 [11][12][13][14]. The atmospheric correction is particularly important when comparing remote sensing data (surface reflectance) from different sensors in-orbit under realistic atmospheric conditions [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

PACO: Python-Based Atmospheric Correction

Reyes

Langheinrich

Schwind

et al. 2020

Sensors

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The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of Sentinel-2, Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range.

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Sentinel-2 Global Surface Reflectance Level-2a Product Generated with Sen2Cor

Cited by 83 publications

References 7 publications

Fine-Resolution Mapping of Pan-Arctic Lake Ice-Off Phenology Based on Dense Sentinel-2 Time Series Data

Fine-Resolution Mapping of Pan-Arctic Lake Ice-Off Phenology Based on Dense Sentinel-2 Time Series Data

BRDF Estimations and Normalizations of Sentinel 2 Level 2 Data Using a Kalman-Filtering Approach and Comparisons with RadCalNet Measurements

PACO: Python-Based Atmospheric Correction

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