Sentinel-2 level 1 products and image processing performances

Baillarin, Simon; Meygret, Aimé; Dechoz, Cécile; Petrucci, Béatrice; Lachérade, Sophie; Trémas, Thierry; Isola, Claudia; Martimort, Philippe; Spoto, François

doi:10.1109/igarss.2012.6351959

Cited by 42 publications

(20 citation statements)

References 3 publications

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“…In both datasets, the coordinate system is WGS84/UTM 33 and, from the radiometric point of view, the pixel values are refl ectance. If the pixel values were not refl ectance, the transformation from Digital Number (DN) to refl ectance would have been applied; it is widely explained in several papers [20,21,22,23,24] and can be summarized in the following steps:…”

Section: Datasetsmentioning

confidence: 99%

Recognition of burned area change of detection analysis using images derived from satellite Sentinel-2: Case studio of Sorrento Penisola, Italy

Pepe¹,

Parente²

2018

J Appl Eng Science

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The purpose of this paper is to identify the burned areas that occurred in Italy during the summer of 2017 using change detection analysis techniques. This task is possible thanks to continuous, free and open availability of the multispectral images obtained by Sentinel-2 satellites. Indeed, comparing the satellite images of the same scene recorded at different times, it was possible to evaluate the landscape change. In this paper, the Direct Comparison change detection technique was applied to the analysis and identifi cation of burned area using several Remote Sensing indexes. In particular, in order to achieve this aim, NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) were used. By case study in South Italy region (Sorrento peninsula), using images derived from Sentinel-2A imagery, it was possible to identify the burned areas in a specifi c period and evaluate the performance of the two indexes. In fact, after having constructed the confusion matrix for the two tested indexes, through the use of methods that indicate the quality of a thematic map (User's Accuracy, Producer's Accuracy, Overall Accuracy and Kappa coeffi cient), the percentage values for each remote sensing index analyzed were compared. The analysis of the different methods revealed, from one side the high quality of the results achievable by NBR index, on the other side, it was shown how, in some areas, the NDVI was inadequate for the recognition of burned areas.

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Section: Datasetsmentioning

confidence: 99%

Recognition of burned area change of detection analysis using images derived from satellite Sentinel-2: Case studio of Sorrento Penisola, Italy

Pepe¹,

Parente²

2018

J Appl Eng Science

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“…Species mapping has proven potential for monitoring invasive or commercially valuable species (Asner et al, 2008;Asner G., R.E, Ford, Metcalfe, & Liddell, 2009;Somers & Asner, 2013). The ESA Sentinel-2 (S2) launch represents a very valuable opportunity for the fine characterization and monitoring of forest types on large scales (Baillarin et al, 2012), and even if this is not an hyperspectral sensor, its innovative features can add value. Sentinel-2 offers a multispectral sensor with 13 bands from 443 to 2190 nm, and a 10 day repeat cycle.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data

Laurin

Puletti

Hawthorne

et al. 2016

Remote Sensing of Environment

157

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a b s t r a c tTo answer new scientific and ecological questions and monitor multiple forest changes, a fine scale characterization of these ecosystems is needed, and could imply the mapping of specific species, of detailed forest types, and of functional composition. This characterization can be now provided by the novel Earth Observation tools. This study aims to contribute to understanding the innovation in forest and ecological research that can be brought in by advanced remote sensing instruments, and proposes the guild mapping approach as a tool to efficiently monitor the varied tropical forest resources. We evaluated, in tropical Ghanaian forests, the ability of airborne hyperspectral and simulated multispectral Sentinel-2 data, and derived vegetation indices and textures, to: distinguish between two different forest types; to discriminate among selected dominant species; and to separate trees species grouped according to their functional guilds: Pioneer, Non Pioneer Light Demanding, and Shade Bearer. We then produced guild classification maps for each area using hyperspectral data. Our results showed that with both hyperspectral and simulated Sentinel-2 data these discrimination tasks can be successfully accomplished. Results also stressed the importance of texture features, especially if using the lower spectral and spatial Sentinel-2 resolution data, and highlighted the important role of the new Sentinel-2 data for ecological monitoring. Classification results showed a statistically significant improvement in overall accuracy using Support Vector Machine, over Maximum Likelihood approach. We proposed the functional guilds mapping as an innovative approach to: (i) monitor compositional changes, especially with respect to the effects of global climate change on forests, and particularly in the tropical biome where the occurrence of hundreds of species prevents mapping activities at species level; (ii) support large-scale forest inventories. The imminent Sentinel-2 data could serve to open the road for the development of new concepts and methods in forestry and ecological research..

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“…Therefore, all those high optical properties configure an attractive sensor for inland water studies, in particular, over high cloud cover regions (e.g., Amazon Basin) due to the high temporal resolution. The standard MSI scene is delivered as Level-1C (L1C) product with radiometric and geometric correction in UTM/WGS84 projection [41].…”

Section: Msi/sentinel-2 Datamentioning

confidence: 99%

Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes

et al. 2017

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Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance (R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency's Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%-96%) and blue (84%-92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes (R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.

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Sentinel-2 level 1 products and image processing performances

Cited by 42 publications

References 3 publications

Recognition of burned area change of detection analysis using images derived from satellite Sentinel-2: Case studio of Sorrento Penisola, Italy

Recognition of burned area change of detection analysis using images derived from satellite Sentinel-2: Case studio of Sorrento Penisola, Italy

Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data

Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes

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