Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services

Drusch, Matthias; Bello, Umberto Del; Carlier, Stefane; Colin, Olivier; Fernandez, Valérie; Gascon, Ferran; Hoersch, Bianca; Isola, Claudia; Laberinti, Paolo; Martimort, Philippe; Meygret, Aimé; Spoto, François; Sy, Omar; Marchese, Franco; Bargellini, P. L.

doi:10.1016/j.rse.2011.11.026

Cited by 2,946 publications

(1,917 citation statements)

References 21 publications

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“…Further, Sentinel 2 will enable computation of commonly used broad band indices such as the NDVI green plus the highly correlated VI's using the red edge bands. Sentinel 2 transcends the capabilities of the Landsat mission in terms of swath width, spatial resolution, revisit time, and number of spectral bands [92]. Information in the red edge combined with the frequent revisit time of Sentinel 2 (5 days) is expected to increase the accuracy of leaf CC estimation.…”

Section: Chl Mapmentioning

confidence: 99%

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

et al. 2015

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There is a need to develop indicators of mangrove condition using remotely sensed data. However, remote estimation of leaf and canopy biochemical properties and vegetation condition remains challenging. In this paper, we (i) tested the performance of selected hyperspectral and broad band indices to predict chlorophyll concentration (CC) on mangrove leaves and (ii) showed the potential of Landsat 8 for estimation of mangrove CC at the landscape level. Relative leaf CC and leaf spectral response were measured at 12 Elementary Sampling Units (ESU) distributed along the northwest coast of the Yucatan Peninsula, Mexico. Linear regression models and coefficients of determination were computed to measure the association between CC and spectral response. At leaf level, the narrow band indices with the largest correlation with CC were Vogelmann indices and the MTCI (R 2 > 0.5). Indices with spectral bands around the red edge (705-753 nm) were more OPEN ACCESSRemote Sens. 2015, 7 14531 sensitive to mangrove leaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (R 2 > 0.8). Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV) method yielded a root mean squared error (RMSE) = 15 μg·cm −2 , and R 2 = 0.703. CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangrove areas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edge such as Sentinel 2 will improve mangrove monitoring at higher spatial and temporal resolutions.

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Section: Chl Mapmentioning

confidence: 99%

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

et al. 2015

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“…However, at more detailed scales, the increased spatial resolution comes along with a less frequent revisit and the multi-temporal approach is thus compromised. Some missions, in particular Sentinel-2, can provide a good opportunity to increase the revisit time through the use of large swaths and twin sensors that double the acquisition frequency [7].…”

Section: Introductionmentioning

confidence: 99%

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Larrañaga

Álvarez‐Mozos

2016

Remote Sensing

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Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45˝), and also to configurations where several polarimetric features (Pauli and Cloude-Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45˝bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational.

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“…With the launch of the Sentinel 1A C band radar in April 2014, the Euro pean Space Agency (ESA) and the European Commission have ini tiated an important series of dual satellite constellations known as Sentinels. The Sentinel 2 mission, planned for launch in 2015 and 2017, will provide medium spatial resolution (10 m to 60 m comparable to Landsat resolution of 30 m at most channels) imagery of global land surfaces and coastal waters every five days (Landsat currently has a revisit time of 16 days) (Berger et al, 2012;Drusch et al, 2012). Together with Landsat, these satellites will provide the potential to observe any area on our planet's sur face with landscape scale data every three to four days.…”

Section: Data Continuitymentioning

confidence: 99%

Free and open-access satellite data are key to biodiversity conservation

Turner¹,

Rondinini

Pettorelli

et al. 2015

Biological Conservation

305

169

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Satellite remote sensing is an important tool for monitoring the status of biodiversity and associated environmental parameters, including certain elements of habitats. However, satellite data are currently underused within the biodiversity research and conservation communities. Three factors have significant impact on the utility of remote sensing data for tracking and understanding biodiversity change. They are its continuity, affordability, and access. Data continuity relates to the maintenance of long term satellite data products. Such products promote knowledge of how biodiversity has changed over time and why. Data affordability arises from the cost of the imagery. New data policies promoting free and open access to government satellite imagery are expanding the use of certain imagery but the number of free and open data sets remains too limited. Data access addresses the ability of conservation biologists and bio diversity researchers to discover, retrieve, manipulate, and extract value from satellite imagery as well as link it with other types of information. Tools are rapidly improving access. Still, more cross community interactions are necessary to strengthen ties between the biodiversity and remote sensing communities.

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Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services

Cited by 2,946 publications

References 21 publications

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Free and open-access satellite data are key to biodiversity conservation

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