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Topical Collection "Sentinel-2: Science and Applications"

Editors

Collection Editor
Prof. Dr. Clement Atzberger

University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
Website | E-Mail
Phone: +43 (1) 47654 5101
Interests: time series analysis; vegetation monitoring and dynamics; land surface phenology; drought early warning systems; EO for agriculture, forestry and natural resource management; imaging spectroscopy; radiative transfer modeling; machine learning; neural nets; vegetation biophysical variables
Collection Editor
Dr. Jadu Dash

Professor in Remote Sensing, Grography and Environment, University of Southampton, Avenue Campus, Highfield Rd, Southampton SO17 1BJ, UK
Website | E-Mail
Interests: land surface phenology; Earth observation; biophysical variables; agriculture
Collection Editor
Mr. Olivier Hagolle

CESBIO/CNES, 18 avenue E. Belin, 31401 Toulouse Cedex 9, France
Website | E-Mail
Interests: calibration; atmospheric corrections; time series
Collection Editor
Dr. Jochem Verrelst

Image Processing Laboratory (IPL), University of Valencia, Valencia
Website | E-Mail
Phone: (+34) 963544067
Interests: vegetation properties mapping; imaging spectroscopy; radiative transfer model inversion
Collection Editor
Mr. Quinten Vanhellemont

Royal Belgian Institute of Natural Sciences, Brussels, Belgium
Website | E-Mail
Interests: atmospheric correction; satellite image processing
Collection Editor
Dr. Jordi Inglada

CESBIO/CNES, BPI 811, 18 Avenue E. Berlin, 31401 Toulouse Cedex 9, France
Website | E-Mail
Interests: land cover mapping; satellite image time series; image classification
Collection Editor
Prof. Dr. Tuomas Häme

VTT Technical Research Centre of Finland, Espoo, Finland
Website | E-Mail
Interests: forest biomass and carbon; forest management support with remote sensing; forest inventory and statistical techniques; change detection; automatic and adaptive image analysis systems

Topical Collection Information

Dear Colleagues,

With two identical (twin) satellites in orbit, Sentinel-2 (A and B) now provide unprecedented coverage of the land Earth surface at 5 days revisit time, in several carefully chosen spectral channels, including channels designed to facilitate the atmospheric/radiometric correction of the data streams. In this Collection, we wish to collate papers dealing with this sensor, its application and related science. Also highly welcome are those papers combining Sentinel-2 with other satellite data. Typical applications and problems studied with Sentinel-2 include the following (non-exhaustive) list:

  • Land cover/land use classification
  • Change detection
  • Precision agriculture and monitoring of agricultural land
  • Forestry and natural resources
  • Land surface phenology and monitoring of phenology stages
  • Contribution to Essential Climate Variables (ECV) monitoring
  • Coastal zones and inland water
  • Data assimilation in dynamic process models
  • Data fusion
  • Cloud detection and atmospheric correction
  • Cal/Val activities

Prof. Dr. Clement Atzberger
Prof. Dr. Jadu Dash
Mr. Hagolle Olivier
Dr. Jochem Verrelst
Dr. Quinten Vanhellemont
Dr. Jordi Inglada
Prof. Dr. Tuomas Häme
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sentinel-2
  • Time series
  • Land use and land cover mapping
  • Retrieval of Earth surface variables
  • Land surface phenology
  • Data fusion

Related Special Issue

Published Papers (4 papers)

2018

Open AccessArticle Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species
Remote Sens. 2018, 10(9), 1468; https://doi.org/10.3390/rs10091468
Received: 16 July 2018 / Revised: 9 September 2018 / Accepted: 10 September 2018 / Published: 14 September 2018
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Abstract
Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2)
[...] Read more.
Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species. Full article
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Graphical abstract

Open AccessEditor’s ChoiceTechnical Note Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
Remote Sens. 2018, 10(6), 926; https://doi.org/10.3390/rs10060926
Received: 17 April 2018 / Accepted: 6 June 2018 / Published: 12 June 2018
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Abstract
Sentinel-2 is a constellation of two satellites launched by the European Space Agency (ESA), respectively on 23 June 2015 and 7 March 2017, to map geophysical parameters over land surfaces. ESA provides Level 2 bottom-of-atmosphere reflectance (BOA) products (ESA-L2A) for Europe, with plans
[...] Read more.
Sentinel-2 is a constellation of two satellites launched by the European Space Agency (ESA), respectively on 23 June 2015 and 7 March 2017, to map geophysical parameters over land surfaces. ESA provides Level 2 bottom-of-atmosphere reflectance (BOA) products (ESA-L2A) for Europe, with plans for operational global coverage, as well as the Sen2Cor (S2C) offline processor. In this study, aerosol optical thickness (AOT), precipitable water vapour (WVP) and surface reflectance from ESA-L2A products are compared with S2C output when using identical input Level 1 radiance products. Additionally, AOT and WVP are validated against reference measurement. As ESA and S2C share the same input and atmospheric correction algorithm, it was hypothesized that they should show identical validation performance and that differences between products should be negligible in comparison to the uncertainty of retrieved geophysical parameters due to radiometric uncertainty alone. Validation and intercomparison was performed for five clear-sky growing season dates for each of three ESA-L2A tiles selected to span a range of vegetation and topography as well as to be close to the AERONET measurement site. Validation of S2C (ESA) products using AERONET site measurements indicated an overall root mean square error (RMSE) of 0.06 (0.07) and a bias of 0.05 (0.09) for AOT and 0.20 cm (0.22 cm) and the bias was −0.02 cm (−0.10 cm) for WVP. Intercomparison of S2C-L2A and ESA-L2A showed an overall agreement higher than 99% for scene classification (SCL) maps and negligible differences for WVP (RMSE under 0.09 and R2 above 0.99). Larger disagreement was observed for aerosol optical thickness (AOT) (RMSE up to 0.04 and R2 as low as 0.14). For BOA reflectance, disagreement between products depends on vegetation cover density, topography slope and spectral band. The largest differences were observed for red-edge and infrared bands in mountainous vegetated areas (RMSE up to 4.9% reflectance and R2 as low as 0.53). These differences are of similar magnitude to the radiometric calibration requirements for the Sentinel 2 imager. The differences had minimal impact of commonly used vegetation indices (NDVI, NDWI, EVI), but application of the Sentinel Level 2 biophysical processor generally resulted in proportional differences in most derived vegetation parameters. It is recommended that the consistency of ESA and S2C products should be improved by the developers of the ESA and S2C processors. Full article
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Figure 1

Open AccessArticle Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China
Remote Sens. 2018, 10(4), 638; https://doi.org/10.3390/rs10040638
Received: 4 April 2018 / Revised: 18 April 2018 / Accepted: 18 April 2018 / Published: 20 April 2018
Cited by 2 | PDF Full-text (12316 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A
[...] Read more.
As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A imager was utilized to assess its ability to perform lithological classification in the Shibanjing ophiolite complex in Inner Mongolia, China. Five conventional machine learning methods, including artificial neural network (ANN), k-nearest neighbor (k-NN), maximum likelihood classification (MLC), random forest classifier (RFC), and support vector machine (SVM), were compared in order to find an optimal classifier for lithological mapping. The experiment revealed that the MLC method offered the highest overall accuracy. After that, Sentinel-2A image was compared with common multispectral data ASTER and Landsat-8 OLI (operational land imager) for lithological mapping using the MLC method. The comparison results showed that the Sentinel-2A imagery yielded a classification accuracy of 74.5%, which was 2.5% and 5.08% higher than those of the ASTER and OLI imagery, respectively, indicating that Sentinel-2A imagery is adequate for lithological discrimination, due to its high spectral resolution in the VNIR to SWIR range. Moreover, different data combinations of Sentinel-2A + ASTER + DEM (digital elevation model) and OLI + ASTER + DEM data were tested on lithological mapping using the MLC method. The best mapping result was obtained from Sentinel-2A + ASTER + DEM dataset, demonstrating that OLI can be replaced by Sentinel-2A, which, when combined with ASTER, can achieve sufficient bandpasses for lithological classification. Full article
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Figure 1

Open AccessArticle Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model
Remote Sens. 2018, 10(2), 269; https://doi.org/10.3390/rs10020269
Received: 7 December 2017 / Revised: 26 January 2018 / Accepted: 5 February 2018 / Published: 9 February 2018
Cited by 1 | PDF Full-text (2857 KB) | HTML Full-text | XML Full-text
Abstract
Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as
[...] Read more.
Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as input for rangeland carrying capacity and stocking rate models. The estimation of leaf N has been successful using hyperspectral and commercial high spatial resolution satellite data such as WorldView-2 and RapidEye. Empirical methods have been used successfully to estimate leaf N, on the basis that it correlates with leaf chlorophyll. As such, leaf N was estimated using red edge based indices. The new Sentinel-2 sensor has two red edge bands, is freely available, and could further improve the estimation of leaf N at a regional scale. The objective of this study is to develop red edge based Sentinel-2 models derived from an analytical spectral device (ASD) spectrometer to map and monitor leaf N using Sentinel-2 images. Field work for leaf N and ASD data were collected in 2014 (December) in and around Kruger National Park, South Africa. ASD data were resampled to the Sentinel-2 spectral configuration using the spectral response function. The Sentinel-2 data for various dates were acquired from the European Space Agency (ESA) portal. The Sentinel-2 atmospheric correction (Sen2Cor) process was implemented. Simple empirical regression was used to estimate leaf N. High leaf N prediction accuracy was achieved at the ASD level and the best model was inverted on Sentinel-2 images to explain leaf N distribution at a regional scale over time. The spatial distribution of leaf N is influenced by the underlying geological substrate, fire frequency and other environmental variables. This study is a demonstration of how ASD data can be used to calibrate Sentinel-2 for leaf N estimation and mapping. Full article
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Graphical abstract

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