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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), A-1190 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
Prof. Jadu Dash

Professor in Remote Sensing, Geography and Environment, University of Southampton, UK
Website | E-Mail
Interests: land surface phenology; Earth Observation; biophysical variable; 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: remote sensing applications; environment

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 (2 papers)

2018

Open AccessArticle Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China
Remote Sens. 2018, 10(4), 638; doi:10.3390/rs10040638
Received: 4 April 2018 / Revised: 18 April 2018 / Accepted: 18 April 2018 / Published: 20 April 2018
PDF Full-text (12213 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
Figures

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; doi:10.3390/rs10020269
Received: 7 December 2017 / Revised: 26 January 2018 / Accepted: 5 February 2018 / Published: 9 February 2018
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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