Next Article in Journal
Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data
Next Article in Special Issue
Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions
Previous Article in Journal
A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS) Soil Moisture: Retrieval Ensembles
Previous Article in Special Issue
A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements
Article Menu

Export Article

Open AccessTechnical Note
Remote Sens. 2015, 7(12), 16062-16090; doi:10.3390/rs71215815

Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2

1
Earth and Life Institute, Université Catholique de Louvain, 2 Croix du Sud bte L7.05.16, 1348 Louvain-la-Neuve, Belgium
2
Centre d’Etudes Spatiales de la BIOsphère CESBIO, Université de Toulouse, CNES/CNRS/IRD/UPS, 18 Avenue Edouard Belin, 31401 Toulouse, France
3
CS Romania S.A., 29 Strada Pacii, 200692 Craiova, Romania
4
CS Systèmes d’Information, 5 rue Brindejonc des Moulinais, 31506 Toulouse, France
5
National Agriculture Information Center Directorate, Space Applications Research Complex, Pakistan Space and Upper Atmosphere Research Commission, 44000 Islamabad, Pakistan
6
Maison de la télédétection (CIRAD-UMR TETIS), 500 rue J.-F. Breton, 34093 Montpellier, France
7
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, West Beichen Road, Chaoyang, Beijing 100101, China
8
Instituto de Clima y Agua, Instituto Nacional de Tecnología Agropecuaria (INTA), Repetto y de Los Reseros s/n, 1686 Hurlingham, Argentina
9
Faculté des Sciences Semlalia, Université Cadi Ayyad, BP 2390, 40000 Marrakech, Morocco
10
Laboratoire Mixte International TREMA, Centre Geber, Faculté des Sciences de Semlalia, 40000 Marrakech, Morocco
11
U.S. Arid Land Agricultural Research Center, ARS-USDA, Maricopa, AZ 85138, USA
12
Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, 40 prosp. Glushkov, build.4/1, 03680 Kyiv, Ukraine
13
Agricultural Research Council (South Africa), Private Bag X79, 0001 Pretoria, South Africa
14
V.V. Dokuchaev Soil Science Institute, PFUR, 119017 Moscow, Russia
15
European Space Research Institute, European Space Agency, Via Galileo Galilei, Casella Postale 64, 00044 Frascati (Rome), Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Olivier Arino, Sylvia Sylvander, Clement Atzberger and Prasad S. Thenkabail
Received: 31 May 2015 / Revised: 10 November 2015 / Accepted: 16 November 2015 / Published: 2 December 2015
View Full-Text   |   Download PDF [4225 KB, uploaded 14 December 2015]   |  

Abstract

Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10–20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the “Sentinel­2 for Agriculture” project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of “Sentinel-2 like” time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the “Joint Experiment for Crop Assessment and Monitoring” network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future “Sentinel­2 for Agriculture” system. View Full-Text
Keywords: agriculture monitoring; satellite time series; in situ data; Sentinel-2; SPOT4 (Take 5); Landsat 8; JECAM; GEOGLAM agriculture monitoring; satellite time series; in situ data; Sentinel-2; SPOT4 (Take 5); Landsat 8; JECAM; GEOGLAM
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Bontemps, S.; Arias, M.; Cara, C.; Dedieu, G.; Guzzonato, E.; Hagolle, O.; Inglada, J.; Matton, N.; Morin, D.; Popescu, R.; Rabaute, T.; Savinaud, M.; Sepulcre, G.; Valero, S.; Ahmad, I.; Bégué, A.; Wu, B.; de Abelleyra, D.; Diarra, A.; Dupuy, S.; French, A.; ul Hassan Akhtar, I.; Kussul, N.; Lebourgeois, V.; Le Page, M.; Newby, T.; Savin, I.; Verón, S.R.; Koetz, B.; Defourny, P. Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2. Remote Sens. 2015, 7, 16062-16090.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top