https://scholar.google.com/citations?hl=en&user=7QwnQC0AAAAJ&view_op=list_works&authuser=4&gmla=AH70aAXSgsGfbihg4XfTuewCeQeYGy1HTwvT72Ir9iHrnZEDh1XFE7EzcqgkFv5kr1vS-lIMrz6MeOglUi59DhKE

Journal of Remote Sensing and Geoinformation Research

Semiannual Publication

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Staff

Publication Ethics

Related Links

FAQ

Peer Review Process

Journal Metrics

News

Regulations

Sentinel Satellites: Revolutionizing Earth Observation for Science and Sustainability

Document Type : Review Paper

Author

Department of Electrical and Computer Engineering, Malek Ashtar University of Technology, Tehran, Iran

Abstract

Background and Objectives: Earth has a complex ecosystem that is affected by natural processes and human activities, and understanding these processes is a real necessity. Environmental changes, from climate change to the reduction of natural resources, affect human life, economies, and the well-being of future generations. Therefore, the need for comprehensive Earth observation has never been more critical. To address this need, the Copernicus program was created by the European Space Agency in the early 2000s. The goal of this program was to create an operational system for earth monitoring with open access to high quality satellite data. The Sentinel satellites, as the core of the Copernicus program, is one of the symbols of humanity's progress in earth observation and environmental monitoring. This research examines the Sentinel satellites and shows the importance, basic principles and unique features of each of them. Sentinel satellites are unique operational platforms compared to the satellites of the previous generation.
Methods: In this paper, the main framework of the Copernicus program, such as open access to satellite data, global coverage, operational stability, diverse sensors, and the framework of global cooperation, which is of great help to researchers, policymakers, and other users of satellite data, has been evaluated. The Sentinel satellites includes several satellites that acquire images from the earth in different spectral ranges, with global coverage and different viewing times. These satellites have operational stability and new generations of satellites are developed and launched to replace older satellites. Each satellite is equipped with special sensors according to its mission goals and makes it possible to monitor the land, oceans and atmosphere with the best possible tools from space.
Findings: The Sentinel-1 satellite, which is equipped with a radar sensor, can image in the C frequency band and different spatial powers from 5 to 40 meters. Also, this satellite is able to take images in any weather conditions and at any hour of the day and night, which is valuable for many applications. The accuracy of Sentinel-1 data is very high in monitoring changes in the shape of the earth's surface, crisis management, polar ice observations and ocean monitoring. This vital role in detecting and monitoring land subsidence in urban areas makes it effective in urban planning and helping to prevent crises. On the other hand, the Sentinel-2 satellite with a multispectral sensor provides a powerful tool for Earth observation. By recording data in a wide spectral range in 13 spectral bands from visible to short-wave infrared, this satellite has been able to deepen our understanding of the features of the Earth's surface. The re-viewing time of this satellite has made it possible to monitor crops and evaluate the health of vegetation. Also, the spatial resolution of Sentinel-2 satellite images is an effective factor in urban planning, tree health monitoring, and natural disaster monitoring, such as fire and flood. In addition, the high bandwidth of this satellite helps to efficiently cover large areas and increases its efficiency in environmental monitoring.
Conclusion: The Copernicus program is known for several key principles that have underpinned their success. These principles include open access data, global coverage, operational interoperability, diverse sensors, and a global cooperation framework. The principle of open access has given the assurance to the general users, including researchers, policymakers and commercial companies, that a continuous flow of satellite images will support their activities. The Copernicus program with a system of satellites ensures global coverage with optimal revisit time. The operational continuity of the program has resulted in new generations of satellites being developed and launched to replace older generations, to ensure a continuous flow of data. Each Sentinel satellite is equipped with specific sensors designed for its mission objectives, enabling it to monitor the Earth, oceans, atmosphere, and more. According to the characteristics of the Copernicus program, Sentinel satellites have started a new era of Earth observation and have provided a powerful and versatile tool for monitoring and understanding the Earth's ecosystem.

Keywords

Main Subjects

COPYRIGHTS 
© 2023 The Author(s).  This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) 

References
[1] Schulthess U, Rodrigues F, Taymans M, Bellemans N, Bontemps S, Ortiz-Monasterio I, et al. Optimal Sample Size and Composition for Crop Classification with Sen2-Agri’s Random Forest Classifier. Remote Sens (Basel) 2023;15.
https://doi.org/10.3390/RS15030608.
[2] Debella-Gilo M, Gjertsen AK. Mapping seasonal agricultural land use types using deep learning on sentinel-2 image time series. Remote Sens (Basel) 2021;13:1–17.
https://doi.org/10.3390/RS13020289.
[3] Howe AA, Parks SA, Harvey BJ, Saberi SJ, Lutz JA, Yocom LL. Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America. Remote Sens (Basel) 2022;14.
https://doi.org/10.3390/RS14205249.
[4] Haack BN. Landsat: A tool for development. World Dev 1982;10:899–909.
https://doi.org/10.1016/0305-750X(82)90064-X.
[5] Uribeetxebarria A, Castellón A, Aizpurua A. A First Approach to Determine if It Is Possible to Delineate In-Season N Fertilization Maps for Wheat Using NDVI Derived from Sentinel-2. Remote Sens (Basel) 2022;14.
https://doi.org/10.3390/RS14122872.
[6]           Azzari G, Jain S, Jeffries G, Kilic T, Murray S. Understanding the requirements for surveys to support satellite-based crop type mapping: Evidence from sub-saharan Africa. Remote Sens (Basel) 2021;13.
https://doi.org/10.3390/RS13234749.
[7] Ibrahim ES, Rufin P, Nill L, Kamali B, Nendel C, Hostert P. Mapping crop types and cropping systems in nigeria with sentinel-2 imagery. Remote Sens (Basel) 2021;13.
https://doi.org/10.3390/RS13173523.
[8] Kganyago M, Mhangara P, Adjorlolo C. Estimating crop biophysical parameters using machine learning algorithms and sentinel-2 imagery. Remote Sens (Basel) 2021;13.
https://doi.org/10.3390/RS13214314.
[9] Reinermann S, Gessner U, Asam S, Ullmann T, Schucknecht A, Kuenzer C. Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series. Remote Sens (Basel) 2022;14.
https://doi.org/10.3390/RS14071647.
[10] Gašparović M, Jogun T. The effect of fusing Sentinel-2 bands on land-cover classification. Int J Remote Sens 2018;39:822–41.
https://doi.org/10.1080/01431161.2017.1392640.
[11] Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 2007;28:823–70.
https://doi.org/10.1080/01431160600746456.
[12] Dronova I. Object-based image analysis in wetland research: A review. Remote Sens (Basel) 2015;7:6380–413.
https://doi.org/10.3390/RS70506380.
[13] Nomura K, Mitchard ETA. More than meets the eye: Using Sentinel-2 to map small plantations in complex forest landscapes. Remote Sens (Basel) 2018;10.
https://doi.org/10.3390/RS10111693.
[14] Verrelst J, Muñoz J, Alonso L, Delegido J, Rivera JP, Camps-Valls G, et al. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3. Remote Sens Environ 2012;118:127–39.
https://doi.org/10.1016/J.RSE.2011.11.002.
[15] Clevers JGPW, Gitelson AA. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on sentinel-2 and-3. International Journal of Applied Earth Observation and Geoinformation 2013;23:344–51.
https://doi.org/10.1016/J.JAG.2012.10.008.
[16] Claverie M, Ju J, Masek JG, Dungan JL, Vermote EF, Roger JC, et al. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens Environ 2018;219:145–61.
https://doi.org/10.1016/J.RSE.2018.09.002.
[17] Chastain R, Housman I, Goldstein J, Finco M. Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States. Remote Sens Environ 2019;221:274–85.
https://doi.org/10.1016/J.RSE.2018.11.012.
[18] Sloan S, Sayer JA. Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries. For Ecol Manage 2015;352:134–45.
https://doi.org/10.1016/J.FORECO.2015.06.013
[19] Sitokonstantinou V, Papoutsis I, Kontoes C, Arnal AL, Andrés APA, Zurbano JAG. Scalable parcel-based crop identification scheme using Sentinel-2 data time-series for the monitoring of the common agricultural policy. Remote Sens (Basel) 2018;10.
https://doi.org/10.3390/RS10060911.
[20] Malenovský Z, Rott H, Cihlar J, Schaepman ME, García-Santos G, Fernandes R, et al. Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sens Environ 2012;120:91–101.
https://doi.org/10.1016/J.RSE.2011.09.026.
[21] Jucker T, Caspersen J, Chave J, Antin C, Barbier N, Bongers F, et al. Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob Chang Biol 2017;23:177–90.
https://doi.org/10.1111/GCB.13388.
[22] Hansen MC, Sohlberg R, Defries RS, Townshend JRG. Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 2000;21:1331–64.
https://doi.org/10.1080/014311600210209.
[23] Chowdhury M, Hasan ME, Abdullah-Al-Mamun MM. Land use/land cover change assessment of Halda watershed using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Science 2020;23:63–75.
https://doi.org/10.1016/J.EJRS.2018.11.003.
[24] Baetens L, Desjardins C, Hagolle O. Validation of copernicus Sentinel-2 cloud masks obtained from MAJA, Sen2Cor, and FMask processors using reference cloud masks generated with a supervised active learning procedure. Remote Sens (Basel) 2019;11.
https://doi.org/10.3390/RS11040433.
[25] Phiri D, Simwanda M, Salekin S, Nyirenda VR, Murayama Y, Ranagalage M. Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing 2020, Vol 12, Page 2291 2020;12:2291.
https://doi.org/10.3390/RS12142291.
[26] Turner W, Rondinini C, Pettorelli N, Mora B, Leidner AK, Szantoi Z, et al. Free and open-access satellite data are key to biodiversity conservation. Biol Conserv 2015;182:173–6.
https://doi.org/10.1016/J.BIOCON.2014.11.048
[27] Konda VGRK, Chejarla VR, Mandla VR, Voleti V, Chokkavarapu N. Vegetation damage assessment due to Hudhud cyclone based on NDVI using Landsat-8 satellite imagery. Arabian Journal of Geosciences 2018;11:35.
https://doi.org/10.1007/s12517-017-3371-8.
[28] Wu M, Yang C, Song X, Hoffmann WC, Huang W, Niu Z, et al. Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion. Sci Rep 2018;8.
https://doi.org/10.1038/S41598-018-20156-Z.
[29] Goor E, Dries J, Daems D, Paepen M, Niro F, Goryl P, et al. PROBA-V mission exploitation platform. Remote Sens (Basel) 2016;8.
https://doi.org/10.3390/RS8070564.
[30] Hansen MC, Potapov P V., Moore R, Hancher M, Turubanova SA, Tyukavina A, et al. High-resolution global maps of 21st-century forest cover change. Science (1979) 2013;342:850–3.
https://doi.org/10.1126/SCIENCE.1244693.
[31] Xu Y, Yu L, Feng D, Peng D, Li C, Huang X, et al. Comparisons of three recent moderate resolution African land cover datasets: CGLS-LC100, ESA-S2-LC20, and FROM-GLC-Africa30. Int J Remote Sens 2019;40:6185–202.
https://doi.org/10.1080/01431161.2019.1587207.
[32] Lark TJ, Mueller RM, Johnson DM, Gibbs HK. Measuring land-use and land-cover change using the U.S. department of agriculture’s cropland data layer: Cautions and recommendations. International Journal of Applied Earth Observation and Geoinformation 2017;62:224–35.
https://doi.org/10.1016/j.jag.2017.06.007.
[33] Otunga C, Odindi J, Mutanga O, Adjorlolo C. Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data. Geocarto Int 2019;34:1123–43.
https://doi.org/10.1080/10106049.2018.1474274.
[34] Padró JC, Pons X, Aragonés D, Díaz-Delgado R, García D, Bustamante J, et al. Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy. Remote Sensing 2017;9:1319.
https://doi.org/10.3390/RS9121319.
Journal of Remote Sensing and Geoinformation Research
Volume 1, Issue 2 - Serial Number 2
July 2023
Pages 205-215
Files
History
  • Receive Date: 09 July 2023
  • Revise Date: 06 September 2023
  • Accept Date: 01 October 2023
Share
How to cite
Statistics