The CMEMS High Resolution Coastal Service

2021 ◽  
Author(s):  
Dimitry Van der Zande ◽  
Kerstin Stelzer ◽  
Martin Böttcher ◽  
João Felipe Cardoso dos Santos ◽  
Carole Lebreton ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Landsat 8 ◽  
Backscatter Coefficient ◽  
Ocean Colour ◽  
New Production ◽  
In Situ Data ◽  
Full Picture ◽  
Spm Concentration ◽  
Sentinel 2

<p>High-quality satellite-based ocean colour products can provide valuable support and insights in management and monitoring of coastal ecosystems. Today’s availability of Earth Observation (EO) data is unprecedented including traditional medium resolution ocean colour systems (e.g. SeaWiFS, MODIS-AQUA, MERIS, Sentinel-3/OLCI), high resolution land sensors (e.g. Sentinel-2/MSI, Landsat-8/OLI, Pleiades) and geostationary satellites (e.g. SEVIRI). Each of these sensors offers specific advantages in terms of spatial, temporal or radiometric characteristics.</p><p>As a new production unit, the high resolution coastal service will be integrated in CMEMS. It offers 12 different products which are covered within the Ocean Colour Thematic Assembly Centre (OCTAC). The products can be categorized in two groups: 1) near real time (NRT) and Multi-Year near real time (MYNRT). The products are generated the coastal waters (20km stripe for the coastline) for all European Seas and are provided in 100m spatial resolution. All products are based on Sentinel-2 MSI data. The primary OCTAC variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral Remote Sensing Reflectance (RRS). This, together with the Particulate Backscatter Coefficient (BBP), constitute the category of the optics products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm. The transparency products include turbidity (TUR) and Suspended Particulate Matter (SPM) concentration. They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions. The geophysical product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging. The NRT products are generally provided withing 24 hours after end of the acquisition day, while monthly averaged products are provided few days after end of the respective month. A third group of products are daily gap-filled products which are provided once in a quarter. Validation of the variables has been performed by match-up analysis with in situ data as well as by comparison of the high resolution products with the well established Low Resolution CMEMS Ocean Colour products. The products will be introduced in the CMEMS service by May 2021. We will present the products themselves as well as the validation results for the different variables. The known limitations will be reported in order to provide a full picture of the new service.</p>

scholarly journals Spatial Variability Assessment on The High-resolution Chlorophyll-a Extraction from Landsat 8and Sentinel 2 Imageries in Johor Waters

2020 ◽  
Vol 2 ◽  
pp. 38-43
Author(s):  
Fatin Nabihah Syahira Ridzuan ◽  
Mohd Nadzri Md Reba ◽  
Monaliza Mohd Din ◽  
Mazlan Hashim ◽  
Po Teen Lim ◽  
...  
Keyword(s):  
High Resolution ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
Coastal Region ◽  
Water Quality Monitoring ◽  
Landsat 8 ◽  
Statistical Regression ◽  
Chl A ◽  
In Situ Data ◽  
Sentinel 2

High resolution Chlorophyll-a (Chl-a) can indicate the trophic status of the water and provide useful information on optical features of water body in water quality monitoring. Remote sensing has great potential to offer the spatial and temporal coverage needed. Over the last decades the Sea WIFS and MODIS were applied, but not suitable due to the low spatial resolution for monitoring Chl-a in coastal area. However, the retrieval of Chl-a in the coastal region is usually challenging due to the other in-water substances regardless of Chl-a, hence resulting in the satellite retrieved Chl-a overestimation. By the advancement of the Sentinel-2 and Landsat 8 satellites, continuous high resolution optical imageries have served for remarkable coastal mapping capability thanks to the spectroscopic capability certain spectral bands and as high as 10-meter spatial resolution. This paper aims to evaluate the performance of the SEADASS and SNAP processor for Chl-a estimation from the Operational Land Imager (OLI)and MultiSpectral Instrument(MSI) data in Johor waters. The representative models, in standard algorithm OC3and C2RCC, were adapted to retrieve Chl-a concentration. The statistical regression has shown that these algorithms give an acceptable Chl-a estimation at medium and high resolution with R2=0.44 from OC3and R2=0.55from C2RCC comparing to the in-situ data. Despite of the spatial, temporal and spectral variability, this paper shows that OLI and MSI could provide Chl-a mapping capability at suitable Chl-a estimation techniques.

High-resolution sea surface salinity and temperature in coastal areas from Sentinel-2 and Copernicus Marine in situ data

2020 ◽  
Author(s):  
Encarni Medina-Lopez
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
In Situ Data ◽  
The Neural Network ◽  
Sentinel 2

<p>The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m x 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.</p>

An empirical cross sensor model for high-resolution multispectral imagery

2020 ◽  
Author(s):  
Manivasagam Vellalapalayam Subramanian ◽  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Transformation Model ◽  
Sensor Data ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Spectral Angle Mapper ◽  
Statistical Measures ◽  
Sentinel 2

<p>The availability of public-domain high-resolution satellite imagery such as Sentinel-2 and Landsat-8 has increased earth observation (EO) studies across the globe. Empirically combining different EO sensor data into a single dataset increases the temporal coverage, which is useful for land-cover monitoring. In this study, a transformation model was developed for Sentinel-2 and Vegetation and Environmental New micro Spacecraft (VENμS) imagery over Israel. Both sensors offer high spatio-temporal resolution imagery, i.e., VENμS has a 10m spatial resolution with a two-day revisit period, and Sentinel-2 has a 10-20 m spatial resolution with a five-day revisit period. Near-simultaneously acquired imagery was employed for the transformation model development. The model coefficients were derived for the overlapping spectral regions of both sensors. Further, the transformation model performance was tested using various statistical measures, namely, orthogonal distance regression (ODR), spectral angle mapper (SAM), and mean absolute difference (MAD). The validation results highlighted that MAD values were reduced between Sentinel-2 and transformed VENμS reflectance. Similarly, the ODR slope values became closer to one, and the overall spectral similarity increased as demonstrated by a decrease in SAM values. This transformation function creates a unified reflectance dataset in the form of a dense time-series of observation, especially useful for vegetation monitoring.</p>

scholarly journals Estimación de áreas quemadas de los incendios en la provincia de Córdoba durante agosto de 2020 utilizando imágenes multiespectrales

AJEA ◽  
2020 ◽  
Author(s):  
Facundo Ismael Casasola ◽  
Lidia Ana Otero
Keyword(s):  
Real Time ◽  
Landsat 8 ◽  
Sentinel 2

Durante el mes de agosto de 2020, se presentaron en la provincia de Córdoba numerosos focos de incendios que derivaron en la pérdida de más de 80 mil hectáreas de bosques nativos. El presente trabajo tiene la finalidad de describir cómo el empleo de la información espectral de sensores remotos satelitales NOAA, MODIS, VIIRS, Landsat 8 y Sentinel 2 y 3 ofrecen la posibilidad de brindar recursos técnicos para el seguimiento “quasi real time” de la evolución e impacto en este tipo de emergencias.Los incendios forestales son una de las mayores causas de problemas ambientales y ecológicos en muchas partes del mundo. En la Argentina anualmente provocan la destrucción de miles de hectáreas de bosques nativos y otros tipos de vegetación. También consumen valiosos recursos económicos, generan un inmenso daño económico y alteraciones significativas de los ecosistemas, siendo una de las principales causas de la transformación de la cubierta terrestre.

scholarly journals High Resolution Sentinel-2 Images for Improved Bathymetric Mapping of Coastal and Lake Environments

2019 ◽  
Author(s):  
Ali P. Yunus ◽  
Dou Jie ◽  
Xuan Song ◽  
Ram Avtar
Keyword(s):  
High Resolution ◽  
Landsat 8 ◽  
Mobile Bay ◽  
Landsat 8 Oli ◽  
Transport Pathways ◽  
Satellite Technology ◽  
Bathymetric Data ◽  
Crucial Component ◽  
Bathymetric Mapping ◽  
Sentinel 2

Bathymetry of nearshore coastal environments and lakes are constantly reworking because of the change in the patterns of energy dispersal and related sediment transport pathways. Therefore, updated and accurate bathymetric models are a crucial component in providing basic information for scientific, managerial, and geographical studies. Recent advances in satellite technology have revolutionized the acquisition of bathymetric profiles, offering new vistas in mapping. This contribution analysed the suitability of high resolution Sentinel-2 images for bathymetric mapping of coastal and lake environments. The bathymetric algorithm for satellite imageries was developed based on the available high resolution bathymetric data for Mobile Bay, Tampa Bay and Lake Huron regions obtained from National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center (NGDC). The results demonstrate that the satellite derived bathymetry is efficient for retrieving depths up to 10 m for coastal regions and up to 30 m for lake environment. The root mean square error (RMSE) varies between 1.99 m and 2.80 m for the three regions. A comparison of Sentinel-2 derived bathymetry is also carried with the Landsat 8 OLI derived bathymetry. The results suggest Sentinel-2 images are capable of producing much accurate bathymetric maps than those from the Landsat 8 OLI images. Our work demonstrated that the freely available Sentinel-2 imagery proved to be a reliable method for acquiring updated high resolution bathymetric information for large areas in short span of time.

scholarly journals Theia Snow collection: high resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

2018 ◽  
Author(s):  
Simon Gascoin ◽  
Manuel Grizonnet ◽  
Marine Bouchet ◽  
Germain Salgues ◽  
Olivier Hagolle
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western Europe ◽  
Landsat 8 ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Level 2 ◽  
Sentinel 2

Abstract. The Theia Snow collection routinely provides high resolution maps of the snow cover area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide including the main mountain regions in Western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Snow collection contains four classes: snow, no-snow, cloud and no-data. We present the algorithm to generate the snow products and provide an evaluation of their accuracy using in situ snow depth measurements, higher resolution snow maps, and visual control. The results suggest that the snow is accurately detected in the Theia snow collection, and that the snow detection is more accurate than the sen2cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed in average 5 days after the image acquisition as raster and vector files via the Theia portal (http://doi.org/10.24400/329360/F7Q52MNK).

scholarly journals Assessment and inventory of forest parameters using medium-high resolution satellite data

2021 ◽  
Author(s):  
Ειρήνη Χρυσάφη
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Data ◽  
Earth Observation ◽  
Landsat 8 ◽  
Spectral Instrument ◽  
Stacked Generalization ◽  
Sentinel 2

Τα μεσογειακά δάση χαρακτηρίζονται από υψηλή χωροχρονική ετερογένεια και αποτελούν ένα από σημαντικότερα σημεία της βιοποικιλότητας στον πλανήτη. Η σημαντική αξία τους και το ευρύ φάσμα των οικοσυστημικών υπηρεσιών που παρέχουν, αναγνωρίζεται ευρέως από επιστήμονες, διεθνείς συμβάσεις και οργανισμούς. Ωστόσο, η ευπάθεια τους σε ανθρώπινες και φυσικές απειλές έχει ως αποτέλεσμα την διατάραξη τους. Συνεπώς, σχέδια βιώσιμης διαχείρισης και αειφορικής ανάπτυξης καθίστανται ως επιτακτική ανάγκη. Οι πρακτικές παρακολούθησης και απογραφής δασών απαιτούν την αξιόπιστη εκτίμηση δασικών παραμέτρων, όπως η κυκλική επιφάνεια, ο αριθμός δέντρων ανά μονάδα επιφάνειας και ξυλώδες όγκου. Η ετερογένεια των μεσογειακών δασών και η δύσκολη πρόσβασής τους, καθιστά την επιστήμη της τηλεπισκόπησης ως εξαιρετικά χρήσιμο μέσο για την αξιολόγηση των δασικών πόρων. Η τεχνολογία της τηλεπισκόπησης και τα ανοιχτά δεδομένα τηλεπισκόπησης παρέχουν μεγάλες δυνατότητες στον τομέα της δασολογίας και στην δασική απογραφή. Επιπλέον, η ταχεία πρόοδος στους αλγόριθμους τεχνητής νοημοσύνης διευκολύνει την ανάλυση ευρέος φάσματος δεδομένων. Σε αυτό το πλαίσιο, ο συνδυασμός αυτών των ισχυρών εργαλείων (δεδομένα τηλεπισκόπησης και προσεγγίσεις μηχανικής μάθησης) συνιστά μια πολλά υποσχόμενη, αλλά και ερευνητική πρόκληση, για την εκτίμηση δασικών παραμέτρων. Στη παρούσα διατριβή, εξετάζονται διάφορες προσεγγίσεις για την βελτιστοποίηση της εκτίμησης δασικών παραμέτρων με την χρήση δορυφορικών εικόνων και τεχνικών μηχανικής μάθησης.Η δομή της παρούσας διατριβής αποτελείται από τρία μέρη. Το πρώτο μέρος αποτελείται από τέσσερα κεφάλαια. Αρχικά, στο Κεφάλαιο 1, γίνεται μια εισαγωγή στην αξία των μεσογειακών δασών, στις υπηρεσίες που παρέχουν και στις απειλές που αντιμετωπίζουν. Το Κεφάλαιο 2 τονίζει την ανάγκη αειφορικής διαχείρισης των δασών και κατ 'επέκταση της απογραφής και αξιόπιστης εκτίμησης δασικών παραμέτρων. Στο Κεφάλαιο 3, παρουσιάζονται εν συντομία πηγές δεδομένων τηλεπισκόπησης και η συμβολή τους σε δασικές εφαρμογές και ιδιαίτερα στην εκτίμηση δασικών παραμέτρων, σε περιοχές της Μεσογείου. Το κεφάλαιο 4, αποτελεί μια εισαγωγή στους αλγόριθμους τεχνητής νοημοσύνης και μηχανικής μάθησης και πώς αυτές οι προσεγγίσεις εφαρμόζονται στον τομέα της τηλεπισκόπησης και της δασολογίας. Τέλος παρουσιάζονται τα ερευνητικά ερωτήματα και τα αντικείμενα της παρούσας διατριβής. Το δεύτερο μέρος αποτελείται από τέσσερα άρθρα, εκ των οποίων, το πρώτο (Κεφάλαιο 7) έχει δημοσιευτεί στο περιοδικό Remote Sensing of Environment (2017) και αφορά την εκτίμηση δασικών παραμέτρων χρησιμοποιώντας δια-εποχιακές εικόνες Landsat 8 Operational Land Imager. Το δεύτερο άρθρο (Κεφάλαιο 8) έχει δημοσιευτεί στο Remote Sensing Letters (2017) και αφορά τις σχέσεις μεταξύ ξυλώδες όγκου και εικόνων Sentinel-2 Multi Spectral Instrument. Το τρίτο άρθρο (Κεφάλαιο 9) έχει δημοσιευτεί στο περιοδικό International Journal of Applied Earth Observation and Geoinformation (2019) και αφορά την αξιολόγηση των δορυφορικών δεδομένων Sentinel-2 Multi Spectral Instrument για την εκτίμηση του ξυλώδες όγκου. Το τελευταίο άρθρο (Κεφάλαιο 10) που προορίζεται προς δημοσίευση, αποτελεί μια προκαταρκτική μελέτη για την εκτίμηση του ξυλώδες όγκου σε ένα μεσογειακό δασικό οικοσύστημα, με μία μετά-μαθησιακή προσέγγιση και την ανάπτυξη ενός μοντέλου συσσωρευμένης γενίκευσης (stacked generalization). Τέλος, στο τρίτο μέρος της παρούσας διατριβής παρουσιάζονται συνοπτικά οι απαντήσεις των ερωτημάτων που τέθηκαν στην παρούσα διατριβή και τα προβλήματα - περιορισμοί που αντιμετωπίστηκαν. Επίσης, προτείνονται δυνατότητες και προοπτικές εξέλιξης της παρούσας έρευνας, που θα μπορούσε να αποτελέσουν αντικείμενο για μελλοντική έρευνα.

scholarly journals Fusing Retrievals of High Resolution Aerosol Optical Depth from Landsat-8 and Sentinel-2 Observations over Urban Areas

2021 ◽  
Vol 13 (20) ◽  
pp. 4140
Author(s):  
Hao Lin ◽  
Siwei Li ◽  
Jia Xing ◽  
Jie Yang ◽  
Qingxin Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Retrieval Algorithm ◽  
Landsat 8 ◽  
Beijing City ◽  
Spatial Coverage ◽  
Sentinel 2

Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R2) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor.

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