Sentinel-2 MSI and Sentinel-3 OLCI consistent ocean colour products using POLYMER

2018 ◽  
Author(s):  
François Steinmetz ◽  
Didier Ramon
Keyword(s):  
Ocean Colour ◽  
Sentinel 2

Multi-scale ocean colour synergy products for coastal water quality monitoring

2020 ◽  
Author(s):  
Dimitry Van der Zande ◽  
Aida Alvera-Azcárate ◽  
Charles Troupin ◽  
João Cardoso Dos Santos ◽  
Dries Van den Eynde
Keyword(s):  
Spatial Resolution ◽  
Spectral Characteristics ◽  
Empirical Orthogonal Functions ◽  
High Temporal Resolution ◽  
Ocean Colour ◽  
Orthogonal Functions ◽  
Multi Scale ◽  
Medium Resolution ◽  
Sentinel 2

<p>High-quality satellite-based ocean colour products can provide valuable support and insights in the management and monitoring of coastal ecosystems. Today’s availability of Earth Observation (EO) data is unprecedented including medium resolution ocean colour systems (e.g. Sentinel-3/OLCI), high resolution land sensors (e.g. Sentinel-2/MSI) and geostationary satellites (e.g. MSG/SEVIRI). Each of these sensors offers specific advantages in terms of spatial, temporal or radiometric characteristics. In the Multi-Sync project, we developed advanced ocean colour products (i.e. remote sensing reflectance, turbidity, and chlorophyll a concentration) through the synergetic use of these multi-scale EO data taking advantage of spectral characteristics of traditional medium resolution sensors, the high spatial resolution of some land sensors and the high temporal resolution of geostationary sensors.</p><p>To achieve this goal a multi-scale DINEOF (Data Interpolating Empirical Orthogonal Functions) approach was developed to reconstruct missing data using empirical orthogonal functions (EOF), reduce noise and exploit spatio-temporal coherency by joining several spatial and temporal resolutions. Here we present the capacity of DINEOF to extract multi-scale information through the integration of Sentinel-3, Sentinel-2 and SEVIRI datasets.</p><p>The functionality of the advanced multi-scale products will be demonstrated in a case study for the Belgian Coastal Zone (BCZ) highly relevant to the user community: sediment transport modelling near the harbour of Zeebrugge in support of dredging operations. As stated in the OSPAR treaty (1992), Belgium is obliged to monitor and evaluate the effects of all human activities on the marine ecosystem. Dredging activities in and near Belgian harbors fall under this treaty and are performed daily to ensure accessibility of the port by ships. Optimization of these dredging activities requires monitoring data which is typically acquired through in situ observations or modelling data. In this case study we take advantage of Sentinel-3, Sentinel-2 and SEVIRI data characteristics to provide a satellite product that meets the end user requirements in terms of product quality and temporal/spatial resolution.</p><p> </p>

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>

Variation in ocean colour may help predict cod and haddock recruitment

2013 ◽  
Vol 491 ◽  
pp. 187-197 ◽  
Author(s):  
MK Trzcinski ◽  
E Devred ◽  
T Platt ◽  
S Sathyendranath
Keyword(s):  
Ocean Colour

Estimación del Factor C de la RUSLE en la microcuenca del rio Lodana, Ecuador, usando imágenes del satélite Sentinel 2

2020 ◽  
Vol ve2020 (2) ◽  
pp. 69-75
Author(s):  
Ángel Claudio Ruiz Vélez ◽  
◽  
Henry Antonio Pacheco Gil ◽  
Keyword(s):  
Del Rio ◽  
Factor C ◽  
Sentinel 2

Surface chlorophyll a estimation in the Arabian Sea using IRS-P4 Ocean Colour Monitor (OCM) satellite data

2002 ◽  
Vol 23 (16) ◽  
pp. 3305-3305
Author(s):  
P. Chauhan ◽  
M. Mohan ◽  
R. K. Sarangi ◽  
B. Kumari ◽  
S. Nayak ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Satellite Data ◽  
Arabian Sea ◽  
Ocean Colour

Post-Fire Assessment of Burned Areas with Landsat-8 and Sentinel-2 Imagery Together with Modis and Viirs Active Fire Products

2020 ◽  
Author(s):  
Cesario Vincenzo Angelino ◽  
Luca Cicala ◽  
Sara Parrilli ◽  
Nicomino Fiscante ◽  
Silvia Liberata Ullo
Keyword(s):  
Landsat 8 ◽  
Burned Areas ◽  
Active Fire ◽  
Sentinel 2

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sentinel 2

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

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