New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea

Abstract. In the framework of the European Space Agency (ESA) regional initiative called Earth Observation data For Science and Innovation in the Black Sea (EO4SIBS), a new dedicated Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) product is generated for the Black Sea for the years 2011–2020. Three SMOS SSS fields are retrieved and distributed: a level 2 product consisting of binned SSS in daily maps at 0.25° × 0.25° spatial resolution grid by considering ascending ((Olmedo et al., 2021b), https://doi.org/10.20350/digitalCSIC/13993) and descending ((Olmedo et al., 2021c), https://doi.org/10.20350/digitalCSIC/13995) satellite overpass directions separately; a level 3 product ((Olmedo et al., 2021d), https://doi.org/10.20350/digitalCSIC/13996) consisting of binned SSS in 9-day maps at 0.25° × 0.25° grid by combining as cending and descending satellite overpass directions; and a level 4 product ((Olmedo et al., 2021e), https://doi.org/10.20350/digitalCSIC/13997) consisting of daily maps at 0.05 × 0.0505° that are computed by merging the level 3 SSS product with Sea Surface Temperature (SST) maps. The generation of SMOS SSS fields in the Black Sea requires the use of enhanced data processing algorithms for improving the Brightness Temperatures in the region since this basin is typically strongly affected by Radio Frequency Interference (RFI) sources which hinders the retrieval of salinity. Here, we describe the algorithms introduced to improve the quality of the salinity retrieval in this basin. The validation of the EO4SIBS SMOS SSS products is performed by: i) comparing the EO4SIBS SMOS SSS products with near-to-surface salinity measurements provided by in situ measurements; ii) assessing the geophysical consistency of the products by comparing them with a model and other satellite salinity measurements; iii) computing maps of SSS errors by using Correlated Triple Collocation analysis. The accuracy of the EO4SIBS SMOS SSS products depend on the time period and on the product level. The accuracy in the period 2016–2020 is better than in 2011–2015 and it is as follows for the different products: i) Level 2 ascending: 1.85 / 1.50 psu (in 2011–2015 / 2016–2020); Level 2 descending: 2.95 1.95 psu; ii) Level 3: 0.7 / 0.5 psu; and iii) Level 4: 0.6 / 0.4 psu.

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Earth Observation products for Science and Innovation in the Black Sea

10.5194/egusphere-egu21-10237 ◽

2021 ◽

Author(s):

Marilaure Grégoire ◽

Keyword(s):

Black Sea ◽

Earth Observation ◽

Sea Surface Salinity ◽

The Black Sea ◽

Full Potential ◽

Ocean Colour ◽

Surface Salinity ◽

Mesoscale Dynamics

The Black Sea is a small enclosed basin where coastal regions have a large influence and mesoscale signals dominate the dynamics (the Rossby radius of deformation is about 20km). Large riverine inputs, mainly on the northwestern shelf, induce well-marked horizontal gradients in the distribution of the Black Sea salinity and optical characteristics: coastal and shelf waters have very low salinity and contain large amounts of optically active materials (e.g. coloured dissolved organic matter) and its oligotrophic deep sea has a salinity around 18.2. The presence of these contrasting water characteristics in a relatively small enclosed environment, combined with land contamination and the specificities of its atmospheric composition(e.g. high cloud coverage, aerosols) make the Black Sea a challenging area for the development of high quality satellite products.&#160;&#160;We present first results from a 2-year on-going ESA-funded project, EO4SIBS (Earth Observation for Science and Innovation in the Black Sea) dedicated to the development, and subsequent scientific analysis, of new algorithms and products. In particular, ocean colour products (chlorophyll-a and total suspended matter concentrations, turbidity) were produced from Sentinel 3 (S3) OLCI data combining different algorithms selected based on an automatic water mass classification procedure (case-1 versus case-2 waters). In specific areas, S3-OLCI and Sentinel 2-MSI data were merged to address local features. A revised gridded altimetry product based on 5-Hz along track data (combining Cryosat and S3 SAR) was produced and validated in the coastal zone with tide gauge data. Sea Surface Salinity was derived from the L-Band measured by SMOS and compared with in-situ surface salinity data from field sampling and Argo.&#160;&#160;All these products are now being integrated to further understand the Black Sea physical and biogeochemical functioning (e.g., plume and productivity patterns, mesoscale dynamics, deoxygenation). For instance, the Black Sea mesoscale dynamics are inferred from the 5-Hz altimetry product using an eddy detection and tracking algorithm. The quality of the eddy mapping is assessed by comparison with visible and infrared satellite products while the derived velocities are compared with drifters. Also, the benefit of assimilating ocean colour data in the Black Sea operational model (also known as CMEMS BS-MFC BIO) for the prediction of the Black Sea ecosystem will&#160; be illustrated.&#160;Gridded products are archived as CF-compliant NetCDF files and disseminated through ncWMS protocol. In-situ data are modeled as vector points in a PostGIS database. A web portal is being implemented in order to propose an efficient spatiotemporal exploration of both data sources in a user-friendly interface, including interactive map layers and export possibilities.&#160;We conclude with a set of recommendations for observational requirements needed&#160; to increase the quality of satellite products in the Black Sea and to be able to use the full potential of current and new information provided by&#160; satellites.&#160;&#160;

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JupyTEP IDE as an Online Tool for Earth Observation Data Processing

Remote Sensing ◽

10.3390/rs11171973 ◽

2019 ◽

Vol 11 (17) ◽

pp. 1973 ◽

Cited By ~ 5

Author(s):

Rapiński ◽

Bednarczyk ◽

Zinkiewicz

Keyword(s):

User Interface ◽

Data Processing ◽

European Space Agency ◽

Earth Observation ◽

Observation Data ◽

Cloud Infrastructure ◽

Integrated Development Environment ◽

Development Environment ◽

Integrated Development ◽

Earth Observation Data

The paper describes a new tool called JupyTEP integrated development environment (IDE), which is an online integrated development environment for earth observation data processing available in the cloud. This work is a result of the project entitled “JupyTEP IDE—Jupyter-based IDE as an interactive and collaborative environment for the development of notebook style EO algorithms on network of exploitation platforms infrastructure” carried out in cooperation with European Space Agency. The main goal of this project was to provide a universal earth observation data processing tool to the community. JupyTEP IDE is an extension of Jupyter software ecosystem with customization of existing components for the needs of earth observation scientists and other professional and non-professional users. The approach is based on configuration, customization, adaptation, and extension of Jupyter, Jupyter Hub, and Docker components on earth observation data cloud infrastructure in the most flexible way; integration with accessible libraries and earth observation data tools (sentinel application platform (SNAP), geospatial data abstraction library (GDAL), etc.); adaptation of existing web processing service (WPS)-oriented earth observation services. The user-oriented product is based on a web-related user interface in the form of extended and modified Jupyter user interface (frontend) with customized layout, earth observation data processing extension, and a set of predefined notebooks, widgets, and tools. The final IDE is addressed to the remote sensing experts and other users who intend to develop Jupyter notebooks with the reuse of embedded tools, common WPS interfaces, and existing notebooks. The paper describes the background of the system, its architecture, and possible use cases.

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GENERATING BIOLOGICALLY RELEVANT ENVIRONMENTAL DATA FROM REMOTE SENSING IMAGERIES AND OCEANOGRAPHIC MODELS TO SUPPORT SPATIAL PRIORITIZATION OF MARINE BIODIVERSITY CONSERVATION AND MANAGEMENT IN INDONESIA

Seminar Nasional Geomatika ◽

10.24895/sng.2018.3-0.1064 ◽

2019 ◽

Vol 3 ◽

pp. 965

Author(s):

Safran Yusri ◽

Vincentius P. Siregar ◽

Suharsono Suharsono

Keyword(s):

Environmental Variables ◽

Google Earth ◽

Earth Observation ◽

Environmental Data ◽

Marine Biodiversity ◽

Observation Data ◽

Distribution Models ◽

Surface Salinity ◽

Biologically Relevant ◽

Earth Observation Data

Long term Earth observation data stored in Google Earth Engine (GEE) can be ingested and derived to biologically relevant environmental variables that can used as the predictors of a species niche. The aim of this research was to create a script using GEE to generate biologically meaningful environmental variables from various Earth observation data and models in Indonesia. Elevation and bathymetry raster data from GEBCO were land masked and benthic terrain modelling were done in order to get the aspect, depth, curvature, and slope. HYCOM and MODIS AQUA dataset were filtered using spatial (Indonesia and surrounding region) and temporal filter (from 2002–2017), and reduced to biologically meaningful variables, the maximum, minimum, and mean. Water speed vector (northward and eastward) data were also converted in to scalar unit. In order to fill data gaps, kriging was done using Bayesian slope. Results shows the water depth in Indonesia ranges from 0 – 6827 m, with slope ranging from 0 – 34.33°, aspect from 0 – 359.99°, and curvature from 0 – 0.94. Variables representing water energy, mean sea surface elevation ranges from 0 – 0.85 m, and mean scalar water velocity 0 – 4 m/s. Mean surface salinity ranges from 20.09 – 35.32‰. Variables representing water quality includes mean of particulate organic carbon which ranges from 25.31 – 953.47‰ and mean of clorophyll-A concentration from 0.05 – 13.63‰. These data can be used as the input for species distribution models or spatially explicit decision support systems such as Marxan for spatial planning and zonation in Marine and Coastal Zone Management Plan.

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Earth observation Water Cycle Multi-Mission Observation Strategy (WACMOS)

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-7899-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 7899-7956 ◽

Cited By ~ 17

Author(s):

Z. Su ◽

W. Dorigo ◽

D. Fernández-Prieto ◽

M. Van Helvoirt ◽

K. Hungershoefer ◽

...

Keyword(s):

Water Cycle ◽

European Space Agency ◽

Earth Observation ◽

Observation Data ◽

Space Agency ◽

Observation Strategy ◽

Different Types ◽

Routine Basis ◽

Earth Observation Data ◽

Types Of Information

Abstract. Observing and monitoring the different components of the global water cycle and their dynamics are essential steps to understand the climate of the Earth, forecast the weather, predict natural disasters like floods and droughts, and improve water resources management. Earth observation technology is a unique tool to provide a global understanding of many of the essential variables governing the water cycle and monitor their evolution over time from global to basin scales. In the coming years an increasing number of Earth observation missions will provide an unprecedented capacity to quantify several of these variables on a routine basis. In this context, the European Space Agency (ESA), in collaboration with the Global Energy and Water Cycle Experiment (GEWEX) of the World Climate Research Program (WCRP), launched the Water Cycle Multi-Mission Observation Strategy (WACMOS) project in 2009. The project aims at developing and validating a novel set of geo-information products relevant to the water cycle covering the following thematic areas: evapotranspiration, soil moisture, cloud characterization and water vapour. The generation of these products is based on a number of innovative techniques and methods aiming at exploiting the synergies of different types of Earth observation data available today to the science community. This paper provides an overview of the major findings of the project with the ultimate goal of demonstrating the potential of innovative multi-mission based strategies to improve current observations by maximizing the synergistic use of the different types of information provided by the currently available observation systems.

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Usage of Earth Observation for Solar Energy Market Development: Lessons Learned

Solar Energy ◽

10.1115/isec2006-99113 ◽

2006 ◽

Author(s):

M. Schroedter-Homscheidt ◽

S. Bofinger ◽

H. Breitkreuz ◽

G. Heilscher ◽

S. Stettler

Keyword(s):

Solar Energy ◽

Investment Decision ◽

European Space Agency ◽

Earth Observation ◽

Lessons Learned ◽

Market Development ◽

Plant Management ◽

Observation Data ◽

Large Set ◽

Earth Observation Data

Within the Earth Observation Market Development (EOMD) program of the European Space Agency (ESA) the ENVISOLAR project aims at an intensified usage of earth observation based information products in the solar energy industries. Existing services for investment decision, plant management, load forecasting, and science and consulting rely on high quality surface solar irradiance measurements and reliable processing chains to deliver such information regularly. Requirements for earth observation data as well as blockages preventing their use have been identified. In consequence, existing data processing chains are analyzed as to their conformity with the needs of the solar industry. The paper focuses on how earth observation itself can contribute to the market development of solar energy technologies. Issues like quality of irradiance data for planning and managing solar energy systems, reliability and availability of earth observation information, requirements as to temporal, spatial and spectral resolution of earth observation data, and the cost-effectiveness of satellite based information compared to maintenance costs for a large set of on-site measurement devices are addressed.

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Variational assimilation of observation data in the mathematical model of the Black Sea taking into account the tide-generating forces

Russian Journal of Numerical Analysis and Mathematical Modelling ◽

10.1515/rnam-2015-0013 ◽

2015 ◽

Vol 30 (3) ◽

Cited By ~ 2

Author(s):

Valeriy I. Agoshkov ◽

Maksim V. Assovskii ◽

Vladimir B. Zalesny ◽

Natalia B. Zakharova ◽

Eugene I. Parmuzin ◽

...

Keyword(s):

Mathematical Model ◽

Black Sea ◽

Sea Surface ◽

The Black Sea ◽

Observation Data ◽

Variational Assimilation ◽

Flux Function ◽

The Mean ◽

The Mathematical Model ◽

Satellite Altimetry Data

AbstractA mathematical model of the dynamics of the Black and the Azov Seas is considered taking into account tide-generating forces. The problem of variational assimilation of sea surface temperature (SST) data is formulated and studied. Based on variational assimilation of satellite altimetry data, we propose an algorithm for solving the inverse problem of reconstruction of potential forces affecting the formation of the mean level and present a method of approximate solution of this problem.We also present numerical experiments concerning the study of the influence of tide-generating forces on the dynamics of the Black Sea and restoration of the heat flux function in the problem of variational data assimilation of SST observations

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Aquarius Sea Surface Salinity Gridding Method Based on Dual Quality–Distance Weighting

Remote Sensing ◽

10.3390/rs11091131 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1131

Author(s):

Yanyan Li ◽

Qing Dong ◽

Yongzheng Ren

Keyword(s):

Sea Surface ◽

New Method ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Interpolation Methods ◽

Distance Weighting ◽

Level 3 ◽

Along Track ◽

Level 2 ◽

Buoy Measurements

A new method for improving the accuracy of gridded sea surface salinity (SSS) fields is proposed in this paper. The method mainly focuses on dual quality–distance weighting of the Aquarius level 2 along-track SSS data according to quality flags, which represent nonnominal data conditions for measurements. In the weighting progress, 14 data conditions were considered, and their geospatial distributions and influences on the SSS were also visualized and evaluated. Three interpolation methods were employed, and weekly gridded SSS maps were produced for the period from September 2011 to May 2015. These maps were evaluated via comparisons with concurrent Argo buoy measurements. The results show that the proposed method improved the accuracy of the SSS fields by approximately 36% compared to the officially released weekly level 3 products and yielded root mean squared difference (RMSD), correlation and bias values of 0.19 psu, 0.98 and 0.01 psu, respectively. These findings indicate a significant improvement in the accuracy of the SSS fields and provide a better understanding of the influences of different conditions on salinity.

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Sea Surface Salinity trends in the Mediterranean and Black Seas from 10 years of SMOS measurements

10.5194/egusphere-egu2020-21636 ◽

2020 ◽

Author(s):

Estrella Olmedo ◽

Verónica González-Gambau ◽

Antonio Turiel ◽

Cristina González-Haro ◽

Justino Martínez ◽

...

Keyword(s):

Mediterranean Sea ◽

Black Sea ◽

Eastern Mediterranean ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Black Sea ◽

Surface Salinity ◽

Run Off ◽

The Mediterranean ◽

Regional Product

The monitoring of the sea surface salinity (SSS) in the semi-enclosed seas has a significant impact in the study of the climate change. In those basins the oceanographic processes occur at higher temporal scales than in the open ocean, and therefore, trends and anomalies can be detected before. The Mediterranean Sea is a strongly evaporative basin (evaporation exceeds the precipitation and river run-off). Converserly, in the Black Sea the river run-off and precipitation exceeds the evaporation. Based on a 4-year time series (2015-2019) of SMAP SSS, a recent study has shown that there is an increase of the salinity in the Eastern Mediterranean [Grodsky, et al. 2019]. On the other hand, the Black Sea exhibits a rich variability in space and time from (sub)mesoscale to larger scales (interannual and larger)&#160; that needs to be appropriately taken into account when trying to identify long-term trends. We present new estimates of SSS trends in the Mediterranean and Black Seas. These estimations are based on 10-year series obtained from the European Soil Moisture and Ocean Salinity (SMOS) mission. Two new SMOS SSS regional products have been generated. On the one hand, we have generated a new realease of SMOS SSS regional product for the Mediterranean Sea. The new release of SMOS SSS regional product for the Mediterranean Sea provides better coverage in the Eastern Mediterranean than the previous version of this product (see [Olmedo et al 2018]). The new dedicated SMOS SSS product for the Black Sea has been developed under the currently on-going ESA EO4BIS contract (An Earth Observation Data for Science and Innovation in the Black Sea). The Black Sea and the Eastern Mediterranean are strongly affected by Radio Frequency Interferences (RFI) sources, which hamper the salinity retrieval. We have applied specific methodologies to diminish the strong RFI effects in these two basins [Gonz&#225;lez-Gambau et al 2017].&#160; The new realase of these two SMOS SSS regional products will be available soon in the Barcelona Expert Center website (http://bec.icm.csic.es ). At this conference we will present&#160;the&#160;methodologies that we have used for the generation of&#160;both&#160;regional&#160;SMOS SSS products.&#160;We will&#160;also&#160;present a quality assessment&#160;over the&#160;two regions consisting of&#160;comparing&#160;with in situ salinity measurements. Finally, we will show the SSS trends that are obtained in the different basin (and sub-basins) as well as the significance of the results with respect to the accuracy of the new SMOS SSS products. [Grodsky, et al. 2019] Grodsky S., et al. (2019), &#8220;Eastern Mediterranean salinification observed in satellite salinity from SMAP mission&#8221;, Journal of Marine Systems,&#160; 198 [Olmedo et al 2018] Olmedo, E, et al. , (2018) &#8220;Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis,&#8221; Remote sensing,&#160; 10(3), 485. [Gonz&#225;lez-Gambau et al 2017] Gonz&#225;lez-Gambau, V. et. al, (2017), "Improvements on calibration and image reconstruction of SMOS for salinity retrievals in coastal regions," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 7, 3064-3078

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Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽

10.26643/gis.v12i3.5187 ◽

2019 ◽

Vol 12 (3) ◽

pp. 12-14

Author(s):

Eicher, A

Keyword(s):

Earth Observation ◽

Observation Data ◽

Business World ◽

The Earth ◽

Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

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