scholarly journals Scientists Invited to Collaborate in Satellite Mission’s Debut

Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Rosemary Morrow ◽  
Lee-Lueng Fu ◽  
Francesco D’Ovidio ◽  
J. Farrar
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Ocean Topography

The Surface Water and Ocean Topography mission will begin by scanning Earth’s surface once a day. We invite ocean scientists to contribute ground-based measurements to compare with the satellite data.

The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A global river network for satellite data products

2021 ◽  
Author(s):  
Elizabeth H. Altenau ◽  
Tamlin M. Pavelsky ◽  
Michael T. Durand ◽  
Xiao Yang ◽  
Renato Prata de Moraes Frasson ◽  
...  
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
River Network ◽  
Data Products ◽  
Ocean Topography

Monitoring intertidal topography using the future SWOT (Surface Water and Ocean Topography) mission

2021 ◽  
pp. 100578
Author(s):  
Edward Salameh ◽  
Frédéric Frappart ◽  
Damien Desroches ◽  
Imen Turki ◽  
Denis Carbonne ◽  
...  
Keyword(s):  
Surface Water ◽  
The Future ◽  
Ocean Topography

Evaluating quality of surface water resources by ANN and ANFIS networks using Sentinel-2 satellite data

2022 ◽  
Author(s):  
Mostafa Kabolizadeh ◽  
Kazem Rangzan ◽  
Sajad Zareie ◽  
Mohsen Rashidian ◽  
Hossein Delfan
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Satellite Data ◽  
Surface Water Resources ◽  
Sentinel 2

An integrated decision support system using satellite and in-situ data for coastal area hazard mitigation and resilience to natural disasters

2021 ◽  
Author(s):  
Christos Kontopoulos ◽  
Nikos Grammalidis ◽  
Dimitra Kitsiou ◽  
Vasiliki Charalampopoulou ◽  
Anastasios Tzepkenlis ◽  
...  
Keyword(s):  
Decision Support ◽  
Surface Water ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Satellite Data ◽  
Hazard Mitigation ◽  
Google Earth ◽  
Landsat 8

<p>Nowadays, the importance of coastal areas is greater than ever, with approximately 10% of the global population living in these areas. These zones are an intermediate space between sea and land and are exposed to a variety of natural (e.g. ground deformation, coastal erosion, flooding, tornados, sea level rise, etc.) and anthropogenic (e.g. excessive urbanisation) hazards. Therefore, their conservation and proper sustainable management is deemed crucial both for economic and environmental purposes. The main goal of the Greece-China bilateral research project “EPIPELAGIC: ExPert Integrated suPport systEm for coastaL mixed urbAn – industrial – critical infrastructure monitorinG usIng Combined technologies” is the design and deployment of an integrated Decision Support System (DSS) for hazard mitigation and resilience. The system exploits near-real time data from both satellite and in-situ sources to efficiently identify and produce alerts for important risks (e.g. coastal flooding, soil erosion, degradation, subsidence), as well as to monitor other important changes (e.g. urbanization, coastline). To this end, a robust methodology has been defined by fusing satellite data (Optical/multispectral, SAR, High Resolution imagery, DEMs etc.) and in situ real-time measurements (tide gauges, GPS/GNSS etc.). For the satellite data pre-processing chain, image composite/mosaic generation techniques will be implemented via Google Earth Engine (GEE) platform in order to access Sentinel 1, Sentinel 2, Landsat 5 and Landsat 8 imagery for the studied time period (1991-2021). These optical and SAR composites will be stored into the main database of the EPIPELAGIC server, after all necessary harmonization and correction techniques, along with other products that are not yet available in GEE (e.g. ERS or Sentinel-1 SLC products) and will have to be locally processed. A Machine Learning (ML) module, using data from this main database will be trained to extract additional high-level information (e.g. coastlines, surface water, urban areas, etc.). Both conventional (e.g. Otsu thresholding, Random Forest, Simple Non-Iterative Clustering (SNIC) algorithm, etc.) and deep learning approaches (e.g. U-NET convolutional networks) will be deployed to address problems such as surface water detection and land cover/use classification. Additionally, in-situ or auxiliary/cadastral datasets will be used as ground truth data. Finally, a Decision Support System (DSS), will be developed to periodically monitor the evolution of these measurements, detect significant changes that may indicate impending risks and hazards, and issue alarms along with suggestions for appropriate actions to mitigate the detected risks. Through the project, the extensive use of Explainable Artificial Intelligence (xAI) techniques will also be investigated in order to provide “explainable recommendations” that will significantly facilitate the users to choose the optimal mitigation approach. The proposed integrated monitoring solutions is currently under development and will be applied in two Areas of Interest, namely Thermaic Gulf in Thessaloniki, Greece, and the Yellow River Delta in China. They are expected to provide valuable knowledge, methodologies and modern techniques for exploring the relevant physical mechanisms and offer an innovative decision support tool. Additionally, all project related research activities will provide ongoing support to the local culture, society, economy and environment in both involved countries, Greece and China.</p>

scholarly journals Anticipated Improvements to River Surface Elevation Profiles From the Surface Water and Ocean Topography Mission

2019 ◽  
Vol 7 ◽  
Author(s):  
Theodore Langhorst ◽  
Tamlin M. Pavelsky ◽  
Renato Prata de Moraes Frasson ◽  
Rui Wei ◽  
Alessio Domeneghetti ◽  
...  
Keyword(s):  
Surface Water ◽  
Surface Elevation ◽  
Ocean Topography

scholarly journals Surface waters properties in the Laptev and the East-Siberian Seas in summer 2018 from in situ and satellite data

2019 ◽  
Author(s):  
Anastasiia Tarasenko ◽  
Alexandre Supply ◽  
Nikita Kusse-Tiuz ◽  
Vladimir Ivanov ◽  
Mikhail Makhotin ◽  
...  
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Water Masses ◽  
Sea Surface ◽  
Ekman Transport ◽  
Water Displacement ◽  
Wind Speeds ◽  
In Situ Data ◽  
Ocean Salinity

Abstract. Variability of surface water masses of the Laptev and the East-Siberian seas in August–September 2018 is studied using in situ and satellite data. In situ data was collected during ARKTIKA-2018 expedition and then completed with satellite estimates of sea surface temperature (SST) and salinity (SSS), sea surface height, satellite-derived wind speeds and sea ice concentrations. Derivation of SSS is still challenging in high latitude regions, and the quality of Soil Moisture and Ocean Salinity (SMOS) SSS retrieval was improved by applying a threshold on SSS weekly error. The validity of SST and SSS products is demonstrated using ARKTIKA-2018 continuous thermosalinograph measurements and CTD casts. The surface gradients and mixing of river and sea waters in the free of ice and ice covered areas is described with a special attention to the marginal ice zone. The Ekman transport was calculated to better understand the pathway of surface water displacement. T-S diagram using surface satellite estimates shows a possibility to investigate the surface water masses transformation in detail.

scholarly journals Automated River Reach Definition Strategies: Applications for the Surface Water and Ocean Topography Mission

2017 ◽  
Vol 53 (10) ◽  
pp. 8164-8186 ◽  
Author(s):  
Renato Prata de Moraes Frasson ◽  
Rui Wei ◽  
Michael Durand ◽  
J. Toby Minear ◽  
Alessio Domeneghetti ◽  
...  
Keyword(s):  
Surface Water ◽  
River Reach ◽  
Ocean Topography

The Surface Water and Ocean Topography (SWOT) Mission

2010 ◽  
Author(s):  
Lee-Lueng Fu ◽  
Lee-Lueng Fu ◽  
Lee-Lueng Fu ◽  
Lee-Lueng Fu ◽  
Lee-Lueng Fu ◽  
...  
Keyword(s):  
Surface Water ◽  
Ocean Topography

scholarly journals An Observing System Simulation Experiment for the Calibration and Validation of the Surface Water Ocean Topography Sea Surface Height Measurement Using In Situ Platforms

2018 ◽  
Vol 35 (2) ◽  
pp. 281-297 ◽  
Author(s):  
Jinbo Wang ◽  
Lee-Lueng Fu ◽  
Bo Qiu ◽  
Dimitris Menemenlis ◽  
J. Thomas Farrar ◽  
...  
Keyword(s):  
Surface Water ◽  
Simulation Experiment ◽  
System Simulation ◽  
Sea Surface ◽  
Station Keeping ◽  
Calibration And Validation ◽  
Wavenumber Spectrum ◽  
Ocean Topography ◽  
Observing System Simulation Experiment

AbstractThe wavenumber spectrum of sea surface height (SSH) is an important indicator of the dynamics of the ocean interior. While the SSH wavenumber spectrum has been well studied at mesoscale wavelengths and longer, using both in situ oceanographic measurements and satellite altimetry, it remains largely unknown for wavelengths less than ~70 km. The Surface Water Ocean Topography (SWOT) satellite mission aims to resolve the SSH wavenumber spectrum at 15–150-km wavelengths, which is specified as one of the mission requirements. The mission calibration and validation (CalVal) requires the ground truth of a synoptic SSH field to resolve the targeted wavelengths, but no existing observational network is able to fulfill the task. A high-resolution global ocean simulation is used to conduct an observing system simulation experiment (OSSE) to identify the suitable oceanographic in situ measurements for SWOT SSH CalVal. After fixing 20 measuring locations (the minimum number for resolving 15–150-km wavelengths) along the SWOT swath, four instrument platforms were tested: pressure-sensor-equipped inverted echo sounders (PIES), underway conductivity–temperature–depth (UCTD) sensors, instrumented moorings, and underwater gliders. In the context of the OSSE, PIES was found to be an unsuitable tool for the target region and for SSH scales 15–70 km; the slowness of a single UCTD leads to significant aliasing by high-frequency motions at short wavelengths below ~30 km; an array of station-keeping gliders may meet the requirement; and an array of moorings is the most effective system among the four tested instruments for meeting the mission’s requirement. The results shown here warrant a prelaunch field campaign to further test the performance of station-keeping gliders.

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