A spaceborne coastal and inland water color hyperspectral imager

Qian, Shen‐En; Bergeron, Martin; Djazovski, Oleg; Maszkiewicz, Michael; Girard, Ralph; Kappus, Mary E.; Bowles, Jeffrey H.; Mannino, Antonio; Matuszeski, Adam; Furlong, Michael J.; Ardouin, Jean-Pierre; Fournier, Georges; Lévesque, Josée; Lavigne, Jean‐François; Mandar, Julie; Busler, Jennifer; Buttner, G.

doi:10.1109/igarss.2017.8126990

Cited by 4 publications

(8 citation statements)

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“…The Canadian Space Agency (CSA) has planned the WaterSat microsatellite mission to improve its RS capabilities in inland waters (particularly rivers and lakes below 10 km in width) and in near-shore coastal areas. The proposed specifications for the WaterSat optical imager are aligned with the above outlined aquatic needs: 100 m spatial resolution over a 300 km swath width and 10 nm spectral resolution from 400 nm to 1000 nm [106]. Clearly the proposed 100 m spatial resolution would not be well suited to small SAV patches, rather it would be appropriate for assessing SAV communities at large.…”

Section: Advancing Technologiesmentioning

confidence: 99%

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Rowan

Kalácska

2021

Remote Sensing

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Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

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Section: Advancing Technologiesmentioning

confidence: 99%

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Rowan

Kalácska

2021

Remote Sensing

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“…The Canadian Space Agency (CSA) and NASA agree on the need to improve satellite RS capabilities for underwater environments (Qian et al 2017). There are therefore several sensors being developed to address that need.…”

Section: Advancing Technologiesmentioning

confidence: 99%

“…The CSA has planned the WaterSat microsatellite mission to improve its RS capabilities in inland waters (particularly rivers and lakes below 10 km in width) and in near-shore coastal areas. The proposed specifications for the WaterSat optical imager are aligned with the previously identified aquatic needs: 100 m spatial resolution over a 300 km swath width and 10 nm spectral resolution from 400 nm to 1000 nm (Qian et al 2017). A prototype for this optical sensor, the Water Imaging Spectrometer Experiment (WISE), was developed by ITRES in 2018 (Achal et al 2018) and is currently being tested.…”

Section: Advancing Technologiesmentioning

confidence: 99%

“…NASA is also planning improvements to its satellite constellation to include better aquatic capabilities. The Pre-Aerosol, Clouds and ocean Ecosystems (PACE) sensor, planned to be launched in 2022/2023, will provide ocean colour information and data relating to phytoplankton and atmospheric conditions at a 1 km spatial resolution (Del Castillo and Platnick 2012; Qian et al 2017). If the WaterSat and WISE prove successful, a similar sensor called the Coastal Ocean Colour Imager will be added to the PACE mission, thus providing large spatial scale multispectral, and finer spatial scale hyperspectral, imagery.…”

Section: Advancing Technologiesmentioning

confidence: 99%

“…NASA is additionally developing two new active sensors for aquatic observation purposes: the MiDAR, designed to help correct for the effects of waves, and the Surface Water & Ocean Topography (SWOT) sensor which will allow improved measurements of in-land and marine surface height to better understand hydrological dynamics (Chirayath and Earle 2016;Fu 2016). The completion and deployment of these sensors is expected to drastically improve satellite RS observation of ocean colour and distinct targets like macrophytes (Qian et al 2017).…”

Section: Advancing Technologiesmentioning

confidence: 99%

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Remote sensing of submerged aquatic vegetation: an introduction and best practices review

Rowan¹,

Kalácska²

2020

Preprint

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Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing can provide the efficient, accurate and large-scale monitoring needed to ensure proper SAV management. Our objective is to introduce remote sensing to researchers in the field of aquatic ecology. Applying remote sensing to the underwater environment is more complex in comparison to terrestrial studies due to the water column. A wide range of sensors and platforms from remotely operated vehicles to satellites are available for use in the underwater environment, a sample of which being presented herein. The utility of any sensor/platform combination varies depending on the aquatic conditions being observed. An overview of the required corrections, processing, and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detecting SAV are briefly presented and notable results and lessons are discussed.

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