Simultaneous retrieval of aerosols and in-water constituents in turbid coastal waters using multi- and hyperspectral data

2004 ◽  
Author(s):  
Hans A. Eide ◽  
Wei Li ◽  
Knut Stamnes ◽  
Robert Spurr ◽  
Jakob J. Stamnes
Keyword(s):  
Coastal Waters ◽  
Hyperspectral Data ◽  
Water Constituents

scholarly journals Can Water Constituents Be Used as Proxy to Map Microplastic Dispersal Within Transitional and Coastal Waters?

2020 ◽  
Vol 8 ◽  
Author(s):  
Sarah Piehl ◽  
Elizabeth C. Atwood ◽  
Mathias Bochow ◽  
Hannes K. Imhof ◽  
Jonas Franke ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Water Constituents

Remote estimation of in water constituents in coastal waters using neural networks

2014 ◽  
Author(s):  
Ioannis Ioannou ◽  
Alexander Gilerson ◽  
Michael Ondrusek ◽  
Soe Hlaing ◽  
Robert Foster ◽  
...  
Keyword(s):  
Neural Networks ◽  
Coastal Waters ◽  
Water Constituents ◽  
Remote Estimation

scholarly journals ENABLING SEARCHES ON WAVELENGTHS IN A HYPERSPECTRAL INDICES DATABASE

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 161-164
Author(s):  
F. Piñuela ◽  
D. Cerra ◽  
R. Müller
Keyword(s):  
Spectral Range ◽  
Ad Hoc ◽  
Spectral Band ◽  
Hyperspectral Data ◽  
Physical Parameters ◽  
Spectral Indices ◽  
Central Wavelength ◽  
Water Constituents ◽  
Basic And Applied Research ◽  
Near Future

Spectral indices derived from hyperspectral reflectance measurements are powerful tools to estimate physical parameters in a non-destructive and precise way for several fields of applications, among others vegetation health analysis, coastal and deep water constituents, geology, and atmosphere composition. In the last years, several micro-hyperspectral sensors have appeared, with both full-frame and push-broom acquisition technologies, while in the near future several hyperspectral spaceborne missions are planned to be launched. This is fostering the use of hyperspectral data in basic and applied research causing a large number of spectral indices to be defined and used in various applications. Ad hoc search engines are therefore needed to retrieve the most appropriate indices for a given application. In traditional systems, query input parameters are limited to alphanumeric strings, while characteristics such as spectral range/ bandwidth are not used in any existing search engine. Such information would be relevant, as it enables an inverse type of search: given the spectral capabilities of a given sensor or a specific spectral band, find all indices which can be derived from it. This paper describes a tool which enables a search as described above, by using the central wavelength or spectral range used by a given index as a search parameter. This offers the ability to manage numeric wavelength ranges in order to select indices which work at best in a given set of wavelengths or wavelength ranges.

scholarly journals Atmospheric Correction of Airborne Hyperspectral CASI Data Using Polymer, 6S and FLAASH

2021 ◽  
Vol 13 (24) ◽  
pp. 5062
Author(s):  
Mengmeng Yang ◽  
Yong Hu ◽  
Hongzhen Tian ◽  
Faisal Ahmed Khan ◽  
Qingping Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Spectral Shape ◽  
Time Difference ◽  
The Other ◽  
Atmospheric Effects ◽  
Aerosol Scattering ◽  
Correction Technique

Airborne hyperspectral data play an important role in remote sensing of coastal waters. However, before their application, atmospheric correction is required to remove or reduce the atmospheric effects caused by molecular and aerosol scattering and absorption. In this study, we first processed airborne hyperspectral CASI-1500 data acquired on 4 May 2019 over the Uljin coast of Korea with Polymer and then compared the performance with the other two widely used atmospheric correction approaches, i.e., 6S and FLAASH, to determine the most appropriate correction technique for CASI-1500 data in coastal waters. Our results show the superiority of Polymer over 6S and FLAASH in deriving the Rrs spectral shape and magnitude. The performance of Polymer was further evaluated by comparing CASI-1500 Rrs data with those obtained from the MODIS-Aqua sensor on 3 May 2019 and processed using Polymer. The spectral shapes of the derived Rrs from CASI-1500 and MODIS-Aqua matched well, but the magnitude of CASI-1500 Rrs was approximately 0.8 times lower than MODIS Rrs. The possible reasons for this difference were time difference (1 day) between CASI-1500 and MODIS data, higher land adjacency effect for MODIS-Aqua than for CASI-1500, and possible errors in MODIS Rrs from Polymer.

scholarly journals Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters

2021 ◽  
Vol 9 ◽  
Author(s):  
Anna E. Windle ◽  
Greg M. Silsbe
Keyword(s):  
Water Quality ◽  
Coastal Waters ◽  
Total Suspended Solid ◽  
Water Quality Monitoring ◽  
Suspended Solid ◽  
Quality Monitoring ◽  
Future Research ◽  
Quality Properties ◽  
Water Constituents

Unoccupied aircraft systems (UAS, or drones) equipped with off-the-shelf multispectral sensors originally designed for terrestrial applications can also be used to derive water quality properties in coastal waters. The at-sensor total radiance a UAS measured constitutes the sum of water-leaving radiance (LW) and incident radiance reflected off the sea surface into the detector’s field of view (LSR). LW is radiance that emanates from the water and contains a spectral shape and magnitude governed by optically active water constituents interacting with downwelling irradiance while LSR is independent of water constituents and is instead governed by a given sea-state surface reflecting light; a familiar example is sun glint. Failure to accurately account for LSR can significantly influence Rrs, resulting in inaccurate water quality estimates once algorithms are applied. The objective of this paper is to evaluate the efficacy of methods that remove LSR from total UAS radiance measurements in order to derive more accurate remotely sensed retrievals of scientifically valuable in-water constituents. UAS derived radiometric measurements are evaluated against in situ hyperspectral Rrs measurements to determine the best performing method of estimating and removing surface reflected light and derived water quality estimates. It is recommended to use a pixel-based approach that exploits the high absorption of water at NIR wavelengths to estimate and remove LSR. Multiple linear regressions applied to UAS derived Rrs measurements and in situ chlorophyll a and total suspended solid concentrations resulted in 37 and 9% relative error, respectively, which is comparable to coastal water quality algorithms found in the literature. Future research could account for the high resolution and multi-angular aspect of LSR by using a combination of photogrammetry and radiometry techniques. Management implications from this research include improved water quality monitoring of coastal and inland water bodies in order to effectively track trends, identify and mitigate pollution sources, and discern potential human health risks.

Chlorophyll-a retrieval of coastal waters based on in situ hyperspectral data

2009 ◽  
Author(s):  
Wandong Ma ◽  
Ping Shi ◽  
Yuanzhi Zhang ◽  
Qianguo Xing ◽  
Jiakui Tang ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Coastal Waters ◽  
Hyperspectral Data

scholarly journals Spectral and Radiometric Measurement Requirements for Inland, Coastal and Reef Waters

2020 ◽  
Vol 12 (14) ◽  
pp. 2247 ◽  
Author(s):  
Peter Gege ◽  
Arnold G. Dekker
Keyword(s):  
Spectral Resolution ◽  
Coastal Waters ◽  
Signal To Noise Ratio ◽  
Shallow Waters ◽  
Radiometric Measurement ◽  
Colored Dissolved Organic Matter ◽  
Water Constituents ◽  
Concentration Changes ◽  
Variable Water

This paper studies the measurement requirements of spectral resolution and radiometric sensitivity to enable the quantitative determination of water constituents and benthic parameters for the majority of optically deep and optically shallow waters on Earth. The spectral and radiometric variability is investigated by simulating remote sensing reflectance (Rrs) spectra of optically deep water for twelve inland water scenarios representing typical and extreme concentration ranges of phytoplankton, colored dissolved organic matter and non-algal particles. For optically shallow waters, Rrs changes induced by variable water depth are simulated for fourteen bottom substrate types, from lakes to coastal waters and coral reefs. The required radiometric sensitivity is derived for the conditions that the spectral shape of Rrs should be resolvable with a quantization of 100 levels and that measurable reflection differences at at least one wavelength must occur at concentration changes in water constituents of 10% and depth differences of 20 cm. These simulations are also used to derive the optimal spectral resolution and the most sensitive wavelengths. Finally, the Rrs spectra and their changes are converted to radiances and radiance differences in order to derive sensor (noise-equivalent radiance) and measurement requirements (signal-to-noise ratio) at the water surface and at the top of the atmosphere for a range of solar zenith angles.

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