scholarly journals Water Optics and Water Colour Remote Sensing

2017 ◽  
Vol 9 (8) ◽  
pp. 818 ◽  
Author(s):  
Yunlin Zhang ◽  
Claudia Giardino ◽  
Linhai Li
Keyword(s):  
Remote Sensing ◽  
Water Colour ◽  
Water Optics

scholarly journals Recognition of Water Colour Anomaly by Using Hue Angle and Sentinel 2 Image

2020 ◽  
Vol 12 (4) ◽  
pp. 716 ◽  
Author(s):  
Yelong Zhao ◽  
Qian Shen ◽  
Qian Wang ◽  
Fan Yang ◽  
Shenglei Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Large Scale ◽  
Measured Data ◽  
Water Bodies ◽  
Polluted Water ◽  
Water Colour ◽  
Hue Angle ◽  
Anomalous Water ◽  
Sentinel 2

As polluted water bodies are often small in area and widely distributed, performing artificial field screening is difficult; however, remote-sensing-based screening has the advantages of being rapid, large-scale, and dynamic. Polluted water bodies often show anomalous water colours, such as black, grey, and red. Therefore, the large-scale recognition of suspected polluted water bodies through high-resolution remote-sensing images and water colour can improve the screening efficiency and narrow the screening scope. However, few studies have been conducted on such kinds of water bodies. The hue angle of a water body is a parameter used to describe colour in the International Commission on Illumination (CIE) colour space. Based on the measured data, the water body with a hue angle greater than 230.958° is defined as a water colour anomaly, which is recognised based on the Sentinel-2 image through the threshold set in this study. The results showed that the hue angle of the water body was extracted from the Sentinel-2 image, and the accuracy of the hue angle calculated by the in situ remote-sensing reflectance Rrs (λ) was evaluated, where the root mean square error (RMSE) and mean relative error (MRE) were 4.397° and 1.744%, respectively, proving that this method is feasible. The hue angle was calculated for a water colour anomaly and a general water body in Qiqihar. The water body was regarded as a water colour anomaly when the hue angle was >230.958° and as a general water body when the hue angle was ≤230.958°. High-quality Sentinel-2 images of Qiqihar taken from May 2016 to August 2019 were chosen, and the position of the water body remained unchanged; there was no error or omission, and the hue angle of the water colour anomaly changed obviously, indicating that this method had good stability. Additionally, the method proposed is only suitable for optical deep water, not for optical shallow water. When this method was applied to Xiong’an New Area, the results showed good recognition accuracy, demonstrating good universality of this method. In this study, taking Qiqihar as an example, a surface survey experiment was conducted from October 14 to 15, 2018, and the measured data of six general and four anomalous water sample points were obtained, including water quality terms such as Rrs (λ), transparency, water colour, water temperature, and turbidity.

Numerical modelling of natural water colour: Implications for remote sensing and limnological studies

1998 ◽  
Vol 19 (10) ◽  
pp. 1913-1932 ◽  
Author(s):  
D. V. Pozdnyakov ◽  
K. Ya. Kondratyev ◽  
R. P. Bukata ◽  
J. H. Jerome
Keyword(s):  
Remote Sensing ◽  
Numerical Modelling ◽  
Natural Water ◽  
Water Colour

scholarly journals Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4125
Author(s):  
Mariana A. Soppa ◽  
Brenner Silva ◽  
François Steinmetz ◽  
Darryl Keith ◽  
Daniel Scheffler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Coastal Waters ◽  
Spectral Region ◽  
Atmospheric Correction ◽  
Hyperspectral Remote Sensing ◽  
Water Colour ◽  
Percentage Difference ◽  
Green Spectral Region

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

scholarly journals Remote Sensing-Based Analysis of Spatial and Temporal Water Colour Variations in Baiyangdian Lake after the Establishment of the Xiong’an New Area

2021 ◽  
Vol 13 (9) ◽  
pp. 1729
Author(s):  
Yelong Zhao ◽  
Shenglei Wang ◽  
Fangfang Zhang ◽  
Qian Shen ◽  
Junsheng Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Seasonal Variations ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
The South ◽  
Baiyangdian Lake ◽  
Water Colour ◽  
Annual Variations

The Forel-Ule Index (FUI) is an important parameter that can be calculated from optical remote sensing data to assess water quality based on water colour. Using Sentinel-2 images from April to November within the 2016–2020 period coupled with the Google Earth Engine Platform, we calculated FUI to analyse the spatial distribution, seasonal variations, and inter-annual variations of water colour in Baiyangdian Lake in the Xiong’an New Area established on 1 April 2017. The lake was divided into seven sub-regions, A–G; subsequently, high and low FUI values were observed in the south and north, respectively. Additionally, the mean FUI values of G and F zones in the south were 11.9 and 12.7, respectively, whereas those for the A, B, C, D, and E zones in the north were 10.5, 9.8, 10.4, 11.1, 11.2, respectively. The seasonal variations in the Baiyangdian Lake and seven sub-regions were consistent, with turbid water in spring and autumn, and clear water in summer. Inter-annual variations analyses for 2016–2020 indicated that the zone of A became progressively turbid, whereas the B, C, D, E, F, and G zones exhibited slow and gradually decreasing trends. Our findings suggest that the overall water quality of Baiyangdian Lake may be better, which may be related to the governance policies of the region.

Phased Arrays for Radio Astronomy, Remote Sensing, and Satellite Communications

2018 ◽  
Author(s):  
Karl F. Warnick ◽  
Rob Maaskant ◽  
Marianna V. Ivashina ◽  
David B. Davidson ◽  
Brian D. Jeffs
Keyword(s):  
Remote Sensing ◽  
Radio Astronomy ◽  
Phased Arrays ◽  
Satellite Communications
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