scholarly journals A remote-sensing reflectance model of a red-tide dinoflagellate off west Florida1

1985 ◽  
Vol 30 (2) ◽  
pp. 286-298 ◽  
Author(s):  
K. L. Carder ◽  
R. G. Steward
Keyword(s):  
Remote Sensing ◽  
Red Tide ◽  
Remote Sensing Reflectance ◽  
Reflectance Model

Comparison of optical characteristics in non-red tide and red tide areas in the South Sea of Korea

2020 ◽  
Author(s):  
Eunkyung Lee ◽  
Jeong-Eon Moon ◽  
Young-Je Park ◽  
Tai-Hyun Han
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Korean Peninsula ◽  
Analytical Methods ◽  
Field Data ◽  
Red Tide ◽  
Optical Characteristics ◽  
Cochlodinium Polykrikoides ◽  
Southern Coast ◽  
Remote Sensing Reflectance

<p>Red tide, which occurs off the southern coast of the Korean Peninsula, is a maritime phenomenon that usually occurs between June and September every year, mostly by Cochlodinium polykrikoides single species. There are very few studies using the analytical methods of the inherent and apparent optical properties for these red tide. Ahn et al.(2009) analyzed the inherent optical properties of 26 species of red tide organisms occurring off the southern coast of the Korean Peninsula. Kim et al.(2016) distinguished the optical characteristics for Cochlodinium polykrikoides using field data and Hydrolight simulator. Using these analytical methods, we will understand the ocean optical properties of red tide and use the remote sensing reflectance simulator in the future to produce the input data necessary for developing the red tide analysis technology based on machine learning. Therefore, in this study, as an initial analysis, we will compare the in-situ data of red tide and non-red tide waters off the southern coast of the Korean Peninsula in September 2014 and August 2017 to identify differences in the spectral form and compare the ability of the remote sensing reflectance spectrum with the field data using a remote sensing reflectance simulator.</p>

scholarly journals Performance Evaluation of Four Ocean Reflectance Model

2021 ◽  
Vol 13 (14) ◽  
pp. 2748
Author(s):  
Jun Li ◽  
Tongji Li ◽  
Qingjun Song ◽  
Chaofei Ma
Keyword(s):  
Remote Sensing ◽  
Sea Surface ◽  
Concentration Data ◽  
In Situ Data ◽  
Factors Influencing ◽  
Remote Sensing Reflectance ◽  
Reflectance Model ◽  
Main Factors ◽  
And Performance

Phytoplankton are the main factors influencing light under the sea surface in Case Ι water. The ocean reflectance model (ORM), which takes into account the chlorophyll a concentration data, can calculate the remote sensing reflectance of Case Ι water. In this study, we examined the differences and performance of four ORMs, including Morel and Maritorena (2001, MM01), Morel and Gentili (2007, MG07), Mobley (2014, MO14), and Hydrolight Abcase1 Lookup Tables. The differences between the four ORMs in terms of their absorption and backscattering coefficients were evaluated. Preformation of the four ORMs was compared using the NASA bio-Optical Marine Algorithm Dataset and in situ data from the South China Sea. The results showed that preformation of MM01 was the best.

scholarly journals Monitoring Changes in the Transparency of the Largest Reservoir in Eastern China in the Past Decade, 2013–2020

2021 ◽  
Vol 13 (13) ◽  
pp. 2570
Author(s):  
Teng Li ◽  
Bozhong Zhu ◽  
Fei Cao ◽  
Hao Sun ◽  
Xianqiang He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mean Squared Error ◽  
Eastern China ◽  
Water Environment ◽  
Relative Difference ◽  
Boreal Summer ◽  
Support Vector ◽  
Lake Area ◽  
The Past ◽  
Remote Sensing Reflectance

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.

scholarly journals Meta-classification of remote sensing reflectance to estimate trophic status of inland and nearshore waters

2021 ◽  
Vol 176 ◽  
pp. 109-126
Author(s):  
Mortimer Werther ◽  
Evangelos Spyrakos ◽  
Stefan G.H. Simis ◽  
Daniel Odermatt ◽  
Kerstin Stelzer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Trophic Status ◽  
Remote Sensing Reflectance

scholarly journals Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

2021 ◽  
Vol 13 (2) ◽  
pp. 184
Author(s):  
Rongjie Liu ◽  
Jie Zhang ◽  
Tingwei Cui ◽  
Haocheng Yu
Keyword(s):  
Remote Sensing ◽  
Indian Ocean ◽  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Spatiotemporal Pattern ◽  
Anthropogenic Aerosols ◽  
Remote Sensing Reflectance ◽  
The Indian Ocean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

Spectral interdependence of remote-sensing reflectance and its implications on the design of ocean color satellite sensors

2014 ◽  
Vol 53 (15) ◽  
pp. 3301 ◽  
Author(s):  
Zhongping Lee ◽  
Shaoling Shang ◽  
Chuanmin Hu ◽  
Giuseppe Zibordi
Keyword(s):  
Remote Sensing ◽  
Ocean Color ◽  
Remote Sensing Reflectance ◽  
Satellite Sensors
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