An empirical algorithm for determining the diffuse attenuation coefficientKdin clear and turbid waters from spectral remote sensing reflectance

Accurate modeling of spectral remote sensing reflectance (Rrs) is of great interest for ocean colour studies in highly turbid and relatively clear waters. In this work a semianalytical model that simulates the spectral curves of remote sensing reflectance of these waters is developed based on the inherent optical properties (IOPs) and f and Q factors. For accommodating differences in the optical properties of the water and accounting for their directional variations, IOPs and f and Q factors are derived as a function of phytoplankton pigments, suspended sediments and solar zenith angle. Results of this model are compared with in-situ bio-optical data collected at 83 stations encompassing highly turbid/relatively cleared waters of the South Sea of Korea. Measured and modeled remote sensing reflectances agree favorably in both magnitude and spectral shape, with considerably low errors (mean relative error MRE -0.0327; root mean square error RMSE 0.205, bias -0.0727 and slope 1.15 and correlation coefficient R2 0.74). These results suggest that the new model has the ability to reproduce measured reflectance values and has potentially profound implications for remote sensing of complex waters in this region.

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Modeling the remote-sensing reflectance of highly turbid waters

Applied Optics ◽

10.1364/ao.58.002671 ◽

2019 ◽

Vol 58 (10) ◽

pp. 2671

Author(s):

Joel Wong ◽

Soo Chin Liew ◽

Elizabeth Wong ◽

Zhongping Lee

Keyword(s):

Remote Sensing ◽

Turbid Waters ◽

Remote Sensing Reflectance

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

Remote Sensing ◽

10.3390/rs13132570 ◽

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.

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

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2021.04.003 ◽

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

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

Remote Sensing ◽

10.3390/rs13020184 ◽

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.

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