Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties

Bernardo, Nariane; Alcântara, Enner; Watanabe, Fernanda; Rodrigues, Thanan; Carmo, Alisson Fernando Coelho do; Gomes, Ana Carolina da Silva França; Andrade, Caroline Piffer de

doi:10.3390/rs10101655

Cited by 11 publications

(8 citation statements)

References 62 publications

(119 reference statements)

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“…As the Ibitinga Reservoir is located in the middle portion of Tietê River, it receives hydrological inputs such as non-treated water and surface runoffs from the metropolitan region of São Paulo city ( Bernardo et al 2018 , 2019b ). Its transparency is affected by organic and inorganic waste and pollutants from agricultural, urban and industrial activities in the watershed; the reservoir is characterized as a turbulent and eutrophic reservoir ( Rotta et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

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Remote sensing of water transparency variability in the Ibitinga reservoir during COVID-19 lockdown

Contador

Alcântara

Rodrigues

et al. 2021

Remote Sensing Applications: Society and Environment

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As of October 8th , 2020, the number of confirmed cases and deaths in Brazil due to COVID-19 hit 5,002,357 and 148,304, respectively, making the country one of the most affected by the pandemic. The State of São Paulo (SSP) hosts the largest number of confirmed cases in Brazil, with over 1,016,755 cases to date. This study was carried out to investigate how the social distancing measures could have influenced the Ibitinga reservoir's water transparency in São Paulo State, Brazil. We hypothesize that although the city's drainage is the major reservoir's input, as opposed to what has been reported elsewhere, the effect of extensive lockdown in the city of São Paulo due to COVID-19 is marginal on the water transparency. A time series of OLI/Landsat-8 images since 2014 were used to estimate the Secchi Disk Depth (Z SD ). The COVID-19 cases and deaths (per 100,000 inhabitants), and social isolation index were used to find links between the Z SD and COVID-19. The results showed that the highest Z DS (higher than 1.6 m) occurred during the dry season (Austral autumn and beginning of Austral winter) and the lowest (0.4–0.8 m) during March 2020 (end of Austral summer). Paired sample t -Tests between images of 2020 and all the others showed that April 20th values were not different from that of June 14th, April 17th and March 18th. Z SD values from May 20th were not statistically different from May 14th and April 15th; June 20th values were not different from June 14th; and March 20th values were statistically different from all. We therefore conclude that, based on satellite data, the lockdown in SSP unlikely have influenced the water transparency in the Ibitinga reservoir.

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Section: Discussionmentioning

confidence: 99%

“…Radiometric data and water quality parameters data were measured during the fieldwork from 19–23 July 2016 (thirty samples), such as depth, Secchi disk depth, turbidity, pH, chlorophyll-a and reflectance. Details about the data collection protocol and processing can be accessed in Bernardo et al (2018 ; 2019b) and Andrade et al (2018) .…”

Section: Methodsmentioning

confidence: 99%

Remote sensing of water transparency variability in the Ibitinga reservoir during COVID-19 lockdown

Contador

Alcântara

Rodrigues

et al. 2021

Remote Sensing Applications: Society and Environment

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“…Band combination in NR-2B and Mer-3B could decrease this variability introducing from surface-reflected light (Figure 11c). In addition, it was demonstrated that there is no general value of to be adopted in different inland water conditions, but the most suitable methodology is the spectral , e.g., approach in Lee et al (2010) [69]. However, spectral variability of was not taken into consideration in the process of deriving field-measured R rs (λ), which should be improved in the further studies.…”

Section: Discussionmentioning

confidence: 99%

Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

Xue

Wang

et al. 2019

Remote Sensing

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Optical water types (OWTs) were identified from remote sensing reflectance (Rrs(λ)) values in a field-measured dataset of several large lakes in the lower reaches of the Yangtze and Huai River (LYHR) Basin. Four OWTs were determined from normalized remote sensing reflectance spectra (NRrs(λ)) using the k-means clustering approach, and were identified in the Sentinel 3A OLCI (Ocean Land Color Instrument) image data over lakes in the LYHR Basin. The results showed that 1) Each OWT is associated with different bio-optical properties, such as the concentration of chlorophyll-a (Chla), suspended particulate matter (SPM), proportion of suspended particulate inorganic matter (SPIM), and absorption coefficient of each component. One optical water type showed an obvious characteristic with a high contribution of mineral particles, while one type was mostly determined by a high content of phytoplankton. The other types belonged to the optically mixed water types. 2) Class-specific Chla inversion algorithms performed better for all water types, except type 4, compared to the overall dataset. In addition, class-specific inversion algorithms for estimating the Chla-specific absorption coefficient of phytoplankton at 443 nm (a*ph(443)) were developed based on the relationship between a*ph(443) and Chla of each OWT. The spatial variations in the class-specific model-derived a*ph(443) values were illustrated for 2 March 2017, and 24 October 2017. 3) The dominant water type and the Shannon index (H) were used to characterize the optical variability or similarity of the lakes in the LYHR Basin using cloud-free OLCI images in 2017. A high optical variation was located in the western and southern parts of Lake Taihu, the southern part of Lake Hongze, Lake Chaohu, and several small lakes near the Yangtze River, while the northern part of Lake Hongze had a low optical diversity. This work demonstrates the potential and necessity of optical classification in estimating bio-optical parameters using class-specific inversion algorithms and monitoring of the optical variations in optically complex and dynamic lake waters.

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“…Measured radiance and irradiance (at 3.3 nm resolution) were interpolated to 1 nm resolution [38,39]. The R rs variable was calculated using the spectral glint removal method [40], which is considered the most reliable [41]. We measured the particulate absorbance using a dual-beam UV-2600 UV-VIS spectrophotometer (SHIMADZU, Japan) at 1 nm resolution, over 280 to 800 nm.…”

Section: Fieldsite and Datasetmentioning

confidence: 99%

Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme

et al. 2019

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Suspended particulate matter (SPM) directly affects the underwater light field and, as a consequence, changes the water clarity and can reduce the primary production. Remote sensing-based bio-optical modeling can provide efficient monitoring of the spatiotemporal dynamics of SPM in inland waters. In this paper, we present a novel and robust bio-optical model to retrieve SPM concentrations for inland waters with widely differing optical properties (the Tietê River Cascade System (TRCS) in Brazil). In this system, high levels of Chl-a concentration of up to 700 mg/m3, turbidity up to 80 NTU and high CDOM absorption highly complicate the optical characteristics of the surface water, imposing an additional challenge in retrieving SPM concentration. Since Kd is not susceptible to the saturation issue encountered when using remote sensing reflectance (Rrs), we estimate SPM concentrations via Kd. Kd was derived analytically from inherent optical properties (IOPs) retrieved through a re-parameterized quasi-analytical algorithm (QAA) that yields relevant accuracy. Our model improved the estimates of the IOPs by up to 30% when compared to other existing QAAs. Our developed bio-optical model using Kd(655) was capable of describing 74% of SPM variations in the TRCS, with average error consistently lower than 30%.

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Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties

Cited by 11 publications

References 62 publications

Remote sensing of water transparency variability in the Ibitinga reservoir during COVID-19 lockdown

Remote sensing of water transparency variability in the Ibitinga reservoir during COVID-19 lockdown

Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

Retrieval of Suspended Particulate Matter in Inland Waters with Widely Differing Optical Properties Using a Semi-Analytical Scheme

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