Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

Uudeberg, Kristi; Aavaste, Age; Kõks, Kerttu-Liis; Ansper, Ave; Uusõue, Mirjam; Kangro, Kersti; Ansko, Ilmar; Ligi, Martin; Toming, Kaire; Reinart, Anu

doi:10.3390/rs12060931

Cited by 31 publications

(17 citation statements)

References 120 publications

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“…Such a difference in the shape of R(λ) spectra may have an effect on the use of algorithms to derive OSC concentrations found as a result of the classification of optical water types, where reflectance spectrum features are associated with a specific bio-optical condition. Such classification approaches are widely used in the remote sensing of water bodies (Vantrepotte et al, 2012;Shen et al, 2015;Jackson et al, 2017;Spyrakos et al, 2018;Uudeberg et al, 2019Uudeberg et al, , 2020. Based on the difference in the R(λ) spectrum shapes in the current study, it is suggested that the ratios between R(490) and R(560) values can be used as an indicator for the detection of upwelled water (in this work R(490)/R(560) ≥ 0.77).…”

Section: Discussionmentioning

confidence: 80%

Impact of Thermohaline Conditions on Vertical Variability of Optical Properties in the Gulf of Finland (Baltic Sea): Implications for Water Quality Remote Sensing

et al. 2021

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Vertical variability of inherent optical properties (IOPs) affect the water quality retrievals from remote sensing data. Here, we studied the vertical variability of IOPs and simulated apparent optical properties (AOPs) in the Gulf of Finland (Baltic Sea) under three characteristic (non)stratification conditions. In the case of mixed water column, the vertical variability of optically significant constituents (OSC) and IOPs was relatively small. While in case of stratified water column the IOPs of surface layer were three times higher compared to the IOPs below the thermocline and the IOPs were strongly correlated with the physical parameters (temperature, salinity). Measurements of IOPs in stratified water column showed that the ratio of scattering (b(440)) to absorption (a(440)) changed under the thermocline (b(440)/a(440) < 1) i.e., absorption became the dominant component of attenuation under thermocline while the opposite is true for the upper layer. Simulated (from IOPs) spectral irradiance reflectance (R(λ)) and spectral diffuse attenuation coefficient (Kd(λ)) from deeper layers (below thermocline) have significantly smaller magnitude and smoother shape. This becomes relevant during upwelling events—a common process in the coastal Baltic Sea. We quantified the effect of upwelling on surface water properties using simulated AOPs. The simulated AOPs (from IOPs measurements) showed a decrease of the signal up to 68.8% and an increase of optical depth (z90(λ)) from 2.3 to 4.3 m in the green part of the spectrum in case upwelled water mass reaches the surface. In the coastal waters a vertical decrease of Kd(λ) in the PAR region (400–700 nm) by 6.8% (surface to 20 m depth) was observed, while vertical decrease of chlorophyll-a (Chl-a) and total suspended matter (TSM) was 31.7 and 42.1%, respectively. The ratio R(490)/R(560)≥0.77 indicates also the upwelled water mass. The study showed that upwelling is a process that, in addition to biological activity, horizontal transport of OSC, and temperature changes, alters the optical signal of surface water measured by a remote sensor. Knowledge about the vertical variability of IOPs and AOPs relation to upwelling can help the parametrisation of remote sensing algorithms for retrieving water quality estimates in the coastal regions.

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Section: Discussionmentioning

confidence: 80%

Impact of Thermohaline Conditions on Vertical Variability of Optical Properties in the Gulf of Finland (Baltic Sea): Implications for Water Quality Remote Sensing

et al. 2021

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“…The total suspended particulate matter is composed of the inorganic part and organic part. Inorganic particulate matter has a high refractive index relative to water molecules, so the light underwater would be strongly scattered, but the optical properties of debris-like organic particulate matter are similar to CDOM [73]. CDOM can absorb the light underwater at the blue band and scatter at the yellow band, thus making the sea surface water present as yellow [74].…”

Section: Discussionmentioning

confidence: 99%

Variations of Water Transparency and Impact Factors in the Bohai and Yellow Seas from Satellite Observations

Zhou

Yang

et al. 2021

Remote Sensing

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Water transparency, measured with Secchi disk depth (SDD), is an important parameter for describing the optical properties of a water body. This study evaluates variations of SDD and related impact factors in the Bohai and Yellow Seas (BYS). Based on a new mechanistic model proposed by Lee et al. (2015) applied to MODIS remote sensing reflectance data, climatological SDD variation from 2003 to 2019 was estimated. The annual mean images showed an increasing trend from the coastal zone to the deep ocean. Lower values were found in the Bohai Sea (BHS), while higher values observed in the center of the southern Yellow Sea (SYS). Additionally, the entire sea has shown a decreasing temporal tend, with the variation rate lowest in the BHS at 0.003 m y−1, and highest in the SYS at 0.015 m y−1. However, the weak increasing trend that appeared since 2017 suggests that water quality seems to have improved. Further, it displayed seasonal patterns of low in winter and spring and high in summer and autumn. The empirical orthogonal function (EOF) analysis of SDD variations over the BYS, shows that the first SDD EOF mode is the highest, strongly correlated with total suspended matter. With the high correlation coefficients of chromophoric dissolved organic matter, it illustrates that the SDD variation is mainly dominated by the optical components in the seawater, although correlation with chlorophyll-a is the weakest. The second and third EOF modes show that photosynthetically available radiation, sea surface temperature, sea surface salinity, and wind speed are the main covariates that cause SDD changes. Water transparency evaluation on a long-term scale is essential for water quality monitoring and marine ecosystem protection.

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“…In addition, the values of both ∠CAO and ∠CAW are between 30°and 60°, respectively [31]. By setting the observation angle in this way, the influence of direct sunlight and reflection could be minimized [32,33], and the collected spectra could be avoided from being saturated [34,35].…”

Section: E Analysis Of Instrument Observation Plane and Fiber Optic Probe Pointingmentioning

confidence: 99%

Design of a Novel Fully Automatic Ocean Spectra Acquisition and Control System Based on the Real-Time Solar Angle Analyzing and Tracking

Yang

Gao

et al. 2021

IEEE Access

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The current manual spectra acquisition for monitoring water constituents has resulted in discontinuous data acquisition, insufficient amount of data, and small ocean coverage. This paper presents the design of a novel fully automatic ocean spectra acquisition and control system based on the real-time solar angle analyzing and tracking. To ensure that the requirements for spectra acquisition are met, the system is capable of accurately calculating the solar angle by collecting the information of latitude, longitude, date, time and direction, and automatically adjusting the position of instrument observation plane and the pointing angle of fiber optic probe in real-time. It achieves full automation of collecting the downward radiance of skylight, the upward radiance from reference panel and seawater separately through controlling the rotation of fiber optic probe. A 188-day observation experiment was carried out at the coastal ocean experimental station in Qingdao from September 11, 2018 to March 17, 2019. After that, the system was conducted onboard the Dongfanghong 3 scientific research vessel for a one-month demonstration and sea trial in June 2019. Comparative experiments including manual spectra collection, chlorophyll-a sensor measurement, and water samples collection were carried out. The experimental results show that the relative error of the spectra between the system and manual collection is less than 5%, and the relative error of the remote sensing reflectance calculated by the spectra is less than 4%. Considering the chlorophyll-a concentration obtained by the sensor and the water samples as the true value, the relative error of the chlorophyll-a concentration obtained by the system is 10% and 25% respectively. The results show its full and reliable capacity in collecting spectra of seawater automatically and continuously in real-time, with satisfactory accuracy and timeliness.

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Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

Cited by 31 publications

References 120 publications

Impact of Thermohaline Conditions on Vertical Variability of Optical Properties in the Gulf of Finland (Baltic Sea): Implications for Water Quality Remote Sensing

Impact of Thermohaline Conditions on Vertical Variability of Optical Properties in the Gulf of Finland (Baltic Sea): Implications for Water Quality Remote Sensing

Variations of Water Transparency and Impact Factors in the Bohai and Yellow Seas from Satellite Observations

Design of a Novel Fully Automatic Ocean Spectra Acquisition and Control System Based on the Real-Time Solar Angle Analyzing and Tracking

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