A blended inherent optical property algorithm for global satellite ocean color observations

Shi, Wei; Wang, Menghua

doi:10.1002/lom3.10320

Cited by 27 publications

(12 citation statements)

References 67 publications

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“…For these models, in-situ measurements are usually required for training and validating the RS models [27], [28]. The latter has a bio-geo-optical basis in which the relationship between WLR, inherent optical properties, and biochemical constituents are defined through the Radiative Transfer (RT) models [29], [31].…”

Section: Optical Rsmentioning

confidence: 99%

Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

Amani¹,

Ghorbanian

Asgarimehr

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Reliable, accurate, and timely information about oceans is important for many applications, including water resource management, hydrological cycle monitoring, environmental studies, agricultural and ecosystem health applications, economy, and the overall health of the environment. In this regard, Remote Sensing (RS) systems offer exceptional advantages for mapping and monitoring various oceanographic parameters with acceptable temporal and spatial resolutions over the oceans and coastal areas. So far, different methods have been developed to study oceans using various RS systems. This urges the necessity of having review studies that comprehensively discuss various RS systems, including passive and active sensors, and their advantages and limitations for ocean applications. In this paper, the goal is to review most remote sensing systems and approaches that have been worked on marine applications. This review paper is divided into two parts. Part 1 is dedicated to the passive RS systems for ocean studies. As such, four primary passive systems, including optical, Thermal Infrared (TIR) radiometers, microwave radiometers, and Global Navigation Satellite Systems (GNSS), are comprehensively discussed. Additionally, this paper summarizes the main passive RS sensors and satellites, which have been utilized for different oceanographic applications. Finally, various oceanographic parameters, which can be retrieved from the data acquired by passive RS systems, along with the corresponding methods, are discussed.

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Section: Optical Rsmentioning

confidence: 99%

Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

Amani¹,

Ghorbanian

Asgarimehr

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Segregation criteria helped us select the appropriate algorithm to produce accurate IOP products for both open-ocean and turbid coastal waters. It is to be noted that (Shi and Wang, 2019) used nL w (745) based classification criteria for open ocean and coastal waters. However, their discrimination approach resulted in high data noise for moderate coastal turbid waters and did not blend our region's coastal and estuarine optical properties.…”

Section: Conceptual Algorithm Developmentmentioning

confidence: 99%

“…The band-integrated above-water remote-sensing reflectance R rs (L,0 + ) from both datasets was then converted to underwater (just below the surface) remote sensing reflectance r rs (L, 0 -) following (Lee et al, 2002) as described in section 1 of Supplementary Information. r rs (L, 0 -) were processed using a blended QAA algorithm (Shi and Wang, 2019) to estimate total absorption a t (l) and backscattering coefficients b b (l) as the next step of the algorithm development (see Supplementary Information Section 2). The adopted blended algorithm uses a near-infrared (NIR)-based quasi-analytical IOP derivation for turbid coastal/inland waters and Lee's quasi-analytical algorithm (QAA-v6) (IOCCG, 2006) for the open-ocean and less turbid coastal waters.…”

Section: Conceptual Algorithm Developmentmentioning

confidence: 99%

“…Inherent optical properties (IOPs, absorption and scattering properties of seawater and its constituents) based vulnerability assessment of photic zone compression is performed in this study for SEA coastal turbid waters. Here, we first develop a new algorithm by extending the blended Quasi analytical algorithm (QAA) by (Shi and Wang, 2019) to derive the CDOM absorption coefficient using in situ bio-optical datasets acquired in the Singapore Strait and the synthesized IOCCG dataset. The proposed algorithm is then implemented on Sentinel-3a satellite ocean color observations to determine the spatial-seasonal dynamics of IOPs-CDOM absorption coefficient at 400 nm (a measure of CDOM concentration), particulate backscattering coefficient at 400 nm (a measure of suspended sediment concentration), and the CDOM source index (g 0, index to trace source of CDOM as terrestrial or marine) for SEA waters.…”

Section: Introductionmentioning

confidence: 99%

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Inherent Optical Properties based Vulnerability Assessment of Euphotic Zone Compression in peatland influenced Southeast Asian coastal waters

et al. 2022

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Underwater light availability is a crucial aspect for the ecological functioning of shallow water bodies. Light extinction from terrestrial inputs is a growing threat to these coastal habitats. The blended quasi-analytical algorithm (QAA) was extended for the derivation of colored dissolved organic matter (CDOM) absorption coefficient along with other inherent optical properties (IOPs) from satellite observations for Southeast Asian waters. The contribution of these IOPs to diffuse attenuation of light (Kd) and penetration depth (Zd) was investigated. A vulnerability assessment was performed to identify locations potentially threatened by poor light quality in Southeast Asian waters. Advection of peatland-influenced Sumatran coastal waters rich in organic matter (ag(400nm): 1.0-2.0m-1) and sediments (bbp(400nm): 0.5-1m-1) drive the spatial heterogeneity of Sunda shelf seawater. Photic zone depth, Zd(490nm), is year-round restricted to ≤5m for critically vulnerable Sumatran coastal waters (vulnerability index, VI>0.8). This critically vulnerable state is further extended towards the southern Malacca Strait, influencing the eastern Singapore Strait from June to September. The areas harbouring marine ecosystems in the shelf waters attain a higher threshold (VI=0.6-0.8), constraining the photosynthesis to depths ≤10m. A transformation of central Malacca Strait from not vulnerable (VI<0.2) to highly vulnerable (VI=0.6-0.8) state from June to September indicates poor light conditions. Further increases in CDOM and sediment inputs into these water columns, therefore, constitute a clear risk of reducing light availability, which may have damaging effects on the functioning of coastal habitats. This study underscores the need for a complete ecological risk assessment for Southeast Asia to aid in the effective management of marine ecosystems.

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“…Recent studies [42][43][44] show that the semi-analytical radiance model [20] can be simplified for the nL w (λ) at the NIR wavelengths because the sea water absorption a w (λ) is normally~1-2 orders higher than the other IOP components at the NIR wavelengths. Consequently, the b bp (λ) spectra for all wavelengths between the short blue and NIR can be computed analytically from b bp (λ) values at the two NIR bands (745 and 862 nm) in coastal and inland turbid waters [42].…”

Section: Introductionmentioning

confidence: 99%

Inherent Optical Properties in Lake Taihu Derived from VIIRS Satellite Observations

2019

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Using in situ remote sensing reflectance and inherent optical property (IOP) measurements, a near-infrared (NIR)-based IOP algorithm is developed and tuned for Lake Taihu, in order to derive the particle backscattering coefficient bbp(λ), total absorption coefficient at(λ), dissolved and detrital absorption coefficient adg(λ), and phytoplankton absorption coefficient aph(λ), with satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). The IOP algorithm for Lake Taihu has a reasonably good accuracy. In fact, the determination coefficients between the retrieved and in situ IOPs are 0.772, 0.638, and 0.487 for at(λ), adg(λ), and aph(λ), respectively. The IOP products in Lake Taihu that have been derived from VIIRS-SNPP observations show significant spatial and temporal variations. Southern Lake Taihu features enhanced bbp(λ) and adg(λ), while northern Lake Taihu shows higher aph(λ). The seasonal and interannual variability of adg(λ) and bbp(λ) in Lake Taihu is quantified and characterized with the highest bbp(λ) and adg(λ) in the winter, and the lowest in the summer. In the winter, bbp(443) and adg(443) can reach over ~1.5 and ~5.0 m−1, respectively, while they are ~0.5–1.0 and ~2.0 m−1 in the summer. This study shows that in Lake Taihu adg(λ) is the most significant IOP, while aph(λ) is the least in terms of the IOP values and contributions to remote sensing reflectance. The highest bbp(λ) and adg(λ) occurred in the winter between 2017–2018, and the lowest bbp(λ) and adg(λ) occurred in the summer of 2014. In comparison, the seasonal and interannual variability of mean aph(λ) for Lake Taihu is less significant, even though enhanced seasonal and interannual variability can be found in some parts of Lake Taihu, such as in the northern Lake Taihu region.

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A blended inherent optical property algorithm for global satellite ocean color observations

Cited by 27 publications

References 67 publications

Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

Inherent Optical Properties based Vulnerability Assessment of Euphotic Zone Compression in peatland influenced Southeast Asian coastal waters

Inherent Optical Properties in Lake Taihu Derived from VIIRS Satellite Observations

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