Spectral modes of radiometric measurements in optically complex waters

Hooker, Stanford B.; Houskeeper, Henry F.; Kudela, Raphael M.; Matsuoka, Atsushi; Suzuki, Koji; Isada, Tomonori

doi:10.1016/j.csr.2021.104357

Cited by 10 publications

(27 citation statements)

References 35 publications

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“…For example, the two-channel L W (λ) N algorithm based on Houskeeper et al [9] using 320 and 780 nm produced a MAD value of 41.4%, compared to 58.8% for the best performing one-channel L W (λ) N algorithm evaluated herein, which was based on L W (320) N . The two-channel L W (λ) N algorithm was applicable to global waters, i.e., algorithm log-scale residuals were approximately uniform across the global range in a CDOM (440), but the single-channel L W (λ) N algorithms were significantly degraded in higher a CDOM (440) waters, which is consistent with the findings of Hooker et al [20], in which L W (λ) N was most sensitive to increasing optical complexity within the UV and NIR domains.…”

Section: One-versus Two-band Algorithms and Wavelength Selectionsupporting

confidence: 87%

“…The opposing trends in dispersion are likely due to

being more sensitive to wave focusing in clear waters and

being more sensitive to broad-spectrum brightness effects (e.g., due to an elevated nonalgal particle concentration) in turbid waters. As optical complexity increases, upwelled radiant flux at the shortest wavelengths is often significantly modified, so small absolute differences in the magnitude of derived data products lead to large dispersions or variance [ 20 ]. Both Houskeeper et al [ 9 ] and Hooker et al [ 13 ] found that the correlation between

and above- or in-water data products, respectively, was the most significant at the spectral end members.…”

Section: Resultsmentioning

confidence: 99%

“…For example, a water mass within line-of-sight proximity of the marginal ice zone (MIZ), but far from it such that MIZ meltwater has only a small influence on water properties associated with the surrounding parent water body [13], is considered near-conservative. The influence of uncertainty sources that are absent from conservative or near-conservative water masses is provided in Hooker et al [13,20].…”

Section: Methodsmentioning

confidence: 99%

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One- and Two-Band Sensors and Algorithms to Derive aCDOM(440) from Global Above- and In-Water Optical Observations

Hooker

Houskeeper

Lind

et al. 2021

Sensors

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The colored (or chromophoric, depending on the literature) dissolved organic matter (CDOM) spectral absorption coefficient, aCDOM(λ), is a variable of global interest that has broad application in the study of biogeochemical processes. Within the funding for scientific research, there is an overarching trend towards increasing the scale of observations both temporally and spatially, while simultaneously reducing the cost per sample, driving a systemic shift towards autonomous sensors and observations. Legacy aCDOM(λ) measurement techniques can be cost-prohibitive and do not lend themselves toward autonomous systems. Spectrally rich datasets carefully collected with advanced optical systems in diverse locations that span a global range of water bodies, in conjunction with appropriate quality assurance and processing, allow for the analysis of methods and algorithms to estimate aCDOM(440) from spectrally constrained one- and two-band subsets of the data. The resulting algorithms were evaluated with respect to established fit-for-purpose criteria as well as quality assured archival data. Existing and proposed optical sensors capable of exploiting the algorithms and intended for autonomous platforms are identified and discussed. One-band in-water algorithms and two-band above-water algorithms showed the most promise for practical use (accuracy of 3.0% and 6.5%, respectively), with the latter demonstrated for an airborne dataset.

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Section: One-versus Two-band Algorithms and Wavelength Selectionsupporting

confidence: 87%

“…The opposing trends in dispersion are likely due to

being more sensitive to wave focusing in clear waters and

and above- or in-water data products, respectively, was the most significant at the spectral end members.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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One- and Two-Band Sensors and Algorithms to Derive aCDOM(440) from Global Above- and In-Water Optical Observations

Hooker

Houskeeper

Lind

et al. 2021

Sensors

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“…Because the parameters of Chl a concentration and Z SD are often used as indicators of eutrophication and turbidity in inland, coastal, and oceanic waters, many previous studies (e.g., Lewis et al, 1988;Falkowski and Wilson, 1992;Morel et al, 2007;Boyce et al, 2010Boyce et al, , 2012Doron et al, 2011) have used the empirical relationship between Z SD and Chl a concentrations to investigate phytoplankton productivity and develop algorithms for satellite ocean color remote sensing, especially in open oceans with optically simple Case I water, where phytoplankton is the primary factor affecting the IOPs of a water body (Morel and Prieur, 1977;Prieur and Sathyendranath, 1981;IOCCG, 2000). However, the variations of IOPs in optically complex coastal waters (Case II water) interacting with terrestrial waters are determined by phytoplankton, NAP, and CDOM (IOCCG, 2000;Hooker et al, 2020Hooker et al, , 2021. Although many empirical conversion factors have been reported for predicting the diffuse attenuation coefficient of the photosynthetically available radiation (PAR) (K PAR , m −1 ), which is an apparent optical property (AOP) that varies with the sun angle (Preisendorfer, 1986), there is no universal conversion factor because Z SD is affected by the bulk optical properties, especially in Case II waters (Lee et al, 2018 and references therein).…”

Section: Implications For Ecosystem Models In Optically Complex Coastal Watersmentioning

confidence: 99%

Dynamics of Nutrients and Colored Dissolved Organic Matter Absorption in a Wetland-Influenced Subarctic Coastal Region of Northeastern Japan: Contributions From Mariculture and Eelgrass Meadows

et al. 2021

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Coastal oceans interacting with terrestrial ecosystems play an important role in biogeochemical cycles. It is therefore essential to research land–ocean interactions for further understanding of the processes influencing nutrients dynamics in coastal areas. We investigated the seasonal and spatial distribution of nutrient concentrations and light absorption coefficients of colored dissolved organic matter (CDOM), non-algal particles (NAP), and phytoplankton in a wetland-influenced river–eelgrass meadows–coastal waters continuum in the protected and semi-enclosed coastal sea of Akkeshi-ko estuary (AKE) and Akkeshi Bay (AB), Japan from April 2014 to February 2015. The mixing dilution lines of the CDOM absorption coefficient at 355 nm [aCDOM(355)] relative to salinity predicted by two end-members between freshwater and coastal water showed conservative mixing in AB. Silicate concentrations were significantly correlated with salinity and aCDOM(355) in AB in each month except for December 2014. These results suggest that silicate and CDOM in AB primarily originates from wetland-influenced river discharge. However, samples collected from the eelgrass meadows of AKE, where mariculture is developed, showed non-conservative mixing of silicate concentrations and aCDOM(355) with salinity except for June 2014. Elevated phosphate concentrations, probably released from sediments, were also found in the eelgrass meadows of AKE, especially during summer. These results suggest that the metabolic activities of mariculture and seagrass ecosystem significantly contribute to the nutrient cycles and CDOM absorption in AKE and to the distinct water-mass systems inside and outside AKE. The relative absorption properties of NAP [aNAP(443)], phytoplankton [aph(443)], and aCDOM(443) showed that CDOM is the main factor affecting the light distribution in AKE. However, the relative absorption properties varied seasonally in AB because of spring and autumn phytoplankton blooms and ice cover during winter. Significant relationships were observed between the Secchi disk depth (ZSD), aNAP(443), and aCDOM(443). Chl a concentration and aph(443) were not good indicators for predicting ZSD in our study region. These results suggest that incorporating inherent optical properties and CDOM from mariculture and seagrass ecosystem into ecosystem models could improve predictions of light distribution along the freshwater–eelgrass–coastal waters continuum in optically complex coastal waters.

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“…Finally, we investigated regional relationships by aggregating kelp canopy data products to the nearest 1 km coastline segments. particles (e.g., through resuspension or terrestrial inputs) challenge atmospheric correction [40] and elevate signals in the NIR domain [41]. In some instances, nearshore remote sensing challenges are anticipated to be less problematic when spatial information is also considered (e.g., the trajectory of a runoff plume), and so the FF8 annotations were retained within the nearshore zone.…”

Section: Resultsmentioning

confidence: 99%

Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas)

Houskeeper

Rosenthal

Cavanaugh

et al. 2021

Preprint

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Giant kelp populations support productive and diverse coastal ecosystems in both hemispheres at temperate and subpolar latitudes but are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally patchy, with disproportionate coverage in the northern hemisphere, despite the size and comparable density of southern hemisphere kelp forests. Satellite imagery enables the mapping of existing and historical giant kelp populations in understudied regions, but automating the detection of giant kelp in large satellite datasets requires approaches that are robust to the optical complexity of the shallow, nearshore environment. We present and compare two approaches for automating the detection of giant kelp in satellite datasets: one based on crowd sourcing of satellite imagery classifications and another based on a decision tree paired with a spectral unmixing algorithm (automated using Google Earth Engine). Both approaches are applied to satellite imagery (Landsat) of the Falkland Islands or Islas Malvinas (FLK), an archipelago in the southern Atlantic Ocean that supports expansive giant kelp ecosystems. The performance of each method is evaluated by comparing the automated classifications with a subset of expert-annotated imagery cumulatively spanning over 2,700 km of coastline. Using the remote sensing approaches evaluated herein, we present the first continuous time series of giant kelp observations in the FLK region using Landsat imagery spanning over three decades. We do not detect evidence of long-term change in the FLK region, although we observe a recent decline in total canopy area from 2017-2021. Using a nitrate model based on nearby ocean state measurements obtained from ships and incorporating satellite sea surface temperature products, we find that the area of giant kelp forests in the FLK region is positively correlated with the nitrate content observed during the prior year. Our results indicate that giant kelp classifications using citizen science are approximately consistent with classifications based on a state-of-the-art automated spectral approach. Despite differences in accuracy and sensitivity, both approaches find high interannual variability that impedes the detection of potential long-term changes in giant kelp canopy area, although recent canopy area declines are notable and should continue to be monitored carefully.

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Spectral modes of radiometric measurements in optically complex waters

Cited by 10 publications

References 35 publications

One- and Two-Band Sensors and Algorithms to Derive aCDOM(440) from Global Above- and In-Water Optical Observations

One- and Two-Band Sensors and Algorithms to Derive aCDOM(440) from Global Above- and In-Water Optical Observations

Dynamics of Nutrients and Colored Dissolved Organic Matter Absorption in a Wetland-Influenced Subarctic Coastal Region of Northeastern Japan: Contributions From Mariculture and Eelgrass Meadows

Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas)

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