Wave‐induced light field fluctuations in measured irradiance depth profiles: A wavelet analysis

Wei, Jianwei; Lewis, Marlon R.; Dommelen, Ronnie Van; Zappa, Christopher J.; Twardowski, Michael

doi:10.1002/2013jc009572

Cited by 15 publications

(11 citation statements)

References 49 publications

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“… E d profiles were processed following the recommendations in the NASA protocols (NASA, ). In order to remove cloud effects, the normalized E d (Ê d (λ, z)) was estimated as follows:

{\overset{normalE}{true}}_{normald} true(λ, normalz true) = \frac{{normalE}_{normald} (|, λ, normalz, {normalt}_{normalz}) \cdot {normalE}_{normalS} (|, λ, {normalt}_{normali})}{{normalE}_{normalS} (|, λ, {normalt}_{normalz})}

where

E_{S}

corresponds to the downwelling irradiance measured above‐water, t i corresponds to the time when the profiling HyperPro radiometer is at the surface and

t_{z}

is the profiling HyperPro radiometer time at depth z.

{\overset{normalE}{true}}_{normald} true(λ, normalz true)

profiles were very noisy, especially in the first few meters, typically a result of the wave focusing effect (Wei et al, ; Zaneveld et al, ; Zibordi et al, ). The noise was uncorrelated between bands, profiles, and stations.…”

Section: Data Used For Trvis Evaluationmentioning

confidence: 99%

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Estimation of Transmittance of Solar Radiation in the Visible Domain Based on Remote Sensing: Evaluation of Models Using In Situ Data

et al. 2017

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The transmittance of solar radiation in the oceanic water column plays an important role in heat transfer and photosynthesis, with implications for the global carbon cycle, global circulation, and climate. Globally, the transmittance of solar radiation in the visible domain (∼400–700 nm) (TRVIS) through the water column, which determines the vertical distribution of visible light, has to be based on remote sensing products. There are models centered on chlorophyll‐a (Chl) concentration or Inherent Optical Properties (IOPs) as both can be derived from ocean color measurements. We present evaluations of both schemes with field data from clear oceanic and from coastal waters. Here five models were evaluated: (1) Morel and Antoine (1994) (MA94), (2) Ohlmann and Siegel (2000) (OS00), (3) Murtugudde et al. (2002) (MU02), (4) Manizza et al. (2005) (MA05), and (5) Lee et al. () (IOPs05), where the first four are Chl‐based and the last one is IOPs‐based, with all inputs derived from remote sensing reflectance. It is found that the best performing model is the IOPs05, with Unbiased Absolute Percent Difference (UAPD) ∼23%, while Chl‐based models show higher uncertainties (UAPD for MA94: ∼54%, OS00: ∼133%, MU02: ∼56%, and MA05: ∼39%). The IOPs‐based model was insensitive to the type of water, allowing it to be applied in most marine environments; whereas some of the Chl‐based models (MU02 and MA05) show much higher sensitivities in coastal turbid waters (higher Chl waters). These results highlight the applicablity of using IOPs products for such applications.

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“… E d profiles were processed following the recommendations in the NASA protocols (NASA, ). In order to remove cloud effects, the normalized E d (Ê d (λ, z)) was estimated as follows:

{\overset{normalE}{true}}_{normald} true(λ, normalz true) = \frac{{normalE}_{normald} (|, λ, normalz, {normalt}_{normalz}) \cdot {normalE}_{normalS} (|, λ, {normalt}_{normali})}{{normalE}_{normalS} (|, λ, {normalt}_{normalz})}

where

E_{S}

corresponds to the downwelling irradiance measured above‐water, t i corresponds to the time when the profiling HyperPro radiometer is at the surface and

t_{z}

is the profiling HyperPro radiometer time at depth z.

{\overset{normalE}{true}}_{normald} true(λ, normalz true)

Section: Data Used For Trvis Evaluationmentioning

confidence: 99%

“…3.Ê d k; z ð Þ profiles were very noisy, especially in the first few meters, typically a result of the wave focusing effect (Wei et al, 2014;Zaneveld et al, 2001;Zibordi et al, 2004). The noise was uncorrelated between bands, profiles, and stations.…”

Section: Processing Of Hyperspectral Profiling Measurements For Tr Vismentioning

confidence: 99%

Estimation of Transmittance of Solar Radiation in the Visible Domain Based on Remote Sensing: Evaluation of Models Using In Situ Data

et al. 2017

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“…Not surprisingly, the discrepancies are larger than that of the simulated data, which is mainly due to errors or uncertainties in the measured T VSR , where impacts from clouds cannot be completely removed. In particular, wave focusing or defocusing can cause large uncertainties in E d ( z ) (Stramski & Legendre, ; Wei et al, ), which will then impact the calculated T VSR from in situ measurements. Note that these median rt values also include impacts resulting from uncertainties or errors in the measured or R rs ‐derived Z SD (for the APO data set) where a 15% deeper Z SD can result in ~30% increase in rt for T VSR at 10%.…”

Section: Resultsmentioning

confidence: 99%

Estimating the Transmittance of Visible Solar Radiation in the Upper Ocean Using Secchi Disk Observations

Lee

Shang

et al. 2019

JGR Oceans

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Penetration of visible solar radiation (VSR) drives heating and phytoplankton photosynthesis in the upper water column; thus, it is always important to accurately describe the vertical distribution of VSR in the oceans. Before the invention and application of modern optical‐electronic instruments to measure the vertical profiles of VSR, the transmittance of VSR from surface to deeper ocean (TVSR) was commonly estimated based on water types and subsequently incorporated in dynamic ocean circulation models. However, the measurement of Secchi disk depth (ZSD) has been carried out since the 1860s and there are about a million of ZSD data available for the global oceans. Because ZSD represents a measure of water's transparency, here we present a scheme based on radiative transfer to accurately estimate TVSR with ZSD as the sole input. It is found that the median ratios between modeled and measured TVSR are ~0.8–1.0 for TVSR in a range of 1–100% for measurements made in coastal waters and oceanic gyres. However, this median ratio spans ~0.04–1.0 for the same measurements when the classical water‐type‐based model was applied. These results suggest a great advantage, and potentially significant impact, in incorporating the volumetric ZSD data to model the dynamic ocean–atmosphere systems in the past 100+ years.

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“…The uncertainty for the particulate absorption coefficient measurements can sometimes be very large (Neeley et al, ). The in situ R rs measurements are also subject to uncertainties originating from the calibration, environmental disturbance, postprocessing, and so forth (Wei et al, ; Zibordi et al, ). Although difficult to determine, these measurement uncertainties have certainty affected the validation results.…”

Section: Discussionmentioning

confidence: 99%

Semianalytical Derivation of Phytoplankton, CDOM, and Detritus Absorption Coefficients From the Landsat 8/OLI Reflectance in Coastal Waters

et al. 2019

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The Operational Land Imager (OLI) onboard Landsat 8 has potential for mapping the water bio‐optical properties with high spatial resolution. Landsat 8/OLI generates the remote sensing reflectance (Rrs) at four visible bands (λ1‐4 = 443, 482, 561, and 655 nm) and is lack of a 412‐nm band commonly included for ocean color sensors. This spectral configuration has limited the use of Landsat 8/OLI reflectance product for analytical derivation of light absorption coefficients of phytoplankton, colored dissolved organic matter (CDOM), and detritus. In this study, we proposed a hybrid approach to fill this gap. First, we developed an algorithm to estimate the reflectance in a virtual band centered at λ0 = 412 nm from the OLI reflectance spectra Rrs(λ1‐4). Both the estimated Rrs(λ0) and measured Rrs(λ1‐4) were then used together to retrieve the water component absorption coefficients with existing algorithms including the quasi‐analytical algorithm. We assessed the model performance using in situ measurements from the global waters. It was found that the proposed approach could estimate Rrs(412) with a median absolute percentage difference of ~9%. The subsequent retrievals of the component absorption coefficients were satisfactorily accurate, with median absolute percentage difference roughly equal to 35%, 40%, and 60% for phytoplankton, CDOM, and detritus, respectively. The results suggest the feasibility to generate analytically the component absorption coefficients from the Landsat 8/OLI reflectance.

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Wave‐induced light field fluctuations in measured irradiance depth profiles: A wavelet analysis

Cited by 15 publications

References 49 publications

Estimation of Transmittance of Solar Radiation in the Visible Domain Based on Remote Sensing: Evaluation of Models Using In Situ Data

Estimation of Transmittance of Solar Radiation in the Visible Domain Based on Remote Sensing: Evaluation of Models Using In Situ Data

Estimating the Transmittance of Visible Solar Radiation in the Upper Ocean Using Secchi Disk Observations

Semianalytical Derivation of Phytoplankton, CDOM, and Detritus Absorption Coefficients From the Landsat 8/OLI Reflectance in Coastal Waters

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