Chlorophyll‐Based Model to Estimate Underwater Photosynthetically Available Radiation for Modeling, <i>In‐Situ</i>, and Remote‐Sensing Applications

Xing, Xiaogang; Boss, Emmanuel

doi:10.1029/2020gl092189

Cited by 13 publications

(15 citation statements)

References 35 publications

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“…These profiles have proved to be fruitful measurements for diverse applications. Downwelling irradiances at various wavelengths have been implied in the analysis of the bio-optical behavior of the global ocean [ 4 ] and the dynamics of dissolved organic matter [ 5 , 6 ], and for the validation of space-based ocean color measurements and products [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Besides, E d and PAR have been widely used to understand particulate organic carbon fluxes and export [ 14 , 15 , 16 ], to study phytoplankton dynamics [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], and to improve numerical and radiative-transfer models [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Correction of Biogeochemical-Argo Radiometry for Sensor Temperature-Dependence and Drift: Protocols for a Delayed-Mode Quality Control

Jutard¹,

Organelli

Briggs

et al. 2021

Sensors

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Measuring the underwater light field is a key mission of the international Biogeochemical-Argo program. Since 2012, 0–250 dbar profiles of downwelling irradiance at 380, 412 and 490 nm besides photosynthetically available radiation (PAR) have been acquired across the globe every 1 to 10 days. The resulting unprecedented amount of radiometric data has been previously quality-controlled for real-time distribution and ocean optics applications, yet some issues affecting the accuracy of measurements at depth have been identified such as changes in sensor dark responsiveness to ambient temperature, with time and according to the material used to build the instrument components. Here, we propose a quality-control procedure to solve these sensor issues to make Argo radiometry data available for delayed-mode distribution, with associated error estimation. The presented protocol requires the acquisition of ancillary radiometric measurements at the 1000 dbar parking depth and night-time profiles. A test on >10000 profiles from across the world revealed a quality-control success rate >90% for each band. The procedure shows similar performance in re-qualifying low radiometry values across diverse oceanic regions. We finally recommend, for future deployments, acquiring daily 1000 dbar measurements and one night profile per year, preferably during moonless nights and when the temperature range between the surface and 1000 dbar is the largest.

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Section: Introductionmentioning

confidence: 99%

Correction of Biogeochemical-Argo Radiometry for Sensor Temperature-Dependence and Drift: Protocols for a Delayed-Mode Quality Control

Jutard¹,

Organelli

Briggs

et al. 2021

Sensors

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“…The second NAAMES cruise took place during the climax of the spring bloom when the phytoplankton community was dominated by eukaryotic phytoplankton, and small diatoms were prevalent at the two stations where the NPP estimates were most improved (Bolaños et al 2020). Model estimates were typically higher than 14 C estimates of NPP, which could be due to subsurface light levels being overestimated because of an underestimate in K d (Xing and Boss 2021).…”

Section: Modeled Primary Production Across Diverse Ecosystemsmentioning

confidence: 86%

An absorption‐based approach to improved estimates of phytoplankton biomass and net primary production

Fox

Kramer

Graff

et al. 2022

Limnol Oceanogr Letters

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The abundance and productivity of phytoplankton constrain energy transfer through marine food webs and the export of organic carbon to the deep ocean. Bio‐optical measurements correlate well with phytoplankton carbon (Cphyto), but the effect of taxonomic variability on this relationship is still uncertain. Here, we explore how changes in phytoplankton community composition influence the relationship between the particulate backscatter coefficient (bbp) and Cphyto and present a new approach to estimate phytoplankton biomass more accurately using bbp. We found that using a fixed scaling factor for the conversion of bbp to Cphyto could lead to the underestimation or overestimation of biomass, depending on the dominant taxonomic group in the phytoplankton community. In addition, we demonstrate how a simple ratio of absorption at two wavelengths can be used to provide a coarse approximation of phytoplankton community composition when scaling bbp to Cphyto, thereby improving the estimation of net primary production.

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“…We have assumed a homogeneous distribution of IOPs below the ocean surface, which may cause poor performance of the NN models in waters with deep chlorophyll maxima (DCM) such as in Subtropical Gyre and Mediterranean Sea. This is true for other semi-analytical models as well [70]. The models are built for a clear sky, and the effect of cloud fractions in the IPAR calculation will be a subject of future studies.…”

Section: B Discussionmentioning

confidence: 98%

Instantaneous photosynthetically available radiation models for ocean waters using neural networks

Aryal¹,

Zhai²,

Gao

et al. 2022

Appl. Opt.

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Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the surface play a critical role in models to calculate net primary productivity of marine phytoplankton. In this work, we report two IPAR prediction models based on the neural network (NN) approach, one for open ocean and the other for coastal waters. These models are trained, validated, and tested using a large volume of synthetic datasets for open ocean and coastal waters simulated by a radiative transfer model. Our NN models are designed to predict IPAR under a large range of atmospheric and oceanic conditions. The NN models can compute the subsurface IPAR profile very accurately up to the euphotic zone depth. The root mean square errors associated with the diffuse attenuation coefficient of IPAR are less than 0.011 m − 1 and 0.036 m − 1 for open ocean and coastal waters, respectively. The performance of the NN models is better than presently available semi-analytical models, with significant superiority in coastal waters.

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Chlorophyll‐Based Model to Estimate Underwater Photosynthetically Available Radiation for Modeling, In‐Situ, and Remote‐Sensing Applications

Cited by 13 publications

References 35 publications

Correction of Biogeochemical-Argo Radiometry for Sensor Temperature-Dependence and Drift: Protocols for a Delayed-Mode Quality Control

Correction of Biogeochemical-Argo Radiometry for Sensor Temperature-Dependence and Drift: Protocols for a Delayed-Mode Quality Control

An absorption‐based approach to improved estimates of phytoplankton biomass and net primary production

Instantaneous photosynthetically available radiation models for ocean waters using neural networks

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