Global Estimates of Marine Gross Primary Production Based on Machine Learning Upscaling of Field Observations

Huang, Yibin; Nicholson, David P.; Huang, Bangqin; Cassar, Nicolas

doi:10.1029/2020gb006718

Cited by 34 publications

(21 citation statements)

References 101 publications

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“…Machine learning (ML) methods are rapidly gaining interest across the geosciences for their adaptability and ability to capture complex relationships without prior knowledge of underpinning mechanisms (Li et al, 2016;Mattei et al, 2018;Rafter et al, 2019;Tang et al, 2019;Wang et al, 2020;Chen et al, 2020;Huang et al, 2021). In addition to being used to evaluate biogeochemical models, they can also be used to guide sampling strategies, and infer mechanisms through statistical inferences (Li and Cassar, 2016;Roshan and DeVries, 2017;Mattei et al, 2018;Chen et al, 2020;Huang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Dissolved Iron in the Global Ocean

2022

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The importance of dissolved Fe (dFe) in regulating ocean primary production and the carbon cycle is well established. However, the large-scale distribution and temporal dynamics of dFe remain poorly constrained in part due to incomplete observational coverage. In this study, we use a compilation of published dFe observations (n=32,344) with paired environmental predictors from contemporaneous satellite observations and reanalysis products to build a data-driven surface-to-seafloor dFe climatology with 1°×1° resolution using three machine-learning approaches (random forest, supper vector machine and artificial neural network). Among the three approaches, random forest achieves the highest accuracy with overall R2 and root mean standard error of 0.8 and 0.3 nmol L-1, respectively. Using this data-driven climatology, we explore the possible mechanisms governing the dFe distribution at various depth horizons using statistical metrics such as Pearson correlation coefficients and the rank of predictors importance in the model construction. Our results are consistent with the critical role of aeolian iron supply in enriching surface dFe in the low latitude regions and suggest a far-reaching impact of this source at depth. Away from the surface layer, the strong correlation between dFe and apparent oxygen utilization implies that a combination of regeneration, scavenging and large-scale ocean circulation are controlling the interior distribution of dFe, with hydrothermal inputs important in some regions. Finally, our data-driven dFe climatology can be used as an alternative reference to evaluate the performance of ocean biogeochemical models. Overall, the new global scale climatology of dFe achieved in our study is an important step toward improved representation of dFe in the contemporary ocean and may also be used to guide future sampling strategies.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Dissolved Iron in the Global Ocean

2022

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“…Several studies [3,57,58] demonstrated that upscaling the field GPP through machine learning methods was also a good way to make the field GPP comparable with remote sensing products. However, the uncertainties and error transfers in the upscaling process are still not very clear [59].…”

Section: Scale Mismatching Between Satellte Gpp and Ground Observed Gppmentioning

confidence: 99%

Spatial Representativeness of Gross Primary Productivity from Carbon Flux Sites in the Heihe River Basin, China

Zhang

Sun

2021

Remote Sensing

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Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R2 = 0.56, RMSE = 3.07 g C m−2 d−1) than at field scale (R2 = 0.51, RMSE = 3.34 g C m−2 d−1), and the same situation occurred in the validation of high-resolution downscaled GPP (R2 = 0.85, RMSE = 1.34 g C m−2 d−1 when validated at footprint scale; R2 = 0.82, RMSE = 1.47 g C m−2 d−1 when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale.

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“…Algae are a heterogenous group of photosynthetic, oxygen-producing, mostly aquatic organisms, that lack the complexity of a typical plant structure (leaves, stem, roots and complex reproductive structures). These simple organisms are critically important; it is estimated that 50-80% of the oxygen produced on Earth originates from algae and about half of the global total annual productivity is carried out by algae in the oceans [1,2]. Furthermore, algae influence climate by controlling processes such as biogenic calcification, oceanic sequestration of CO 2 and release of dimethylsulfide [3], along with the production of a wide array of natural compounds that play a primary role in ecosystem functioning and influence the environment and surrounding organisms [4], thus fulfilling very important ecosystem services.…”

Section: General Introduction To Algaementioning

confidence: 99%

Halogens in Seaweeds: Biological and Environmental Significance

et al. 2022

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Many marine algae are strong accumulators of halogens. Commercial iodine production started by burning seaweeds in the 19th century. The high iodine content of certain seaweeds has potential pharmaceutical and nutritional applications. While the metabolism of iodine in brown algae is linked to oxidative metabolism, with iodide serving the function of an inorganic antioxidant protecting the cell and thallus surface against reactive oxygen species with implications for atmospheric and marine chemistry, rather little is known about the regulation and homoeostasis of other halogens in seaweeds in general and the ecological and biological role of marine algal halogenated metabolites (except for organohalogen secondary metabolites). The present review covers these areas, including the significance of seaweed-derived halogens and of halogens in general in the context of human diet and physiology. Furthermore, the understanding of interactions between halogenated compound production by algae and the environment, including anthropogenic impacts, effects on the ozone layer and global climate change, is reviewed together with the production of halogenated natural products by seaweeds and the potential of seaweeds as bioindicators for halogen radionuclides.

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Global Estimates of Marine Gross Primary Production Based on Machine Learning Upscaling of Field Observations

Cited by 34 publications

References 101 publications

Data-Driven Modeling of Dissolved Iron in the Global Ocean

Data-Driven Modeling of Dissolved Iron in the Global Ocean

Spatial Representativeness of Gross Primary Productivity from Carbon Flux Sites in the Heihe River Basin, China

Halogens in Seaweeds: Biological and Environmental Significance

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