Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

Li, Wenzhao; Tiwari, Surya Prakash; Qurban, Mohammad A.; Amiridis, Vassilis; Manikandan, Karuppasamy P.; Garay, M. J.; Калашникова, О. В.; Piechota, Thomas C.; Struppa, Daniele C.

doi:10.1109/tgrs.2020.2990373

Cited by 12 publications

(6 citation statements)

References 94 publications

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“…The seasonal pattern of NPP in the Mediterranean Sea is considered to be generally high in winter and low in summer, consistent with the algal biomass in the west and the influence of photosynthetically active radiation (PAR) in the east (Bosc et al, 2004). In the Red Sea, NPP is regionally distinct and regulated by environmental factors such as sea surface temperature (SST), mixed layer depth (MLD), and PAR (Li W et al, 2020). Nutrient supply has been shown to be the main regulator of NPP in eastern boundary upwelling regions (Messieá nd Chavez, 2015).…”

Section: Introductionmentioning

confidence: 92%

Seasonal variability of satellite-derived primary production in the South China Sea from an absorption-based model

Wang²,

Xu³

et al. 2023

Front. Mar. Sci.

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Seasonal patterns of marine net primary production (NPP) are crucial for understanding the marine ecosystem and biogeochemical cycles. Uncoupling of seasonal variations between NPP and phytoplankton chlorophyll-a over different areas has attracted much attention. In this study, following a review of previous studies, monthly climatological NPP data from 2003 to 2020, estimated using the Size-fractioned Phytoplankton Pigment Absorption (aph)-based NPP Model (SABPM), were selected to study the seasonal variability of NPP in the South China Sea (SCS). Results showed the spatial differences of NPP seasonality and its departures from climatology in extreme El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) years. Cluster analysis for climatological monthly data identified significant differences of NPP seasonality in five typical regions. In coastal regions along the northern SCS and off eastern Vietnam, NPP exhibited the most obvious seasonal cycle with maximum (minimum) values in summer (winter), attributable mainly to river discharge and summer upwelling. In regions off northwestern Luzon and coast of southern SCS, NPP showed peaks in winter, which were related to strong mixing and upwelling. In northwestern SCS, NPP was high during May–September in phase with sea surface temperature and the primary controlling factors were found to be shallow nutricline depth and wind-driven mixing. Owing to the deep nutricline depth in the central basin, NPP exhibited little seasonal variability; only a weak signal was observed in spring in phase with photosynthetically active radiation. Local dynamics on regulating the nutrient supply and light availability contribute to these regional differences in NPP seasonality, which could also be affected by extreme climate events. The largest anomalies of the NPP seasonal cycle coincide with 2015/2016 ENSO and super IOD in 2020. During these events, enhanced (weakened) westerly winds caused fall (rise) of SLA and increase (decrease) of NPP in coastal regions along the northern SCS and that off eastern Vietnam. Overall, the aph-based model shows a new perspective to study the spatiotemporal variations of NPP in the SCS.

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Section: Introductionmentioning

confidence: 92%

Seasonal variability of satellite-derived primary production in the South China Sea from an absorption-based model

Wang²,

Xu³

et al. 2023

Front. Mar. Sci.

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“…Performance assessment of the P b opt , DIPP and DRPP models was done based on the most common statistical metrics such as mean relative error (MRE), Pearson-correlation coefficient (PCC), mean absolute error (MAE), root mean square error (RMSE) and mean net bias (MNB) [33], [61]. These metrics are defined as…”

Section: Performance Assessmentmentioning

confidence: 99%

“…These models are classified based on the complexity and integration level with wavelength, time and depth. According to the input parameters, the satellite-based NPP models are classified as: i) chlorophyllbased models, ii) carbon-based models, and iii) phytoplankton absorption-based models [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Depth-Resolved and Depth-Integrated Primary Productivity Estimates From In-Situ and Satellite Data in the Global Ocean

Sundararaman

Shanmugam

2023

IEEE Access

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Estimates of marine phytoplankton primary productivity (PP) from satellite remote sensing observations are potentially used to assess global carbon budgets, biogeochemical response, pools and fluxes of carbon and its spatial and temporal variations due to ocean-atmospheric oscillations under climate change. According to the recent studies, satellite-based vertically integrated global PP products have significant uncertainties due to the limitations of the past models, challenges in deriving the appropriate parameters that account for the variation of PP with seasons and provinces, and specify the vertical structure of phytoplankton biomass from satellite observation data and scarcity of in-situ vertical profile data. To overcome these issues, we developed a depth-resolved and depth-integrated model to estimate PP for global oceanic waters. It comprises the depth-resolved primary productivity (DRPP) and satellitebased depth-integrated primary productivity (DIPP) parameterizations to accurately estimate the magnitude and variability of PP in the global ocean. These parameterization algorithms require knowledge of the relative chlorophyll-specific carbon fixation rate (P b rel ) and maximum chlorophyll-specific carbon fixation rate within the water-column P b opt in order to derive the spatial and temporal patterns of DRPP and DIPP. To estimate the chlorophyll-specific maximum rate of carbon fixation at a depth equal to z (P b z ), two different P b rel algorithms were developed based on the relative values of i) the subsurface photosynthetically available radiation (PAR rel ) and ii) the optical depth at depth z (ζ z ). Furthermore, a sensitivity analysis was conducted to understand the effect of sea-surface temperature (SST), sea-surface chlorophyll concentration (SCHL) and sea-surface photosynthetically available radiation (SPAR) on the photo-physiological parameter P b opt . These physical, biological and optical parameters were used to obtain accurate P b opt estimates. The model based on the SST-SCHL-SPAR (P b opt (SSTCP)) produced more accurate P b opt estimates than the global Vertically Generalised Productivity Model (P b opt (VGPM )). Comparison of the model results with in-situ measurement data demonstrated that the ζ z -based DRPP algorithm (DRPP(ζ z )) yields more accurate results than the PAR rel -based DRPP algorithm (DRPP(PAR rel )). This study also investigates the spatial and temporal patterns in MODIS-Aqua-derived P b opt and DIPP products and the impacts of climate-driven perturbations on the global ocean PP due to the La Niña and El Niño phenomenon during 2010 and 2015. INDEX TERMS Primary productivity (PP), maximum carbon fixation rate, depth-resolved PP, depthintegrated PP, ocean color remote sensing, global ocean.

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“…Pearson correlation analysis was used to measures the statistical relationship between PP and chl-a, SST ONI, MEI, and further to evaluate the impact of ENSO on the distribution and variability of PP. Pearson correlation coefficient (𝑟) in the range from −1 (anti-correlation) to +1 (perfect correlation), between these two monthly time series x and y, with N elements as equation 3 [9]:…”

Section: Correlation Analysismentioning

confidence: 99%

“…Traditional shipbased in situ measurements are limited in their ability to capture PP large scale spatial and temporal dynamics and are time-consuming and expensive [7,8]. While remote sensing data can use to observe the dynamics of the ocean surface, providing fundamental means for estimating oceanic PP on large spatiotemporal scales [9]. One of the essential applications of ocean colour data has been in the PP computation at large scales, using remotely sensed fields of phytoplankton biomass [4].…”

Section: Introductionmentioning

confidence: 99%

Assessing the distribution and variation characteristics of marine primary productivity in the coastal marine area of Vietnam South Centre

Hieu

Huan

Thi

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Primary production (PP) of phytoplankton plays an essential role in food web dynamics, biogeochemical cycles and marine fisheries. It is used as one of the basic information for evaluating marine ecosystems. In this paper, monthly composite PP data on a 4 km x 4 km grid for the period 2003-2020 was used to evaluate the distributional characteristics of PP in the coastal marine area of Vietnam South Centre. The statistical results show that the climatological average of PP in 18 years reached 449.2 mgC/m2/day, ranged from 272.1 to 14,205.4 mgC/m2/day. The PP has seasonal and spatial variations. In time, the lowest value of PP was in spring, and the highest was in winter; in space, PP tended to decrease from shore to offshore, PP was higher in coastal areas than in the open sea areas. During the northeast monsoon season, PP increased by more than 1000 mgC/m2/day in the coastal area. Meanwhile, in the southwest monsoon season, due to the ecological influence of the upwelling phenomenon, PP increased with a value greater than 1500 mgC/m2/day, distributed along the coastline of Ninh Thuan - Binh Thuan. Primary productivity positively correlated with chlorophyll content but negatively correlated with sea surface temperature with correlation coefficients of 0.9 and -0.6, respectively. There was a weak correlation between PP and ONI with correlation coefficients of -0.23. The temporal-spatial variation of PP was affected by the ENSO (El Niño-Southern Oscillation) phenomenon, the positive phase of ENSO (El Niño conditions) corresponded to lower PP, and the negative phase of ENSO (La Niña conditions) corresponded to higher PP. The research results from this paper can be used as a reference in marine ecosystem management.

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Synergistic Use of Remote Sensing and Modeling for Estimating Net Primary Productivity in the Red Sea With VGPM, Eppley-VGPM, and CbPM Models Intercomparison

Cited by 12 publications

References 94 publications

Seasonal variability of satellite-derived primary production in the South China Sea from an absorption-based model

Seasonal variability of satellite-derived primary production in the South China Sea from an absorption-based model

Depth-Resolved and Depth-Integrated Primary Productivity Estimates From In-Situ and Satellite Data in the Global Ocean

Assessing the distribution and variation characteristics of marine primary productivity in the coastal marine area of Vietnam South Centre

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