Global Marine Phytoplankton Carbon Sequestration Parameter Dataset - Marine Carbon Satellite Data

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Producer	Second Institute of Oceanography, MNR
Data Type	Remote Sensing (MODIS)
Data Abbreviation	CHL; SDD; NPP; POC
Time Range	CHL(1978-1986; 1997-2019) SDD(1998-2019) NPP(1981-1986; 1997-2019) POC(1998-2019)
Product Level	L3B
Spatial Coverage	Global
Spatial resolution	4 km
Temporal Resolution	8-day/Monthly
Reference Coordinate System	Equal Latitude-Longitude Projection

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Description

Marine chlorophyll (Chla): refers to the mass concentration of chlorophyll a in seawater, which can characterize the phytoplankton biomass in marine ecosystems. Phytoplankton convert atmospheric carbon dioxide into organic carbon through photosynthesis, recycling approximately 50 GT of carbon globally each year. Chlorophyll a concentration has been recognized as a fundamental climate variable by the Global Climate Observing System (GCOS, 2011), and its long-term series changes need to be monitored at a global scale with high temporal and spatial resolution to understand and predict how ecosystems respond to climate change. This study aims to construct accurate chlorophyll trend change information and adopts a new multi-satellite fusion algorithm to eliminate systematic deviations between remote sensors as much as possible and seamlessly connect data from different satellite remote sensors to generate a continuous and consistent time series. The study collected all mainstream ocean color remote sensor data, including L3-level chlorophyll data from CZCS, SeaWiFS, MERIS, MODIS/Aqua, VIIRS/SNPP and OLCI (Sentinel-3A), and produced a fusion product dataset of 8-day average and monthly average Chla from 1978-1986 and 1997-2020, constructing long-term trend changes with a spatial resolution of 4km. The unit of chlorophyll concentration is μg/L.

Seawater transparency (SDD): Seawater transparency directly reflects the transmission ability of light in the water body. Its changes are closely related to the material composition and concentration of the water body. It is an important basic parameter to characterize the water body ecological environment. In actual measurements, transparency is usually measured using a 30cm white disk (the Secchi disk), that is, under backlight, the transparency disk is sunk into the water to a depth just invisible to the naked eye, and then when it is faintly visible. Depth is the seawater transparency value in meters (m). This algorithm is derived through the theory of water body radiation transfer and the theory of underwater object visibility. The research team established a semi-analytic model of seawater transparency and water body inherent and apparent optical quantities. On this basis, we used CCI remote sensing products (443nm remote sensing reflectance, total water absorption coefficient and total backscattering coefficient) to invert and obtain global seawater transparency products. Compared with the traditional seawater transparency remote sensing inversion model (which is only determined by the intrinsic optical quantity or the apparent optical quantity), this algorithm model takes into account the modulation effect of the intrinsic optical quantity and the apparent optical quantity on the transparency at the same time, and is suitable for different environments around the world. The global ocean surface salinity 8-day average and monthly average remote sensing datasets provide global ocean transparency remote sensing products. The time span is 1998-2019, the spatial resolution is 4km, and the temporal resolution is an 8-day average.

Marine primary productivity (NPP): it refers to the ability of phytoplankton in seawater to synthesize organic carbon through photosynthesis. It is generally defined as the mass of organic carbon net synthesized by phytoplankton in the water column per unit time and unit area (mg C‧m-2‧day-1 ). This algorithm uses the VGPM model of Behrenfeld et al. (1997) to calculate global ocean primary productivity products. In order to improve the retrieval accuracy in turbid water bodies in marginal seas, the euphotic depth parameter based on chlorophyll concentration in VGPM is replaced by the euphotic depth calculated by the transparency model of He et al. (2017). This product uses CCI remote sensing products (443nm remote sensing reflectance, total water absorption coefficient, total backscattering coefficient and chlorophyll concentration), AVHRR’s multi-source fusion sea surface temperature (SST) product and Sea WiFS, MODIS sea surface photosynthetically active radiation (PAR) product, the inversion obtained the global ocean primary productivity product from 1997 to 2019. In addition, based on the chlorophyll concentration data of the first ocean color remote sensor CZCS from 1981 to 1986, combined with the model sea surface downward diffuse light field intensity data set, the global ocean primary productivity remote sensing product from 1981 to 1986 was produced, expanding the time range of the dataset. The global ocean primary productivity monthly average remote sensing dataset provides global ocean primary productivity remote sensing products. The time span is 1981-1986 and 1997-2019, the spatial resolution is 4km, and the temporal resolution is the monthly average.

Marine particulate organic carbon concentration (POC): refers to the concentration of particulate organic carbon in unit volume of seawater. It is a basic parameter that characterizes the current status and capacity of seawater organic carbon storage (mg ‧ m-3). This algorithm uses the following formula to calculate and obtain the remote sensing product of sea surface particulate organic carbon concentration: POC= 0.0089 × H2 - 6.256 × H + 1128.8 H= 777.79+22.43 × ln(adg443)+36.33 × ln(bbp665), where POC is sea surface Table POC concentration (mg ‧ m-3), adg443 and bbp665 respectively represent the absorption coefficient of debris and dissolved organic matter in the 443nm band (m-1) and the particle backscattering coefficient (m-1) at 665nm. The 8-day average and monthly average remote sensing dataset of global ocean particulate organic carbon concentration (1998-2019) provides remote sensing products of global ocean surface particulate organic carbon concentration. The time span is 1998-2019, the spatial resolution is 4km, and the temporal resolution is 8-day average and monthly average.

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Naming Convention

SIO_SAT_GOCF_YYYYMMDDTOYYYYMMDD_L3B_GLOBAL_4KM_CHL_YU2021

SIO_SAT_GOCF_YYYYMMDDTOYYYYMMDD_L3B_GLOBAL_4KM_SDD_HE2017

SIO_SAT_GOCF_YYYYMMDDTOYYYYMMDD_L3B_GLOBAL_4KM_NPP_HE2021

SIO_SAT_GOCF_YYYYMMDDTOYYYYMMDD_L3B_GLOBAL_4KM_POC _LI2021

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Related Website

The dataset is available for download at the National Earth System Science Data Center

(http://www.geodata.cn/thematicView/modislly.html?guid2=10341724097961)

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Reference

[1] He, X., Bai, Y., Pan, D., Chen, C.-T.A., Cheng, Q., Wang, D., Gong, F. (2013) Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998–2011). Biogeosciences, 10 (7): 4721-4739.

[2] Bai, Y., He, X., Yu, S., Chen , C.T.A. (2018) Changes in the Ecological Environment of the Marginal Seas along the Eurasian Continent from 2003 to 2014[J]. Sustainability, 10(3): 635.

[3] Xianqiang He, Delu Pan, Yan Bai, Tianyu Wang, Chen-Tung Arthur Chen, Qiankun Zhu, Zengzhou Hao, Fang Gong. Recent changes of global ocean transparency observed by SeaWiFS. Continental Shelf Research, 143: 159-166, 2017.

[4] Bai, Y., He, X., Yu, S., Chen , C.T.A. (2018) Changes in the Ecological Environment of the Marginal Seas along the Eurasian Continent from 2003 to 2014[J]. Sustainability, 10(3): 635.

[5] Li,T., Bai,Y., He, X., Chen, X., Chen, C.T.A., Tao, B., Pan, D., Zhang X. (2018) The relationship between POC export efficiency and primary production: opposite on the shelf and basin of the northern South China Sea. Sustainability, 10(10): 3635.

[6] Li, T., Bai, Y., He, X., Xie, Y., Chen, X., Gong, F., Pan, D. (2018) Satellite-Based Estimation of Particulate Organic Carbon Export in the Northern South China Sea. Journal of Geophysical Research: Oceans, 123(11): 8227-8246.

[7] Song, X., Bai, Y., Cai, W.J., Chen, C.T.A., Pan, D., He, X., Zhu, Q. (2017) Remote sensing of sea surface pCO2 in the Bering sea in summer based on a mechanistic semi-analytical algorithm (MeSAA). Remote Sensing, 8(7): 558.

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