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55. Remote Sensing of Global Sea Surface pH Based on Massive Underway Data and Machine Learning

Author

Jiang, Zhiting, Song, Zigeng, Bai, Yan, He, Xianqiang, Yu, Shujie, Zhang, Siqi, Gong, Fang, Jiang, Zhiting, Song, Zigeng, Bai, Yan, He, Xianqiang, Yu, Shujie, Zhang, Siqi, and Gong, Fang

Abstract

Seawater pH is a direct proxy of ocean acidification, and monitoring the global pH distribution and long-term series changes is critical to understanding the changes and responses of the marine ecology and environment under climate change. Owing to the lack of sufficient global-scale pH data and the complex relationship between seawater pH and related environmental variables, generating time-series products of satellite-derived global sea surface pH poses a great challenge. In this study, we solved the problem of the lack of sufficient data for pH algorithm development by using the massive underway sea surface carbon dioxide partial pressure (pCO(2)) dataset to structure a large data volume of near in situ pH based on carbonate calculation between underway pCO(2) and calculated total alkalinity from sea surface salinity and relevant parameters. The remote sensing inversion model of pH was then constructed through this massive pH training dataset and machine learning methods. After several tests of machine learning methods and groups of input parameters, we chose the random forest model with longitude, latitude, sea surface temperature (SST), chlorophyll a (Chla), and Mixed layer depth (MLD) as model inputs with the best performance of correlation coefficient (R-2 = 0.96) and root mean squared error (RMSE = 0.008) in the training set and R-2 = 0.83 (RMSE = 0.017) in the testing set. The sensitivity analysis of the error variation induced by the uncertainty of SST and Chla (SST <= +/- 0.5 degrees C and Chla <= +/- 20%; RMSESST <= 0.011 and RMSEChla <= 0.009) indicated that our sea surface pH model had good robustness. Monthly average global sea surface pH products from 2004 to 2019 with a spatial resolution of 0.25 degrees x 0.25 degrees were produced based on the satellite-derived SST and Chla products and modeled MLD dataset. The pH model and products were validated using another independent station-measured pH dataset from the Global Ocean Data Analysis Project (GL

Published
2022
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61. Temperature and Secchi Disk Depth Increase More Rapidly in the Subpolar Bering/Okhotsk Seas Than in the Subtropical South China Sea.

Author

Chen, Chen-Tung, Yu, Shujie, Huang, Ting-Hsuan, Bai, Yan, He, Xianqiang, and Lui, Hon-Kit

Subjects
OCEAN temperature, BODIES of water, CHLOROPHYLL, NORTH Atlantic oscillation
Abstract

Like other high-latitude seas, the subpolar Bering and Okhotsk Seas in the northernmost Pacific Ocean changed rapidly from 1998 to 2018. The sea surface temperature (SST) increased by 0.62 and 0.41 °C/decade, respectively, much higher than the global rate of 0.108 °C/decade from 2000 until 2015. Despite this rapid warming, the chlorophyll content did not change significantly in the Bering Sea but increased by 0.047 μg/L/decade in the Okhotsk Sea. The Secchi disk depth (SDD) increased by 0.43 and 0.46 m/decade, respectively. Similar to other warm bodies of water, the SST of the subtropical/tropical South China Sea (SCS) also began rising, by 0.089 °C/decade, albeit more slowly than the global average. The chlorophyll content increased at 0.15 μg/L/decade from 1998 to 2006 but decreased by 0.11 μg/L/decade between 2007 and 2018. The SDD increased by 0.29 m/decade between 1998 and 2018. Although the SDD increased in all three seas, the chlorophyll concentration was maximum around 2006–2009, reflecting different phytoplankton responses to seawater warming. [ABSTRACT FROM AUTHOR]

Published
2023
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69. Construction of a High Spatiotemporal Resolution Dataset of Satellite-Derived pCO2 and Air–Sea CO2 Flux in the South China Sea (2003–2019)

Author

Song, Zigeng, Yu, Shujie, Bai, Yan, Guo, Xianghui, He, Xianqiang, Zhai, Weidong, and Dai, Minhan

Abstract

The South China Sea (SCS) is one of the largest marginal seas in the world. It includes a river-dominated, highly productive ocean margin on the northern shelf and an oligotrophic ocean-dominated basin along with other subregions with various features. It was a challenge to estimate the air–sea CO2 flux in this area. We developed a retrieval algorithm for sea surface partial pressure of CO2 ( ${p}$ CO2) by a combination of our previously established mechanistic semianalytical method (MeSAA) and machine learning (ML) method, named MeSAA-ML-SCS, built upon a large dataset of sea surface ${p}$ CO2 collected from in situ measurements during 44 cruises /legs to the SCS in the last two decades. We set several semianalytical parameters, including ${p}$ CO2_therm represented the combined effect of thermodynamics and the atmospheric CO2 forcing on seawater ${p}$ CO 2; upwelling index (UISST) and mixing layer depth (MLD) to characterize the mixing processes; and chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm [Rrs(443) and Rrs(555)], which were proxies of biological effects and other characteristics for distinguishing shelf, basin, and subregions. We set the difference between seawater ${p}$ CO2 and atmospheric ${p}$ CO2( $\boldsymbol {\Delta p}\mathbf {C}\mathbf {O}_{\mathbf {2}}^{\mathbf {Sea{-}Air}}$ ) as the output, and the seawater ${p}$ CO2 was finally obtained by summing atmospheric ${p}$ CO2 and $\boldsymbol {\Delta p}\mathbf {C}\mathbf {O}_{\mathbf {2}}^{\mathbf {Sea{-}Air}}$ . We compared several ML models, and the XGBoost model was confirmed as the best. Independent cruise-based datasets that are not involved in the model training were used to validate the satellite products, with low root-mean-square error (RMSE = $11.69~\mu $ atm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of time-series satellite-derived ${p}$ CO2 (2.44 ± $0.24~\mu$ atm/year) was validated by the in situ data at the Southeastern Asia Time-series Study (SEATS) station, showing good consistency. Results indicate that the SCS as a whole is a source of atmospheric CO2, releasing an average of 12.34 ± 3.11 Tg C/year from a total area of $2.87\times106$ km2, while the northern shelf acts as a sink (2.02 ± 0.64 Tg C/year). With the forcing of increasing atmospheric CO2, the area-integrated CO2 efflux over the entire SCS is decreasing with a rate of 0.41 Tg C/year during 2003–2019. This shared long time series, high-accuracy dataset (1 km) can be helpful to further improve our understanding of the air–sea CO2 exchange dynamics in the SCS.

Published
2023
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99. GW25-e4460 Diagnostic accuracy of 320-slice dynamic volume computed tomography angiography for detection of coronary artery stenosis compared with selective coronary angiography

Author

Liu Dinghui, Qian Xiaoxian, Liu Yong, Yu Shujie, Hao Baoshun, and Liu Xuelian

Subjects
Coronary angiography, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Angiography, Medicine, Diagnostic accuracy, Coronary stenosis, Radiology, Volume Computed Tomography, business, Cardiology and Cardiovascular Medicine
Published
2014
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