Global Change
Global Changes

Ocean-color remote sensing is the only effective way in which to study large-scale and long-term changes in the marine carbon cycle. So far, ocean-color remote sensing has accumulated high-quality, consecutive data (1998–2017) of global phytoplankton, including biomass (characterization of chlorophyll concentration), primary productivity, and photosynthetically available radiation. The sequence of microwave remote sensing data can also provide information on a variety of environmental factors, such as wind field, making it possible to study changes in the ocean carbon cycle and its responses to climate change on a global scale.
Using long-term remote sensing data, studies have investigated the responses of open-ocean phytoplankton biomass and primary productivity to global changes: with global warming, both phytoplankton biomass and primary productivity of open oceans have generally declined (Polovina et al., 2008). Phytoplankton particle size has also shown a decreasing tendency. On the other hand, changes in phytoplankton in marginal seas differ significantly from those in the open ocean and even exhibit different phases (Yu et al., 2019, submitted). However, research on the changes in phytoplankton in marginal seas is still limited to regional areas, with a lack of systematic understanding at the global scale. Moreover, it is unclear how global phytoplankton functional groups change under global warming. In addition, the regulation mechanisms of phytoplankton changes in marginal seas are complex. Changes in SST may not fully explain the changes in phytoplankton. Under multiple environmental factors, the main regulators of changes in phytoplankton in marginal seas are not yet clear. Thus, it is necessary to comprehensively consider control factors, such as illumination, wind field, aerosol, and land-source input.
Based on analysis of 14-year (1998–2011) satellite data, we systematically studied the spatiotemporal distribution and changes in phytoplankton algal blooms in the East China Sea and analyzed the impact of factors such as the Yangtze River diluted water and climate change index on algal blooms (He et al., 2013). Using satellite-based remote sensing data, we found that a typhoon event could cause a short-term reversal of the coastal currents in Fujian and Zhejiang, transporting terrigenous materials across the Taiwan Strait to the southern part of the Okinawa Trough, and converging with the waters along the coast of Taiwan, leading to algae blooms off northeastern Taiwan (He et al., 2014). Through research on two extreme algal bloom events in the Bay of Bengal, we comprehensively analyzed the effects of multi-scale forced events, including short-scale events (typhoon, vortex), seasonal-scale events (wind field, flow field), and short-period climate oscillations (ENSO, IOD), on algal blooms (Chen et al., 2013). In addition, the effects of solar ultraviolet radiation on remote sensing inversion of marine primary productivity were systematically analyzed (Li et al., 2015).
We also quantitatively analyzed the conserved relationship between SSS of the Yangtze River estuary and East China Sea shelf and the absorption coefficient of colored dissolved organic matter and established an SSS remote sensing inversion model for the Yangtze River diluted water (Bai et al., 2013). Based on this model, we studied the distribution and diffusion patterns of the Yangtze River diluted water in the summers of 1998–2010, as well as the quantitative relationship between the diluted water diffusion area and Yangtze River runoff and the effects of wind speed and climatic factors on freshwater changes (Bai et al., 2014). By quantitatively considering the effects of mixing and phytoplankton carbon sequestration, we established a semi-analytical remote sensing algorithm for CO2 partial pressure in the East China Sea under the influence of the Yangtze River diluted water (Bai et al., 2015). In addition, via satellite remote sensing data, we found that in some years (e.g., 2008, 2009), the Pearl River diluted water spread eastward to the Penghu waterway, followed by transportation north to the Taiwan Strait, causing significant algal blooms (Bai et al., 2015).
Furthermore, we established a globally applicable seawater transparency semi-analytical remote sensing model (He et al., 2016). Based on this model, we analyzed the global ocean transparency trends from 1998 to 2010 and found an increasing trend in the Northern Hemisphere, but a decreasing trend in the Southern Hemisphere, consistent with the trend in temperature changes.

Representative articles
Bai, Y., He, X., Pan, D., Chen, C. T. A., Kang, Y., Chen, X., & Cai, W. J. (2015). Summertime Changjiang River plume variation during 1998–2010. Journal of Geophysical Research Oceans, 119(9), 6238-6257.
Chen, C., Mao, Z., Tang, F., Han, G., & Jiang, Y. (2017). Declining riverine sediment input impact on spring phytoplankton bloom off the Yangtze River Estuary from 17-year satellite observation. Continental Shelf Research, 135, 86-91.
Chen, J., Pan, D., Liu, M., Mao, Z., Zhu, Q., Chen, N., et al. (2017). Relationships Between Long-Term Trend of Satellite-Derived Chlorophyll-a and Hypoxia Off the Changjiang Estuary. Estuaries & Coasts, 40(4), 1-11.
Chen, X., Pan, D., Bai, Y., He, X., Chen, C. T. A., & Hao, Z. (2013). Episodic phytoplankton bloom events in the Bay of Bengal triggered by multiple forcings. Deep Sea Research Part I Oceanographic Research Papers, 73(3), 17-30.
Chen, X., Pan, D., Xianqiang, H. E., Bai, Y., & Wang, D. (2012). Upper ocean responses to category 5 typhoon Megi in the western north Pacific. Acta Oceanologica Sinica, 31(1), 51-58.
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.
He, X., Pan, D., Bai, Y., Wang, T., Chen, C.-T. A., Zhu, Q., et al. (2017). Recent changes of global ocean transparency observed by SeaWiFS. Continental Shelf Research, 143, 159-166.