Publications

Chen, SL; Meng, Y; Lin, S; Xi, JY (2022). Remote Sensing of the Seasonal and Interannual Variability of Surface Chlorophyll-a Concentration in the Northwest Pacific over the Past 23 Years (1997-2020). REMOTE SENSING, 14(21), 5611.

Abstract
Phytoplankton in the northwest Pacific plays an important role in absorbing atmospheric CO2 and promoting the ocean carbon cycle. However, our knowledge on the long-term interannual variabilities of the phytoplankton biomass in this region is quite limited. In this study, based on the Chlorophyll-a concentration (Chl-a) time series observed from ocean color satellites of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) in the period of 1997-2020, we investigated the variabilities of Chl-a on both seasonal and interannual scales, as well as the long-term trends. The phytoplankton Chl-a showed large spatial dynamics with a general decreasing pattern poleward. The seasonal phytoplankton blooms dominated the seasonal characteristics of Chl-a, with spring and fall blooms identified in subpolar waters and single spring blooms in subtropical seas. On interannual scales, we found a Chl-a increasing belt in the subpolar oceans from the marginal sea toward the northeast open ocean waters, with positive trends (similar to 0.02 mg m(-3) yr(-1), on average) in Chl-a at significant levels (p < 0.05). In the subtropical gyre, Chl-a showed slight but significant negative trends (i.e., <-0.0006 mg m(-3) yr(-1), at p < 0.05). The negative Chl-a trends in the subtropical waters tended to be driven by the surface warming, which could inhibit nutrient supplies from the subsurface and thus limit phytoplankton growth. For the subpolar waters, although the surface warming also prevailed over the study period, the in situ surface nitrate reservoir somehow showed significant increases in the targeted spots, indicating potential external nitrate supplies into the surface layer. We did not find significant connections between the Chl-a interannual variabilities and the climate indices in the study area. Environmental data with finer spatial and temporal resolutions will further constrain the findings.

DOI:
10.3390/rs14215611

ISSN:
2072-4292