Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique

Abstract

Several methods have been proposed in previous studies to map macroalgal blooms (MABs) in the Yellow Sea (YS) and East China Sea (ECS), yet some of the required spectral bands are not available on the new HJ-1 satellite sensors although they do provide optimal resolution and temporal coverage (30-m resolution with 2-day revisit frequency) for bloom mapping. In this study, an index of Virtual-Baseline Floating macroAlgae Height (VB-FAH) is proposed to use the green and red bands as the baseline to measure the height of the near-infrared (NIR) reflectance. Cross-sensor comparison with Landsat TM and ETM + data suggests that for several images evaluated here VB-FAH appears to be comparable to the previously proposed Floating Algae Index (FAI) even in the absence of a shortwave-infrared (SWIR) band. VB-FAH is applied to 30-m resolution TM and ETM + data for the YS during 1995-2006 and to HJ-1 data for the ECS during 2009-2014 to map MABs. Results show bloom history in the YS back to 1999 and early bloom occurrence in the ECS in winter and spring (e.g., February and March 2013). MABs are also found to extend to the ECS as far south as 26°N near Fujian Province and Taiwan, and as far east as the Kuroshio Current. These new findings provide important information for exploring the origins, causes, and consequences of MABs in the YS and ECS.

Long-term trend of Ulva prolifera blooms in the western Yellow Sea

“…9). Moreover, some small amount of macroalgae Ulva may drift and sink in other regions, e.g., in the East China Sea and the eastern part of the southern Yellow Sea (Hu et al, 2010;Xing et al, 2011;Son et al, 2015;Xing and Hu, 2016), thus reducing the bloom scale in the area of this study.…”

“…Numerous studies have documented their growth cycles, seasonality, transport patterns, and their linkage with local aquaculture, eutrophication, and ocean circulations (Hu et al, 2010;Liu et al, 2009Liu et al, , 2013Wang et al, 2015). However, to date, although a number of studies used remote sensing to estimate their spatial distributions and temporal changes, nearly all focused on how to effectively detect the presence of the macroalgae (Cui et al, 2012;Hu, 2009;Hu et al, 2010;Keesing et al, 2011;Lee et al, 2011;Garcia et al, 2013;Bao et al, 2015;Xing and Hu, 2016). Several studies used individual images to show the maximum coverage during the bloom season or the maximum impacted water area to show the severity (Keesing et al, 2011;Hu et al, 2010;Garcia et al, 2013;Xu et al, 2014;Xing et al, 2015a,b).…”

Using Landsat 8 data to estimate suspended particulate matter in the Yellow River estuary

“…The improved capabilities of Landsat 8 Operational Land Imager (L8/ OLI) data, including an additional shorter blue band, a narrower near-infrared band, a 12 bit radiometric resolution, and higher signal-to-noise ratios, may improve capacities to monitor high turbid waters [Olmanson et al, 2016]. L8 data have recently been used to estimate Secchi-disk depths, sediment plumes, colored dissolved organic matter, water clarity levels, total suspended matter, and macroalgal blooms with considerable success [Barnes et al, 2015;Concha and Schott, 2016;Lee et al, 2016;Lymburner et al, 2016;Olmanson et al, 2016;Slonecker et al, 2016;Xing and Hu, 2016]. However, to our knowledge, SPM estimations from L8/ OLI have been seldom applied for the Yellow River estuary despite their evident potential.…”

Validation of MODIS-based monitoring for a green tide in the Yellow Sea with the aid of unmanned aerial vehicle

“…20 The MODIS images can provide timely information on the location and area of green tide twice a day with coverage over the entire Yellow Sea, especially when floating biomass increased rapidly after mid-May. 1 Furthermore, floating macroalgae blooms in the Yellow Sea can be traced back to 1999, 36 the year MODIS Terra was launched. Hence, MODIS data can provide a long time series of information for macroalgae blooms, which could help to understand the occurrence mechanisms and influence factors as well as creating management plans.…”