JIANGBinbin FANDaidu JIQingyuanand OBODOEFUNA Doris Chigozie

Dynamic Diurnal Changes in Green Algae Biomass in the Southern Yellow Sea Based on GOCI Images

JIANGBinbin1),3), FANDaidu1),*, JIQingyuan2),and OBODOEFUNA Doris Chigozie1)

1),,200092,2),,NE3 1QD,3),,324000,

Macroalgae blooms ofhave occurred every summer in the southern Yellow Sea since 2007, inducing severe ecological problems and huge economic losses. Genesis and secular movement of green algae blooms have been well monitored by using remote sensing and other methods. In this study, green algae were detected and traced by using Geostationary Ocean Color Imager (GOCI), and a novel biomass estimation model was developed from the relationship between biomass measurements and previously published satellite-derived biomass indexes. The results show that the green algae biomass can be determined most accurately with the biomass index of green algae for GOCI (BIGAG), which is calculated from the Rsurf data that had been atmospherically corrected by ENVI/QUAC method. For the first time, dynamic changes in green algae biomass were studied over an hourly scale. Short-term biomass changes were highly influenced by Photosynthetically Available Radiation (PAR) and tidal phases, but less by sea surface temperature variations on a daily timescale. A new parameter of biomass changes (PBC), calculated by the ratio of the biomass growth rate to movement velocity, could provide an effective way to assess and forecast green tide in the southern Yellow Sea and similar areas.

green algae biomass; GOCI; BIGAG; parameterizing biomass change

1 Introduction

Since 2007, the annual bloom of the seaweedhas become a serious problem that induced severe green tide disasters in the southern Yellow Sea (SOA, 2016). A number of studies have successfully detected green algae occurrences using satellite instruments, inclu- ding the Moderate Resolution Imaging Spectrora-diome-ter (MODIS; Hu and He, 2008; Liu., 2009;Hu., 2017), Huan Jing-1 (HJ-1; Xing and Hu, 2016), and the Geostationary Ocean Color Imager (GOCI; Son., 2012, 2015). Normalized difference vegetation index (NDVI) and floating algae index (FAI) were proposed to detect green algae outbreaks by using MODIS data from the Yel-low Sea(Hu and He 2008; Hu, 2009). FAI is also avail- able from Landsat 4-8, the Visible Infrared Imaging Ra- diometer Suite/NPOESS Preparatory Project (VIIRS/NPP), and other sensors which analyze red bands, near-infrared reflectance (NIR), and short-wave infrared reflectance (SWIR). However, this index cannot be obtained from HJ-1 and GOCI data due to their lack of the SWIR band. For the HJ-1 satellite with a spatial resolution of 30m and a temporal coverage of 2 days, the Index of Virtual-Base- line Floating Algae Height (VB-FAH) was proposed to measure the height of NIR reflectance (Xing and Hu, 2016), using the green and red bands as the baseline. For the GOCI satellite, the index of floating green algae for GOCI (IGAG) was developed to monitor the change of(Son., 2012, 2015), with a spatial re- solution of 500m and temporal coverage of eight hours per day.

Accuracy in green algae detection based on the satellite images is highly depended on the method of atmospheric correction. Due to their high NIR reflectance, green algae can mistakenly be detected as clouds (Lee., 2010;Son., 2012; Lou and Hu, 2014), turbid water (Lou and Hu, 2014), or high aerosol optical thickness (Son., 2012), consequently leading to an overestimation of water signals from the blue to NIR bands(Son., 2012). If ocean reflectance values were retrieved by using atmos- pheric correction data of the NIR or SWIR bands, green algae could potentially be masked as blank pixels (Hu, 2009; Hu., 2017). Although an atmospheric correc- tion was applied by using maritime aerosol (M90) and coastal aerosol (C50) parameters, the resultant uncertainty was 0.4%–10% for MODIS-FAI. This uncertainty resultedin a biomass estimation error of 0.036–0.09kgm?2for FAI less than 0.2, and 0.0008–2.56kgm?2forFAI more than 0.2(Hu., 2017). However, the Environment for Vi- sualizing Images/Quick Atmospheric Correction (ENVI/QUAC) model, an in-scene approach, is a promising te- chnique that uses the retrieval of surface reflectance images for green algae detection, as it requires only approximate specification of sensor central wavelengths and their ra- diometric calibration (Jiang., 2014).

To date, the inversion of green algae biomass has only been reported in a study that simulated the relationship between FAI and biomass using MODIS data in the sou- ern Yellow Sea (Hu., 2017). In theirwork annual dynamic growth of the green algae biomass was also demonstrated. However, critical data on daytime biomass changes on an hourly scale and its controlling factors have not been discussed. In this work, the GOCI is used to monitor green algae biomass. A new algorithm to get the Biomass Index of Green Algae for GOCI (BIGAG) is developed, based partly on previous work by Son. (2015), Xing and Hu (2016), Xiao. (2017), and Hu. (2017) to resolve its problems associated with the absence of the SWIR band.

2 Materials and Methods

In order to construct an improved algorithm to get BI- GAG, we first selected the 865nm, 745nm and 555nm bands for atmospheric correction based on the ENVI/QUAC method. Next, a simulation model, developed based on biomass measurements and the satellite-derived bio- mass index of green algae from Hu. (2017), was used to calculate green algae biomass from our BIGAG data. Finally, the derived biomass data were related with photo- synthetically available radiation (PAR), temperature, and tidal levels to improve our understanding of daily bio- mass dynamic changes and their controlling factors.

2.1 GOCI Data

GOCI L1B data during severe green algae blooms on June 17 and 25, 2016(SOA, 2016), were retrieved from the Korea Ocean Satellite Center (KOSC) website (http://kosc.kiost.ac.kr/). The Rayleigh corrected reflectance (Rrc) data in different bands were calculated by using GOCI Data Progressing Software (GDPS)(Ryu., 2012; Lou and Hu, 2014). Fig.1 shows an enhanced surfremotesensingreflectance(Rsurf) data in RGB (864), with red color indicating the position of the green tide in the southern Yellow Sea.

2.2 PAR Data

Pyranometer data were retrieved from the National Ae- ronautics and Space Administration (NASA) Aerosol Ro- botic Network (AERONET) website (https://aeronet.gsfc.nasa.gov). In this paper, the Pyranometer data under the level 1.5were screened to remove anomalies. Values for photosynthetically active radiation (PAR) were calculatedby using the following equation:

whereis a fraction of 0.368(Delgado-Bonal, 2017), denoting the ratio ofto the total solar emitted ra- diation, measured by the pyranometer at the Yonsei University site and retrieved from AERONET website. Since there is a one-hour time difference between the uni- versity site and the research area, the duration of retrieved data is therefore set between UTC 01:00 and 08:59. Hourly averages of the solar fluxes during the data acquisition period at the Yonsei University site were applied to cal- culate PAR, which is used to compare with the green algae biomass.

Fig.1 The enhanced RGB (864) image at UTC 07:28 on June 17, 2016 in the southern Yellow Sea.The transect AB is marked by the yellow line in image, where Rrc and Rsurf data are extracted and compared.

2.3 SST Data

Sea surface temperature (SST) L2 data were retrieved from the Advanced Very High-Resolution Radiometer (AVHRR), MODIS (Koner and Harris, 2016)and VIIRS/NPPthe websites http://kosc.kordi.re.kr and https://oceancolor.gsfc.nasa.gov, respectively. Due to different resolution and transit times of these satellites, data were calculated at maximum (90%), minimum (10%), and aver- age values (Table 1). In addition, green algae with high NIR can be wrongly interpreted as clouds (Remer., 2005; Lee., 2010; Son., 2012) when L2 pro- ducts were processed. To reduce blank effects of clouds and green algae, average SST was therefore calculated over a large sea surface area (34?–37?N, 119?–122?E).

2.4 Processing GOCI Data with Different Atmospheric Corrections

Rayleigh corrected reflectance data have been used to detect green algae and trace their movement by using bands of 555nm, 660nm, and 745nm (Son., 2012, 2015), but adverse impacts on green algae detection be- cause of different aerosol types and optical thicknesses can not be overlooked. Hu. (2017) proposed additional atmospheric correction with two aerosol types should be applied to retrieve credible Rsurf values from MODIS data. Here we used the ENVI/QUAC method to deduce Rsurf values from GOCI data (Module, 2009; Bernstein., 2012), and selected the Rsurf values larger than 0.07 at 412nm to remove cloud effects. In comparison with Rrc data, the resolution in distinguishing green algae was sig- nificantly improved (Fig.2)

Table 1 Satellite identifier and some parameters for SST products from multiple satellites

Fig.2 Comparison between Rrc and Rsurf data along the AB transect shown in Fig.1 at UTC 07:28 on June 17, 2016. Rrc is Rayleigh corrected reflectance by GDPS software, and Rsurf is atmospheric corrected reflectance by ENVI/QUAC method.B1, 412nm; B2, 443nm; B3, 490nm; B4, 555nm; B5, 660nm; B6, 680nm; B7, 745nm; B8, 865nm.

2.5 Algorithms for Indexing Green Algae Distributions

The FAI was first proposed by Hu (2009) to measure NIR heights by using red and SWIR bands as the base- line:

whereis sea surface reflectance,is wavelength, and the subscripts RED, NIR, and SWIR represent the red, near infrared, and shortwave infrared bands, respectively.

Theis based on the SWIR band which is un- available in GOCI images. NDVI and Enhanced Vegeta- tion Index (EVI) are not considered because they are af- fected by high chlorophyll-and suspended materials. The IGAG is a type of Rrc data which is not processed by to- tal atmospheric correction. Given these limitations, the ENVI/QUAC method was chosen to conduct atmospheric corrections.

Green algae can be identified by using reflectance valuesat 500–600nm, 700–900nm, and 1000–1100nm because of their intimate correlations (Hu., 2017). Since the GOCI image lacks the 1000–1100nm band, the 555nm, 745nm, and 865nm bands were selected to calculate:

whereis sea surface reflectance andis wavelength.

The indexes of IGAG, VB-FAI, andwere cal- culated from GOCI data on June 17, 2016, then they were atmospherically corrected by Rayleigh or ENVI/QUAC method (Fig.3). It is apparent that the green algae distri- bution areas identified by the indexes VB-FAI, andfollowing Rayleigh correction are much larger than those by the ENVI/QUAC correction. These differences may re- sult from the impacts of variable aerosol types and optical thicknesses ignored in the Rayleighcorrection method. The density of green algae in the nearshore waters along the Subei coast (southeast shoreline in Fig.3) was seem- ingly quite high except for that in Fig.3f. However, this is considered to result from very high suspended sediment concentrations in the Subei coastal waterswhich IGAG and VB-FAI methods can not account for. For the off- shore waters, the green algae distribution areas identified by the index IGAG were much larger than those by the indexes VB-FAI and, due to the decrease of de- tecting ability for the IGAG method as the concentration of suspended matter increased (Son., 2012). In short, two steps of GOCI data processing procedures involving ENVI/QUAC correction and the index BIGAG are sug- gested for effectively detecting green algae distribution in the southern Yellow Sea and other similar settings with high concentrations of suspended matter.

2.6 Biomass Simulation

Two step-wise correlations are clearly shown in the scatter plot of green algae biomass.the index,cited from Hu. 2017 (Fig.4). Biomass increases slowly asincreases for values less than 0.2kgm?2, with a linear fitting equation:

whereis biomass,is, and2=0.9891. Bio- mass increases rapidly asincreases above 0.2kgm?2, defined by an exponential fitting equation:

whereis biomass,is, and2=0.9281.

The green algae biomass was calculated by using these two equationswithdata on June 17 and 25, 2016. The results indicate that the number of pixels withvalues larger than 0.2 is less than 300, repre- senting only 0.1% of the green algae area. The recons- tructed biomass ranged from 452kt to 15363kt on June 17, 2016, and from 784kt to1380kt on June 25, 2016 in the study area.

Fig.3 Comparison of green algae distribution detecting by different atmospheric correction and identifying indexes based on GOCI data at UTC 07:28 on June17, 2016. Red pixels represent green algae identified by the IGAG>0 (a, d), VB-FAI >0.025 (b, e), and BIGAG>0 (c, f) after Rrc (top row) and ENVI/QUAC (bottom row) atmospheric corrections, re- spectively. White, blue, and black pixel denote cloud, water, and land, respectively.

Fig.4 The relationship between the green algae biomass and the dimensionless BIGAG (original data from Hu et al., 2017).

3 Results

Hourly changes in the green algae biomass were cal- culated based on its relationship with theas shown in Fig.4 on June 25, 2016 (Fig.5). The green algae were mainly distributed in the northernpart of the south Yel- low Sea. The distribution area increased from 2125km2at UTC 00:28 to 3782km2at UTC 02:28 (a 77% increase), and then decreased to 1212km2at UTC 07:28. The area with the green algae biomass larger than 0.5kgm?2also reached its maximum of 612km2at UTC 02:28. It is in- teresting to note that similar daytime biomass variations in phytoplankton were also reported in Taihu Lake to the southwest of the Yellow sea (Xu., 2016), but they differ from the red tide on May 29 and 30 in the East China Sea (Lou and Hu, 2014). In addition to hourly va- riations, green algae biomass has been reported to change significantly over monthly and seasonal scales (Lee., 2012).

The growth rate of green algae biomass is usually ex- pressed as the dynamic growth rate. We calculated the dynamic growth rate (; Table 2) based on the hourly biomass data derived from the indexon June 25, 2016 using the following the formula:

where() and(+1) are the biomass at hourand the following hour, respectively.

The patches of green algae were defined based on their densities. Green algae movements were monitored by using hourly spatial variations of single patch (Fig.6). Mean mo- vement velocity was calculated by the following equation that involved the moving distance of the geographical centers for each patch during one period and the subsequent period:

Fig.5 Hourly variations in the green algae biomass (kgm?2) on June 25, 2016 from UTC 00:28 to 07:28.

Table 2 The growth rate (α), velocity (V), and parameter of biomass change (PBC) for the green algae bloom on June 25, 2016

The hourly change of biomass,that is the parameter of biomass change (PBC; Table 2), is defined as the ratio of the dynamic growth rate to the hourly movement velocity (dimensionless):

whereis longitude,is latitude, andis the average hourly movement velocity.

The results show that on June 25, 2016, the green algae moved eastward with an average velocity of 3.32kmh?1from UTC 00:28 to 07:28 (Fig.6; Table 2). Maximum and minimum velocities are 5.60kmh?1during UTC 06:28 to 07:28 and 1.12kmh?1during UTC 01:28 to 02:28, respec- tively (Table 2). The green algae distribution and move- ment trajectory displayed as a triangular shape defined by three corners (37?N, 122?E), (35?N, 119?E) and (34?N, 121?E). Most of green algae moved to the east or nor- theast, but at the southern part of study area, they tended to move to the southeast.

Fig.6 The distribution and movement trajectory of green algae on June 25, 2016 from UTC 00:28 to 07:28.

4 Discussion

4.1 Factors Affecting Biomass

The synchronous change between green algae biomass andon a daily scale indicates thatis a key factor affecting green algae bloom (Xu., 2016; Pliego-Cortés., 2017; Fig.7). Biomass was also observed to decrease as tidal level dropped after UTC 2:28, and the general eastward movement trajectory of green algae was consistent with the ebb current directions (Figs.6, 7). Bothand tidal variations can exert significant control on green algae biomass distribution. Ebb tides tend to mix nearshore water masses with abundant green algae with an offshore water mass with less algae, resulting in the dilution effect (Lou and Hu, 2014). The pathway of green algae movement in the south Yellow Sea was also re- ported to be controlled by shelf circulation and wind di- rection (Son., 2015), following the detachment of the Subei coastal area (Liu., 2009, 2010).

Fig.7 Hourly changes in biomass, PAR, and tidal level on June 25, 2016. Tidal data were retrieved from the Qingdao Harbor via http://app.cnss.com.cn.

Sea surface temperature (SST), obtained from AVHRR, MODIS, and NPP data, was also one of the factors af- fecting the biomass. It can be seen that the SST varied between 20℃ and 26℃ from UTC 02:00 to 08:00 on June 25, 2016 (Table2), which has been reported to be a suitable temperature range to support green algae bloom (Fan., 2013; Son., 2015; Xiao., 2016). How-ever, slight hourly changes in the average SST were not found to covary with marked hourly changes in the green algae biomass. In other words, SST may not play an im- portant role in the biomass distribution on an hourly scale.

In addition, large spatiotemporal variations in the green algae biomass were also reported to be well correlated with the nutritional element abundance and types (Fan., 2013; Li., 2016), and trace element abundance (Shi., 2015; Dao and Beardall, 2016; Gao., 2017). Their potential influence on daily biomass changes has not been well documented, and is beyond the scope of this study.

4.2 Dynamic Changes in the Green Algae Biomass

The parameter of biomass change,, is very crucial for monitoring spatiotemporal variations of green algae in the Yellow Sea by satellites (Table 2). Its values range from ?0.01tm?1to 0.1tm?1(Table 2). A negativeimplies that the water conditions inclined to suppress green algae growth, whereas a positiveindicates the setting is favorable for green algae bloom. Thus,is considered to be more useful than the growth rate for an early warning indicator of green algae bloom.

5 Conclusions

Macroalgae blooms of(green tide) in the southern Yellow Sea were detected and tracked for the first time over an hourly scale by using the Geostationary Ocean Color Imager (GOCI). Two steps of GOCI data processing,involving ENVI/QUAC atmospheric correction and the identification based on the index, gave the best estimation of green algae distributions in the sou- thern Yellow Sea, and can potentially be applied to si- milar settings with high suspended loads. The green algae biomass was calculated fromdata by using the fitting formula derived from the relationships between biomass measurements and satellite biomass indexes pro- posed by Hu. (2017) in the southern Yellow Sea. Hourly biomass variations were highly correlated with Photosynthetically Available Radiation (PAR) and tidal phases, but less with sea surface temperature variation on a daily scale. A new parameter of biomass change,was proposed by dividing the biomass growth rate by the movement velocity to monitor high spatiotemporal va- riations of green algae. It could provide a near real-time index to assess and forecast green tide development in the southern Yellow Sea.

Acknowledgements

This work is funded by the National Natural Science Foundation of China (Nos. 41776052, 41476031), the Re- search Fund of State Key Laboratory of Marine Geology (No. MG20190104),andthe China Scholarship Council (No. CSC201906260052). We thank KOSC, AERONETfor providing the GOCI data and NOAA-SST, Pyrano- meter data respectively. We are grateful to the MODIS-SST and NPP-SST by NASA. We would also like to thank ShengHsiang (Carlo) Wang from Prof. Lin’s group in AERONET Liulin site.

Bernstein, L. S., Jin, X. M., Gregor, B., and Adler-Golden, S. M., 2012. Quick atmospheric correction code: Algorithm description and recent upgrades., 51: 111719.

Dao, L. H. T., and Beardall, J., 2016. Effects of lead on growth, photosynthetic characteristics and production of reactive oxy- gen species of two freshwater green algae., 147: 420-429.

Delgado-Bonal, A., 2017. Entropy of radiation: The unseen side of light., 7: 1642, DOI: 10.1038/s41598- 017-01622-6.

Fan, X., Xu, D., Wang, Y., Zhang, X., Cao, S., Mou, S. L., and Ye, N., 2014. The effect of nutrient concentrations, nutrient ratios and temperature on photosynthesis and nutrient uptake by: Implications for the explosion in green tides., 26: 537-544.

Gao, G., Liu, Y., Li, X., Feng, Z., Xu, Z., Wu, H., and Xu, J., 2017. Expected CO2-induced ocean acidification modulates copper toxicity in the green tide alga., 135: 63-72.

Hu, C. M., 2009. A novel ocean color index to detect floating algae in the global oceans., 113: 2118-2129.

Hu, C. M., and He, M. X., 2008. Origin and offshore extent of floating algae in Olympic sailing area., 89: 302-303.

Hu, L. M., Hu, C. M., and Xia, H. E., 2017. Remote estimation of biomass ofmacroalgae in the Yellow Sea., 192: 217-227.

Jiang, B. B., Zhang, X. Y., Du, Y., and Huang, D. S., 2014. Establishing monitoring model of Haze event with multi-satel- lite data and application–A case: Yangtze River Delta.. Beijing, 92991C.

Koner, P. K., and Harris, A., 2016. Sea surface temperature retrieval from MODIS radiances using truncated total least squares with multiple channels and parameters., 8 (9): 725-741.

Lee, J., Kim, J., Song, C. H., Ryu, J. H., Ahn, Y. H., and Song, C. K., 2010. Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager., 114: 1077-1088.

Lee, Z. P., Jiang, M., Davis, C., Pahlevan, N., Ahn, Y. H., and Ma, R., 2012. Impact of multiple satellite ocean color samplings in a day on assessing phytoplankton dynamics., 47: 323-329.

Li, H., Zhang, Y., Han, X., Shi, X., Rivkin, R. B., and Legendre, L., 2016. Growth responses ofto inorganic and organic nutrients: Implications for macroalgal blooms in the southern Yellow Sea, China., 6: 26498.

Liu, D. Y., Keesing, J. K., Dong, Z., Zhen, Y., Di, B., Shi, Y. J., Fearns, P., and Shi, P., 2010. Recurrence of the world’s largest green-tide in 2009 in Yellow Sea, China:aquaculture rafts confirmed as nursery for macroalgal blooms., 60: 1423-1432.

Liu, D. Y., Keesing, J. K., Xing, Q., and Shi, P., 2009. World’s largest macroalgal bloom caused by expansion of seaweed aquaculture in China., 58: 888-895.

Lou, X. L., and Hu, C. M., 2014. Diurnal changes of a harmful algal bloom in the East China Sea: Observation from GOCI., 140: 562-572.

Module, F. L. A. A. S. H., 2009.ITT Visual Information So- lutions Publishers, New York, 6-38.

Pliego-Cortés, H., Caamal-Fuentes, E., Montero-Mu?oz, J., Frei- le-Pelegrín, Y., and Robledo, D., 2017. Growth, biochemical and antioxidant content of(Rho- dymeniales, Rhodophyta) cultivated under salinity and irra- diance treatments., 29: 2595- 2603.

Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R. R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T. F., Vermote, E., and Holben, B. N., 2005. The MODIS aerosol algorithm, products, and validation.s, 62: 947-973.

Ryu, J. H., Han, H. J., Cho, S., Park, Y. J., and Ahn, Y. H., 2012. Overview of Geostationary Ocean Color Imager (GOCI) and GOCI Data Processing System (GDPS)., 47: 223-233.

SOA, 2016. The public report on marine disaster of China. http:// www.soa.gov.cn.

Shi, R. J., Li, G., Zhou, L., Liu, J., and Tan, Y., 2015. The increasing aluminum content affects the growth, cellular chlorophylland oxidation stress of cyanobacteriasp. WH7803., 44: 343-351.

Son, Y. B., Choi, B. J., Kim, Y. H., and Park, Y. G., 2015. Tracing floating green algae blooms in the Yellow Sea and the East China Sea using GOCI satellite data and Lagrangian transport simulations., 156: 21-33.

Son, Y. B., Min, J. E., and Ryu, J. H., 2012. Detecting massive green algae () blooms in the Yellow Sea and East China Sea using Geostationary Ocean Color Imager (GOCI) data., 47: 359-375.

Xiao, J., Zhang, X., Gao, C., Xiao, J., Zhang, X. H., Gao, C. L., Jiang, M. J., Li, R. X., Wang, Z. L., Li, Y., Fan, S. L., and Zhang, X. L., 2016. Effect of temperature, salinity and irra- diance on growth and photosynthesis of., 35: 114-121.

Xiao, Y., Zhang, J., and Cui, T., 2017. High-precision extraction of nearshore green tides using satellite remote sensing data of the Yellow Sea, China., 38: 1626-1641.

Xing, Q., and Hu, C. M., 2016. 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., 178: 113-126.

Xu, D., Zhang, X., Wang, Y., Fan, X., Miao, Y., Ye, N. H., and Zhuang, Z. M., 2016. Responses of photosynthesis and nitrogen assimilation in the green-tide macroalgato desiccation., 163: 9, DOI: 10.1007/s00227- 015-2806-6.

. E-mail: ddfan@#edu.cn

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May 29, 2019;

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(Edited by Chen Wenwen)