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Abstract
Subsurface phytoplankton vertical structure was observed using an offshore fixed platform-based lidar in the Bohai Sea for the first time. The lidar obtained two periods of continuous day-and-night measurements for a week. A hybrid retrieval method for the optical properties and chllorophyll-a concentration vertical structure of seawater using lidar data was developed. We studied offshore subsurface phytoplankton vertical variation responses to Typhoon Bavi. Significant changes in the intensity and depth of the subsurface phytoplankton maximum layer in the Bohai Sea may result from horizonal advection, light availability, and rainfall dilution following Typhoon Bavi. Preliminary results suggested that lidar measurements provide a new approach for understanding oceanic dynamics mechanisms at the submeso-mesoscale.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
The Bohai Sea, the northernmost offshore sea of China, is a nearly closed sea. It is an inlet of the Yellow Sea on the northeast coast of China formed by the Shandong and Liaodong Peninsulas. China’s second-longest river (Yellow River) discharges into the sea. It covers an area of approximately 80,000 square kilometers with a population of approximately 70 million living in its coastal area and has an average depth of 18 meters. The continental monsoon climate is cold in winter and hot in summer, with four distinct seasons. The length of the coastline is approximately 3,800 kilometers. It is approximately 346 kilometers wide from east to west and 550 kilometers long from north to south. Because the Bohai Sea is a semienclosed sea, both the water exchange capacity and self-purification capacity are poor, and the surroundings are developed industrial cities, the sea water pollution of which is very serious [1]. As the largest enclosed sea of China, the Bohai Sea has confronted significant environmental changes in recent decades and a marked increase in total phytoplankton biomass, small-sized species, and harmful algal bloom species. [2,3]. A crucial need exists for monitoring and understanding the environmental changes in this region because of human activity and climate change.
Ocean color remote sensing data can provide large-scale, frequent, and near-surface views of these environmental changes [4–6]. However, ocean color remote sensing is sensitive to only the very-near-surface layer and provides no information on the vertical structure [7]. Usually, the vertical structure of phytonplankton is not homogeneous and displays a pronounced maximum close to the base of the euphotic zone, which is called the subsurface chlorophyll-a maximum layer (SCML) [8]. In addition, the SCML generally exists close to the nutricline, the region of seawater where the greatest changes in the nutrient concentration occur with depth [9]. Generally, the nutritional needs and light availability of resident organisms determine the formation and location of the SCML [10]. A postulated typical stable water structure is characterized by consistent patterns in vertical profiles of chlorophyll, phytoplankton biomass, nutrients, and light across a trophic gradient structured by the vertical flux of nutrients and characterized by the average daily irradiance at the nutricline [11]. However, far from the surface layer “seen” by a satellite, the SCML in the lower part of the euphotic zone in the Bohai Sea is even less understood [8,12].
In this study, we described SCML observations using an offshore fixed platform-based lidar in the Bohai Sea for the first time. The lidar configuration and study area are described in Section 2. Subsequently, a hybrid retrieval method for seawater optical properties and chllorophyll-a using lidar data was developed in Section 3. In Section 4, we found significant changes in the intensity and depth of SCML in the Bohai Sea, which may result from horizonal advection, light availability, and rainfall dilution following Typhoon Bavi. Active lidar measurements could provide depth-resolved values of ocean phytoplankton properties at both day and night. Thus, lidar data can provide a more complete three-dimensional characterization of the typhoon effect on SCML properties and their variability at the submeso-mesoscale.
2. Lidar system
An offshore fixed platform-based lidar was developed by the College of Optical Science and Engineering, Zhejiang University. The lidar is composed mainly of a 532 nm Nd:YAG pulse laser; four PMT detectors for receiving parallel polarization Mie scattering, perpendicular polarization Mie scattering, Raman scattering, and fluorescence; an analog digital converter (ADC) with a sample rate of 400 MS/s and a bandwidth of 200 MHz; interference filters with an efficiency larger than 70% and bandwidths of 0.6 nm for 532 nm, 10 nm for 650, and 10 nm for 685 nm; and a telescope with a diameter of 80 mm and a field of view of 200 mrad. Among them, the laser has a pulse energy of 5 mJ, a repetition rate of 10 Hz, a pulse width of 8 ns, a beam divergence of less than 1 mrad, and a beam diameter of 8 mm. The PMT detector efficiency is larger than 18%, and the anode responsivity is 0.3 A/W. The ADC has four acquisition channels with a resolution of 14 bits. The total weight of the system is approximately 60 kg, and the bulk volume is approximately 0.2 m3. Figure 1(c) shows photos of the lidar system taken at night. Figure 2 shows the schematic diagram of the lidar system, and Table 1 shows the detailed parameters of the platform-based lidar.
Fig. 1. Offshore fixed platform-based lidar observations in the Bohai Sea during the period of Typhoon Bavi. (a) Typhoon Bavi traveling path and position of the offshore fixed platform; the red box is the location of the platform, and the red circles are the typhoon path; (b) actual photo of the fixed platform; (c) actual photo of lidars on the fixed platform.
Fig. 2. Schematic diagram of the lidar system. BS1, BS2, BS3: beam splitter; M1, M2: mirror; PBS: polarizing beam splitter; OF1, OF2, OF3: optical filter; PMT1, PMT2, PMT3, PMT4: photomultiplier tubes.
Table 1. Platform-based lidar system parameters.
The lidar was mounted on a fixed platform (37.6950°N, 121.6269°E) above the sea, which is located near northern Yangma Island, Muping, Yantai. It is approximately 24 km away from the nearest shore, and the mean seawater depth is approximately 18.5 m there. Figure 1(a) shows the location of the platform (red box) in the Bohai Sea. The platform is a floating semisubmersible platform, with an occupation area of 25 m×25 m, which is approximately 7 m above sea level. Figure 1(b) shows a photo of the fixed platform above the sea. Simultaneously, in situ chlorophyll-a fluorescence was measured to validate the lidar observations using a RBR water quality monitor (RBR XR-420, RBR Ltd., Canada), and the fluorescence data were calibrated to extract chlorophyll-a concentration using a laboratory scanning spectrofluorometer (Trilogy, Turner Designs Inc.) [13]. The lidar obtained continuous measurements in the period from August 23 to August 25 at the start of Typhoon Bavi and from August 29 to August 31 at the end of Typhoon Bavi. Unfortunately, the lidar operated unsuccessfully from August 26 to August 28 due to the solar power shortage during the rainfall period. The in situ data were obtained every hour in the period from August 23 to August 25 at the start of Typhoon Bavi and from August 29 to August 31 at the end of Typhoon Bavi.
3. Methods
Based on the laser light propagation process and theory, the depth-dependent lidar return signal $P(z )$ is described as follows [14]:
A more convenient signal variable is the logarithmic range corrected power $S(z)$, defined as:
It can be rewritten in the following differential form: A solution to this equation requires assuming or knowing the relationship between α and β whenever $\frac{{d\beta }}{{dz}} \ne 0$. On the other side, when the water is optically homogeneous, so that $\frac{{d\beta }}{{dz}} = 0$, then ${k_{lidar}}$ could be simplified expressed in terms to the attenuation signal slope [15]:For inhomogeneous water, we can obtain α based on the Klett method as:
where $\zeta $ is the exponent according to a power law of the form ${\beta _\pi } = const \times \alpha {k_{lidar}}^\zeta $, which depends on lidar wavelength and various properties of the water on the interval $0.67 \le \zeta \le 1.0$. In this study, we assume $\zeta $ = 1.0.A hybrid Klett-Perturbation inversion method (K-P) [16,17] was proposed, which estimates the lidar attenuation coefficient ${k_{\textrm{lidar}}}$ and the backscatter coefficient at 180° ${\beta _\pi }(z )$ based on the integration of the Klett retrieval method [15] and the perturbation retrieval (PR) method [18]. The Klett method is a well-known analytical solution that assumes a power law relationship between ${\beta _\pi }(z )$ and ${k_{\textrm{lidar}}}$, and the PR method obtains a backscatter assuming a linear regression to the logarithm of the lidar return for retrieving the nonvarying part of water optical parameters, which is followed by a perturbation signal for retrieving the varying part of water optical parameters. The ${k_{\textrm{lidar}}}(z )$ and ${\beta _\pi }(z )$ could be estimated as follows:
Then, the particulate backscatter coefficient ${b_{bbp}}$ and chlorophyll-a concentration $Chl$ could be estimated by [22–25]:
where $\chi $ is a conversion factor that relates ${\beta _\pi }$ to ${b_{bp}}$. There is an uncertainty for Eq. (7) in $\chi $. Some studies report it was approximately 0.5 [26–28], and some report it was approximately 1.43 [29,30], while others report it was 1.06 [24,31–34]. In this study, we assume $\chi $ was 1.06. In the past, we measured both ${b_{bp}}$ and $\beta ({140^\circ } )$ using the Hydroscat-6 instrument in the East China Sea [35], and the calculated $\chi ({140^\circ } )$ was 1.06 and the estimated $\chi ({180^\circ } )$ was approximately 1.06 based on Sullivan’s conclusion [33,34] that the $\chi$ factor has been assumed to be nearly the same at all wavelengths.4. Results and discussion
4.1 SCML using lidar data
Figure 3 shows an example of step-by-step lidar retrieval along the ship track in sea water in the Bohai Sea. Figure 3(a) is the raw lidar data. It appears that the signal was strong when lidar light just reached the sea surface, and the signal’s magnitude decreased gradually as the water depth increased. Figure 3(b) shows the results after signal de-noised and background correction; and Fig. 3(c) is the result after range corrected process by Eq. (2) in the Section 3. The de-noised signal is the signal after preprocessing process. It contains the multi-pulses averaging procedure, smoothing procedure, Richardson–Lucy de-convolution procedure and so on. We can see that the background signal’s magnitude due to sun light and detector dark current is about 20 (Fig. 3(a)), and has been removed from the original signal (Fig. 3(b)). The lidar signal due to water attenuation and scattering can then be obtained after range correction, which can eliminate lidar observation geometric influence. Figure 3(d) is the retrieved lidar attenuation coefficient using the Klett method by the Eq. (6) in the Section 3. The black masked region means the inversion failed due to low SNR or at the sea bottom. Because the lidar FOV during that experiment was very large, the lidar attenuation coefficient approached water diffuse attenuation coefficient Kd. We then could obtain ${b_{bp}}$ and chlorophyll concentration by the Eqs. (10)–(11) in the Section 3. It reveals that the sea water was relatively turbid in the coastal sea area (Fig. 3), and that there may be usually subsurface phytoplankton maximum layers ranging from approximately 2 m to 6 m depending on time during the experiments from Aug. 23 to Aug. 25.
Fig. 3. An example of step-by-step lidar retrieval results along a ship track in sea water in the Bohai Sea. (a) Lidar raw data, (b) after signal de-noised and background corrected, (c) after range corrected, (d) lidar-retrieved attenuation coefficient.
Figure 4 shows the subsurface vertical structure of ${k_{lidar}}$, $b_{bp}$, and chlorophyll-a retrieved by lidar data using the K-P method obtained in the periods from Aug. 23 to Aug. 25 at the start of Typhoon Bavi and from Aug. 29 to Aug. 31 at the end of Typhoon Bavi. The maximum penetration depth for lidar was approximately 14 m because of the eutrophic water with high concentrations of chlorophyll and low water transparency in this coastal region. We can see that there were usually subsurface phytonplankton maximum layers at depths of approximately 4–5 m depending on time (Figs. 4(d) and 4(h)). There were similar change trends for water optical properties and chlorophyll-a, and both the water optical properties and chlorophyll-a concentration decreased from Aug. 24. Meanwhile, the depth of the SCML decreased slightly (approximately from a depth of 5 m to a depth of 4 m) from August 24 at the start of the typhoon and returned to a depth of 5 m from August 30 at the end of the typhoon. These biological responses to Typhoon Bavi may depend on temperature, nutrients and light availability, dynamic processes and so on [36]. We plotted the statistical difference for lidar retrieved chlorophyll-a concentrations vs. time series at different depths in Fig. 5. It shows the chlorophyll-a concentrations varied as the time varied, and the chlorophyll-a concentrations variation tendency depending on time at different depths varied. Ocean color remote sensing data can provide near-surface views of environmental changes; however, it is sensitive to only the very-near-surface layer and provides no information on the vertical structure. Active lidar measurements could provide depth-resolved values, which can provide new insights into seawater bio-optical vertical structures and temporal and spatial variation.
Fig. 4. Subsurface vertical structure of ${k_{lidar}}$, ${b_{bbp}}$, and chlorophyll-a retrieved by lidar data obtained in the periods from August 23 to August 25 at the start of Typhoon Bavi and from August 29 to August 31 at the end of Typhoon Bavi. (a) and (e) are raw lidar signals; (b) and (f) are ${k_{lidar}}$; (c) and (g) are ${b_{bbp}}$; (d) and (h) are chlorophyll-a concentrations.
We compared the lidar retrievals to the in situ-observed chlorophyll-a concentration. Figure 6(a) shows the comparison between continuous lidar-estimated and discrete in situ-observed chlorophyll-a concentrations. Figure 6(b) shows the regression plot for them. We can see that there appears to be a similar change trend for lidar retrievals and in situ observations. The chlorophyll-a concentration appears larger during the night and smaller during the day (Fig. 6(a)), which may be due to the tide effect. Tides play an important role in the aggregation and diffusion of phytoplankton [37]. There are high tides during the day and ebb tides during the night in this coastal region. Phytoplankton may disperse with high tides because many phytoplankton may be brought into inshore waters during high tides so that the chlorophyll-a values decrease during the day. The chlorophyll-a values increased as phytoplankton aggregated around ebb tides at night. Statistical analysis shows that the lidar estimate agrees well with the in situ-observed chlorophyll-a concentration, with an R2 of 0.69 and RMSE of 0.4 µg/L, and the mean absolute relative error MAPE (the average of all errors divided by the actual value) is low (18.4%). The lidar-estimated values overestimate slightly when the chlorophyll-a concentration is smaller than 1.8 µg/L, while they underestimate when the chlorophyll-a concentration is larger than 1.8 µg/L. Overall, our results showed that the hybrid retrieval method was feasible and effective for phytoplankton monitoring over a long time series. Figure 7 shows the comparisons between lidar retrievals and in situ data by lidar fluorescence signal. It reveals that the relationship between lidar retrievals and in situ data by lidar fluorescence signal was relatively lower than that by lidar Mie signal in this experiment, which may be due to that the disturbance of weak fluorescence signal by the background solar light [38]. The fluorescence lidar performance is another impact factor limited to weak-light detection performance and filter bandwidth. More investigations will be carried out to study the comparisons between lidar fluorescence signal and in situ chlorophyll-a data and improve the fluorescence lidar performance to eliminate the ambiguity of the algal or inorganic signals in the future.
Fig. 6. Comparisons between lidar retrievals and in situ data. (a) LiDAR retrievals and in situ data depending on time; (b) regression plot.
Fig. 7. Comparisons between lidar retrievals and in situ data by lidar fluorescence signal. (a) LiDAR retrievals and in situ data depending on time; (b) regression plot.
4.2 Offshore SCML responses to Typhoon Bavi
We analyzed the sea-surface wind speed (SSW), sea-surface temperature (SST), and daily precipitation data during Typhoon Bavi. Both the SSW and SST data were obtained from Remote Sensing Systems (https://www.remss.com/missions/amsr/) [39]. Precipitation data were obtained from Global Precipitation Climatology Project (GPCP) daily data (http://eagle1.umd.edu/GPCP_ICDR/GPCP_Monthly.html) [40,41]. The typhoon was accompanied by rainfall, wind speed increase, and temperature decrease. As the typhoon approached, the wind speed in the Bohai Gulf began to increase on August 24 (Fig. 8(b)) and reached its maximum on August 26 (Fig. 8(c)) when the typhoon arrived. Then, it gradually decreased (Fig. 8(d)) and recovered to the level before the typhoon on August 30 (Figs. 8(a, e)). The temperature began to drop in the Bohai Gulf on August 24 (Fig. 8(g)), with the largest drop on August 26 and August 28 (Figs. 8(h), 8(i)), and then returned to normal on August 30 (Fig. 8(j)). At the same time, rainfall occurred on August 24 (Fig. 8(l)) and peaked on August 26 (Fig. 8(m)). Afterward, as the impact of the typhoon weakened, the rainfall decreased on August 28 (Fig. 8(n)) and stopped on August 30 (Fig. 8(o)). We found that the time of SCML intensity decrease agreed with the time of precipitation on August 24, which revealed that the rainfall dilution following the typhoon may be a main factor. In addition, the SST variation agrees with those of the SCML depth variation. The temperature began to drop in the Bohai Gulf on August 24 (Fig. 8(g)) and then returned to normal on August 30; meanwhile, the SCML depth decreased from August 24 and increased from August 30, and they had a similar variation trend. This revealed that solar light availability may be another factor for SCML depth variation because temperature variation usually represents the light-availability characteristics of the upper ocean. Thus, the lower temperature on Aug. 24 than that on Aug. 23 means weaker daily irradiance and shallower penetration into the upper layer and makes the SCML depth shallower on Aug. 24, and the higher temperature on Aug. 30 than that on Aug. 29 makes the SCML depth return deeper on Aug. 30. There is a similar SCML pattern driven by light availability [42]. In addition, one note that there may be substrate of suspended or resuspended sediment layer, especially in the sea region which is very shallow and influenced strongly by tidal currents and wind and waves so on [16,17,36,43]. The hourly variations between the lidar-derived subsurface chlorophyll-a concentrations and tide heights were compared throughout the day. The tide height data were obtained from the National Marine Data and Information Service (http://global-tide.nmdis.org.cn). The hourly variations in the lidar-derived chlorophyll-a concentrations at a depth of 10 m (blue line) during a full day on Aug 30 are shown in Fig. 9. The lidar-derived values were highest at 1:00, and they decreased gradually to their lowest values at approximately 10:00. After that time, the lidar-derived values increased gradually over time and reached their highest values at approximately 20:00. Then, they decreased gradually and reached their lowest values at approximately 22:00. Subsequently, they once again increased gradually over time. Overall, the diurnal hourly variations in the chlorophyll-a concentrations were relatively smaller at midday but were larger in the evening, while the relative tide heights showed the opposite change trend, which revealed that the tides possibly impacted the diurnal variations in IOPs and chlorophyll-a concentrations. One possible reason is that tides play an important role in the aggregation and diffusion of phytoplankton [37]. Phytoplankton may disperse with high tides because many phytoplankton may be carried into inshore waters during high tides so that the chlorophyll-a concentrations decrease. The chlorophyll-a concentrations increased as the phytoplankton aggregated during ebb tides. Additionally, the SCML in coastal areas may also be regulated indirectly by injections of nutrients from ebb tide-induced currents, as well as by the effective entrainment mixing of nutrients and resuspended sediment from the bottom due to the shallow depths [44].
Fig. 8. SSW, SST, and precipitation mapping from August 22 to August 30 during Typhoon Bavi. (a)–(e) are SSW; (f)–(j) are SST; and (k)–(o) are precipitation on different days.
Fig. 9. Plot showing a comparison of the hourly variations between the lidar-estimated subsurface chlorophyll concentrations and tide heights. The blue line shows the lidar-estimated chlorophyll levels and the red line shows the tide heights.
To further reveal the mechanisms related to the variability of the subsurface phytoplankton vertical structure, we also applied the temperature, salinity and velocity from data assimilation (JPL ECCO2 cube92, http://apdrc.soest.hawaii.edu/) [45]. The marine upper layer is controlled by surface forcing and oceanic dynamics [46]. The surface forcing consists of solar radiation, precipitation, evaporation, and wind. Horizontal advection is the main component of oceanic dynamics. The variabilities in phytoplankton vertical structure and chlorophyll-a concentration during Typhoon Bavi (Fig. 4) were an air-sea coupling result. Heavy precipitation after August 24 diluted the chlorophyll-a concentration (Fig. 8). Continuous precipitation and westward low-salinity advection led to a significant decrease in SSS from August 25 to August 31 (Fig. 10). As a result, the stratification became more stable, and the mixed layer shoaled [47], which is consistent with the changes in the subsurface chlorophyll-a maximum layer from our observed values (Fig. 4). The cold upper layer during the typhoon period is a response of less shortwave radiation associated with the clouds (blank regions) and cold horizontal advection (Fig. 10). A recent study suggested that the influence of upwelling in the seawater may be the other main factor for the formation of SCML [48]. As shown in Fig. 9, the SCML in coastal areas may be regulated indirectly by the effective entrainment mixing of nutrients and resuspended sediment from the bottom due to the shallow depths [44]. Overall, significant changes in the intensity and depth of the subsurface phytoplankton maximum layer in the Bohai Sea may result from horizonal advection, vertical advection, light availability, and rainfall dilution following Typhoon Bavi. These results also suggested that lidar measurements can be a new approach for understanding oceanic dynamics mechanisms at the submeso-mesoscale.
Fig. 10. Hovmöller diagrams of (a) SSS, (b) SST, and (c) sea-surface zonal velocity (SSU) along 38°N from Aug. 23 to Sep. 5 during Typhoon Bavi. The dashed-lines denote the location of the lidar. The units are psu, °C, and m/s, respectively.
5. Summary and conclusion
Lidar technology was used to successfully investigate the SCML vertical structure in the Bohai Sea for the first time. The results showed that there were usually SCMLs at depths of 4–5 m in this coastal region. There appears to be a similar change trend for lidar retrievals and in situ observations. Statistical analysis shows that the lidar estimate agrees well with the in situ-observed chlorophyll-a concentration, with an R2 of 0.69 and RMSE of 0.4 µg/L, and the relative error is low (18.4%). Overall, our results showed that lidar technology was feasible and effective for phytoplankton monitoring over a long time series. In the past, satellite remote sensing data provided large-scale, frequent, and near-surface views for these environmental changes. However, it is sensitive to only the very-near-surface layer and provides no information on the vertical structure. Active lidar measurements could provide depth-resolved information, which can provide new insights into seawater bio-optical vertical structures with this new vertically resolved and diurnal continuous observed capacity.
We found significant changes in the intensity and depth of SCML in the Bohai Sea during Typhoon Bavi. Both the water optical properties and chlorophyll-a concentration decreased from Aug. 24. Meanwhile, the depth of the SCML decreased slightly (approximately from a depth of 5 m to a depth of 4 m) from August 24 at the start of the typhoon and returned to a depth of 5 m from August 30 at the end of the typhoon. The time of SCML intensity decrease agrees with the time of precipitation on August 24, and the SST variation agrees with those of the SCML depth variation. These results revealed that horizonal advection, light availability, and rainfall dilution following Typhoon Bavi may be the driving factors. However, the causes of SCML variation in optical properties in different sea regions remain poorly understood, including temperature, nutrient availability, light intensity, dynamic processes, and so on [36]. These results also suggested that lidar measurements can be a new approach for understanding oceanic dynamics mechanisms at the submeso-mesoscale.
The lidar obtained continuous measurements in the periods from August 23 to August 25 at the start of Typhoon Bavi and from August 29 to August 31 at the end of Typhoon Bavi. Unfortunately, the lidar operated unsuccessfully from August 26 to August 28 due to the solar power shortage during the rainfall period. One of the future works will be focused on the power supply using many storage batteries in case of solar power shortages during the rainfall period. In this study, we compared the lidar retrievals to the in situ-observed chlorophyll-a concentration at the sea surface due to the lack of in situ profile data. The inorganic particles concentration at the sea surface may be relatively low and the relative measurements error was within 20%, while the relative error for the profile data should be higher, we will investigate the effect from inorganic particles in the future. Moreover, a study on the lidar remote sensing of phytoplankton blooms occurring in the upper euphotic mixed layer will be carried out. Further investigations are needed to conduct more lidar experiments in different sea regions with different detection mechanisms coupling Mie scattering, Raman scattering, fluorescence, and so on in the future.
Funding
Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0602); National Natural Science Foundation (41901305, 61991453); Natural Science Foundation of Zhejiang Province (LQ19D060003).
Acknowledgments
The authors would like to thank the NASA AMSR-E Science Team, NOAA's National Centers for Environmental Information (NCEI), and Asia-Pacific Data Research Center (APDRC) for providing the data used in this study. Satellite SSW and SST data are publicly available through Remote Sensing Systems and were sponsored by the NASA AMSR-E Science Team and the NASA Earth Science MEaSUREs Program (https://www.remss.com/missions/amsr/). Datasets for this research are available in these in-text data citation Refs.: [40]. Precipitation data are freely available from Global Precipitation Climatology Project (GPCP) daily data of NCEI (http://eagle1.umd.edu/GPCP_ICDR/GPCP_Monthly.html). Datasets for this research are available in these in-text data citation Refs.: [41,42]. The assimilation data of temperature, salinity and velocity are available from JPL ECCO2 cube92 of APDRC (http://apdrc.soest.hawaii.edu/). Datasets for this research are available in these in-text data citation Refs.: [46]. All lidar data and in situ chlorophyll data are available at this site (https://doi.org/10.5281/zenodo.5771205).
Disclosures
The authors declare no conflicts of interest
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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