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Abstract
Subsurface phytoplankton vertical structure was observed for the first time by lidar during the onset of the SCS summer monsoon. Based on the lidar data that were obtained by continuous day-and-night measurements over a two-week period, a hybrid retrieval method to determine the vertical structure of the seawater chlorophyll-a concentrations using lidar data was proposed. We compared the data obtained from the lidar retrievals with the ocean color data and studied the spatial variations and hourly diurnal variations in the subsurface chlorophyll-a maximum layer (SCML). The significant changes in the depth of the SCML in the SCS may be due to the variations in light availability and nutrient supply during the onset of the SCS summer monsoon. The preliminary results indicated that lidar measurements allow the submesoscale oceanic dynamics mechanisms to be understood from a new perspective.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Near the middle of May in each year, the southeast winds over the South China Sea (SCS), which last for nearly two months, suddenly shift to the southwest [1]. The stable, sunny and cloudy weather changes to hot, cloudy and rainy weather [2]. This rapid change is often called the outbreak of the southwest monsoon [3,4]. The southwest monsoon season is a turning point that involves a strong weather process, with a sudden change in wind direction. The wind speed and cloud cover, rainfall, humidity, solar radiation, ocean temperature, salinity and atmospheric circulation, and ocean hydrological factors change rapidly due to a series of climate feedback mechanisms that affect the SCS and East Asia [5–8]. After the onset of the SCS monsoon, its front and monsoon rain belt advance northward and accordingly, the monsoon rain belt moves from low latitudes to high latitudes, and the northeast summer monsoon occupies the entire offshore area [9,10]. It has been found that the summer monsoon over the SCS and western Pacific Ocean can even affect droughts and floods in North America [11–13]. Therefore, it is of great significance to understand the ocean ecosystem dynamics processes during the SCS southwest monsoon.
However, far from the surface phytoplankton that is “seen” by satellites, even less is known regarding the vertical structure of the subsurface phytoplankton in the lower part of the euphotic zone in the SCS [14–18]. Phytoplankton are a primary producer in ocean ecosystems and play a vital role in the marine food chain. The chlorophyll-a concentrations can be used to represent the phytoplankton biomass and, in turn, the level of primary productivity in seawater [19–22]. Typically, chlorophyll is not uniformly distributed vertically and exhibits a distinct maximum close to the base of the euphotic zone, which is called the subsurface chlorophyll maximum layer (SCML) [14]. The SCML affects the biogeochemical processes of the upper ocean [23–25]. The SCML usually appears near the nutricline, which is the seawater region where there are the greatest variations in nutrient concentrations depending on depth [26]. Generally, the formation and location of the SCML largely depend on the nutritional requirements and light availability of the resident organisms [27]. A postulated typical stable water structure has a consistent pattern in the vertical profiles of chlorophyll, phytoplankton biomass, nutrients, and light across a trophic gradient that is influenced by the vertical nutrient flux and is characterized by the average daily irradiance at the nutricline [28].
In recent years, the interest in lidar systems has been renewed, and they have been proposed as an effective tool for monitoring the upper ocean [29], including profiling seawater optical properties [30–35], subsurface phytoplankton layers [35–40], mapping bathymetry [41–43], and detecting fish schools, internal waves and bubbles [44–50]. Lidar measurements provide depth-resolved information, which can provide new insights into the vertical structures of seawater phytoplankton by using this new vertically resolved and diurnally continuous observation capacity.
In this study, we described the SCML observations by using a shipborne lidar system in the SCS during the onset of the SCS summer monsoon for the first time. The lidar configuration, study area and hybrid retrieval method for chlorophyll-a vertical structure using lidar data are described in Section 2. Subsequently, comparisons among the lidar retrievals and ocean color data, spatial variations, and diurnal hourly variations in the SCML are presented in Section 3. We summarize our findings in Section 4.
2. Study area and methods
2.1 Shipborne lidar cruise
The lidar cruise was conducted in the eastern South China Sea, as shown in Fig. 1(a). A Mie-Raman-fluorescence lidar (MRFL) was deployed aboard the Jiageng scientific expedition vessel, as shown in Fig. 1(b). The lidar was developed by the College of Optical Science and Engineering, Zhejiang University. The MRFL is composed mainly of three parts: a 532 nm Nd:YAG pulsed laser with a pulse energy of 5 mJ, repetition rate of 10 Hz, and pulse width of 8 ns; four PMT detectors (Hamamatsu, Hamamatsu Photonics Inc., Japan) for synchronously receiving parallel Mie scattering, perpendicular Mie scattering, Raman scattering, and fluorescence signals; and a four-channel analog-digital converter (ADC) (M4X.448X, Spectrum Inc., Germany) with a sample rate of 400 MS/s and bandwidth of 200 MHz, which corresponds to a vertical resolution of approximately 0.282 m in water. The entire research voyage lasted more than two weeks from May 23 to June 8. During the voyage, a fixed station was established at 111.1615°E and 19.9970°N, with two days of continuous observations from May 29 to May 30. The detailed parameters of the ship-based MRFL lidar are shown in Table 1.
Fig. 1. Shipborne lidar observations in the South China Sea during the period of the onset of the summer monsoon. (a) lidar observation tracks at different dates from May 24 to June 8 and (b) actual photo of the shipboard lidars. Station B is a fixed station that provided two days of continuous observations from May 29 to May 30.
Table 1. Detailed lidar system parameters.
2.2 Lidar inversion method
We proposed a hybrid Klett-Perturbation inversion method (K-P) [31,36] for the lidar attenuation coefficient, $\alpha $, and backscatter coefficient at 180°, ${\beta _\pi }$, and combined the Klett retrieval method [51] and the perturbation retrieval (PR) method [33]. The Klett method is a well-known analytical solution that assumes a power law relationship between $\alpha $ and ${\beta _\pi }$, and the PR method assumes that the optical parameters can be expressed as the sum of a homogenous part and variable part and then obtains the backscatter by assuming a linear regression to retrieve the homogenous part, which is followed by a perturbation signal to retrieve the variable part by using a backscatter-ratio method.
For oceanographic lidar, the depth-dependent lidar return signal can be expressed as [52]:
To proceed, $\mathrm{\alpha }$, ${\beta _\pi }$, ${b_{bp}}$ and the chlorophyll concentration can be estimated as follows [54–56], because the lidar has a large field of view, $\alpha ({532} )$ approaches the water diffuse attenuation coefficient ${k_d}({532} )$ in this study [57–60]. And ${K_d}({490} ) $ was then scaled to the lidar attenuation coefficient at 532 nm for comparisons with the ocean color data as follows [61]:
Table 2. Notations and their definitions and dimensional units used in this study.
3. Results and discussion
3.1 Comparisons between lidar retrievals and ocean color data
An example of the step-by-step lidar retrieval along a ship track in the sea water in the SCS is shown in Fig. 2. The raw lidar data in logarithmic form are shown in Fig. 2(a). It appears that the signal is strong when the lidar light just reaches the sea surface, and the signal magnitude decreases gradually as the water depth increases. The results after signal denoising and background corrections are shown in Fig. 2(b), and Fig. 2(c) is the result after the range correction process that is described by Eq. (2) in Section 2. The denoised signal is the signal after preprocessing. It contains the multi-pulse averaging procedure, smoothing procedure, and Richardson–Lucy deconvolution procedure. It is evident that the magnitude of the background signal due to sunlight and the detector dark current is approximately 3 (Fig. 2(a)) and has been removed from the original signal (Fig. 2(b)). Here, we assumed the background signal by the average of the last 100 samples of the signal. The lidar signal due to water attenuation and scattering can then be obtained after range corrections, which can eliminate the geometric influence of lidar observations. The lidar attenuation coefficient that is obtained by using the Klett method from Eq. (3) in Section 2 is shown in Fig. 2(d). The black masked region means that the inversion failed due to a low SNR or at the sea bottom. Because the lidar FOV during that experiment was very large, the lidar attenuation coefficient approached the water diffuse attenuation coefficient Kd. We then obtained the ${b_{bp}}$ and chlorophyll concentrations by using Eqs. (5)–10 described in Section 2. These results reveal that the sea water was relatively clear in the open sea area (Fig. 1) and that there may have been a subsurface scattering layer present at depths ranging from 25 m to 30 m during the experiments from May 27 to May 28 and from May 31 to June 8, while the sea water became more turbid during the experiments from May 29 to May 31, and the subsurface scattering layer became shallower at depths ranging from 5 m to 10 m in the coastal area (Fig. 1).
Fig. 2. An example of the step-by-step processing of the lidar retrieval results along a ship track in sea water in the SCS. (a) Raw lidar data, (b) after signal denoising and background corrections, (c) after range corrections, and (d) lidar-retrieved attenuation coefficients obtained by using the Klett method.
We compared the data from the lidar retrievals to the satellite ocean color data, as shown in Fig. 3. The lidar retrievals were obtained based on averaging the values that were obtained from 2 m to 3 m below the water surface. The lidar-estimated Kd490, bbp, and chlorophyll-a concentrations along the ship tracks (lines with white edges) overlapping on the basemap of satellite ocean color data are shown in Fig. 3(a), 3(b) and 3(c). The merged multi-sensor ocean color data were obtained from the GlobColor dataset (https://hermes.acri.fr/). The weekly level 3 ocean color data for Kd490, bbp, and chlorophyll-a, with a spatial resolution of 4 km, are used from May 25 to June 1, 2021, in the study. To avoid the water surface reflection effect, we chose average value of the lidar retrievals that were obtained at a water depth ranging from 1.5 m to 10 m for comparison with the satellite retrievals. We can see that there appear to be similar change trends for both the lidar retrievals and ocean color observations. The magnitudes of both the lidar-estimated and ocean color Kd490, bbp, and chlorophyll-a values are larger in the coastal regions than in the open sea regions. The ranges for the Kd490, bbp, and chlorophyll-a concentrations in open seawater are [0.03 m−1 0.06 m−1], [0.001 m−1 0.002 m−1] and [0 mg/m3 0.15 mg/m3], respectively, while the data from the coastal regions fall mainly in the ranges of [0.06 m−1 0.1 m−1], [0.002 m−1 0.006 m−1] and [0.2 mg/m3 0.5 mg/m3], respectively. The differences between the lidar-estimated and ocean color data are greater in the coastal regions than in the open sea regions, which reveals that there were larger uncertainties in the retrieval methods in the optically complex coastal waters than in the open seawater, it maybe due to that the equations. (8) and (9) in the study were not feasible for coastal waters. The regression plots for the lidar-estimated and ocean color Kd490, bbp, and chlorophyll-a values are shown in Fig. 3(d), 3(e), and 3f. The statistical analysis shows that the lidar-estimated values agree well with the ocean color data, with an R2 value of 0.75 and RMSE of 0.006 m−1, a bias of 7.3%, and a mean absolute relative error (MAPE: the average of all errors divided by the data range of actual value) of 6.5% for Kd490; with an R2 value of 0.63 and RMSE value of 0.0005 m−1, a bias of 19.3%, a MAPE of 6.7% for bbp; with an R2 value of 0.72 and RMSE value of 0.05 µg/L, a bias of 26.5%, and a MAPE of 4.9% for chlorophyll-a. The lidar-estimated values are underestimates when compared to the ocean color data, and the uncertainties increase as the Kd490, bbp, and chlorophyll-a values increase. Based on Fig. 2(d), (e) and (f), it seems that the mean relative absolute errors are probably larger than the values stated, which may be due to the multiple scattering effect on the lidar retrieval accuracies. Overall, our results showed that the hybrid retrieval method was feasible and effective for seawater chlorophyll monitoring on a large scale.
Fig. 3. Comparisons between the lidar data retrievals and satellite ocean color data. The lines with white edges show the lidar retrievals along the ship tracks, as shown in Fig. 1. The basemap shows the spatial distribution of the satellite ocean color data. (a-c) Spatial distributions of the lidar retrievals along ship tracks overlapping the ocean color data for ${k_d}490$, ${b_{bbp}}$, and chlorophyll-a, respectively; (d-f) regression plots for these data.
3.2 Spatial variations in the SCML
The vertical subsurface structures of chlorophyll-a that were retrieved by lidar data along the ship tracks shown in Fig. 1 using the K-P method in this study are shown in Fig. 4. The lidar signal could penetrate to approximately 35 m in open seawater and 25 m in coastal regions. The estimates from the open sea to the coast are significantly different in the SCS, as shown in Fig. 4. There was a continuous, strong SCML at depths from 25 to 30 m, as shown in Fig. 4(a); as the vessels traveled toward the coast, the SCML depths gradually decreased to 15 m. There was a similar change trend as the vessels traveled from south to north, as shown in Fig. 4(d). As the vessel traveled from the coast to open sea, the water clarity gradually varied from turbid to clear, and the SCML depth increased, as shown in Fig. 4(c),. This demonstrated the feasibility of using lidar technology for optical-contrast seawater. The SCML variations depend on time during a full day at a fixed station, as shown in Fig. 4(b). The SCML magnitudes were greater at night (the red regions occurred at approximately 7 m) than during the day, which revealed that the physiological status of the phytoplankton differed between day and night [64]. Overall, there maximum subsurface phytoplankton layers were usually present at depths of approximately 25–30 m in the open sea regions and at depths of approximately 10–20 m in the coastal regions of the SCS. The SCML depths in different sea regions may be determined by the nutrient and light availability and by temperature [65].
Fig. 4. Subsurface vertical structures of chlorophyll-a retrieved by lidar data along the ship tracks shown in Fig. 1. (a) lidar retrievals along Track AB from May 24 to May 28, (b) lidar retrievals for fixed Station B with continuous diurnal observations from May 29 to May 30, (c) lidar retrievals along Track BC on May 31, and (d) lidar retrievals along Track CD from June 2 to June 8.
We analyzed the sea-surface temperature (SST) and sea-surface wind speed (SSW) during the onset of the SCS summer monsoon. Both the SSW and SST data were obtained from Remote Sensing Systems (https://www.remss.com/missions/amsr/) [66]. The thickness and depth of the SCML along track CD during the onset of the SCS summer monsoon from June 2 to June 8, as well as the corresponding SSW and SST mapping are shown in Fig. 5. The temperatures decreased from the open sea to the coast; meanwhile, the SCML depths decreased and exhibited a similar variation trend. Relatively weak southwesterly monsoonal winds have less influence on the SCML depths [67,68]. This suggested that the availability of sunlight may be an important factor for the depth changes of the SCML because temperature variations usually indicate the light availability in the upper ocean. The cold upper layer in the coast may be reduced by rainfall during the onset of the SCS summer monsoon as a response to less shortwave radiation. A similar SCML pattern that is driven by light availability is shown in [69]. Another factor may be that as the location of nutrient increases from the coast to the open sea, the SCML depths increase accordingly. In the SCS, the nutrient levels generally increase with depth below the surface [70]. In contrast, the SCML thickness increased from the open sea regions to coastal regions, as shown in Fig. 5(a). The SCML in coastal areas may also be regulated indirectly by injections of nutrients from coastal currents, including river runoff and tide-induced currents, as well as the effective entrainment mixing of nutrients from the bottom due to the shallow depths [71]. Light availability and nutrient supply may also result in the SCML thickness being larger along the coast. Overall, the significant changes in the depth of the maximum subsurface phytoplankton layer in the SCS may result from light availability and nutrient supply variations during the onset of the SCS summer monsoon. These results indicated that lidar measurements allow oceanic dynamics mechanisms to be understood at the submesoscale by using a new method.
Fig. 5. Plot of the thicknesses and depths of the SCML along track CD and the corresponding SST and SSW mapping in the SCS from June 2 to June 8 during the onset of the SCS summer monsoon. (a) Plot of the thicknesses and depths of the SCML along track CD, (b) is the SST distribution in the SCS, and (c) is the SSW distribution in the SCS.
3.3 Diurnal hourly variations in the SCML
The hourly variations between the lidar-derived subsurface chlorophyll-a concentrations and tide heights along the SCS coast were compared throughout the day. The data were obtained at a fixed station (111.1615°E, 19.9970°N) with continuous observations (position B in Fig. 1). 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 7 m (blue line) during a full day on May 30 are shown in Fig. 6. The lidar-derived values were highest at 0:00, and they decreased gradually to their lowest values at approximately 12:00. After that time, the lidar-derived values increased gradually over time and reached their highest values at approximately 18:00. Then, they decreased gradually and reached their lowest values at approximately 20:00. Subsequently, they once again increased gradually over time. We analyzed the day-to-night variations in the subsurface chlorophyll-a concentrations and high-to-low tide variations, as shown in Table 3. For the day-to-night variations, we binned the data into daytime (e.g., one hour after sunrise to one hour before sunset) and nighttime (e.g., one hour after sunset to one hour before sunrise) and observed statistically significant differences in the mean values of the day and night bins for each depth. For the tidal effects, we binned the data into high- and low-tide periods. The subsurface chlorophyll-a concentrations were slightly higher at night than during the day, while the mean values were higher at ebb tides than at high tides. 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 along the SCS coast. One possible reason is that tides play an important role in the aggregation and diffusion of phytoplankton [72]. Phytoplankton may disperse with high tides because many phytoplankton may be carried into coastal 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 from the bottom due to the shallow depths [71]. Another possible reason may be due to diel vertical migration [17]. Previous studies were performed on the diel variability of chlorophyll-a in various oceanic regions [65,73–78]. The causes of diel variations remain poorly understood. These results revealed that the phytoplankton levels varied from day to night and demonstrated that lidar technology could provide new insights for monitoring the diurnal variations of phytoplankton.
Fig. 6. 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. The data were obtained at a fixed station (111.1615°E, 19.9970°N).
Table 3. Statistical analysis of the day-to-night variations in subsurface chlorophyll-a concentrations.
4. Summary and conclusion
Lidar technology was deployed to investigate for the first time the vertical structure of the SCML in the SCS during the onset of the SCS summer monsoon. There appear to be similar change trends for the lidar retrievals and ocean color data. The statistical analysis shows that the lidar estimates agrees well with the ocean color data, which reveals that the proposed hybrid retrieval method was effective for seawater chlorophyll monitoring on a large scale. It also showed that there were usually SCMLs at depths of approximately 25–30 m in the open sea regions and at depths of approximately 10–20 m in the coastal regions in the study area. The significant changes in the depth of the subsurface maximum phytoplankton layer in the SCS may result from the light availability and nutrient supply variations during the onset of the SCS summer monsoon. In addition, we obtained continuous day-and-night measurements at a fixed station and found that the diurnal hourly variations in the chlorophyll-a concentrations were relatively smaller at midday but were higher in the evening, while the relative tide heights exhibited an opposite change trend, which revealed that the tides possibly impacted the diurnal variations in chlorophyll-a concentrations along the coast of the SCS. One possible reason is that tides play an important role in the aggregation and diffusion of phytoplankton. These results suggest that lidar measurements can provide new insights for understanding oceanic dynamics mechanisms at the submesoscale. Further investigations are needed to conduct more lidar experiments in different sea regions in the future.
Funding
Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (GML2019ZD0602); National Natural Science Foundation of China (41901305, 61991453); Natural Science Foundation of Zhejiang Province (LQ19D060003).
Acknowledgments
The authors would like to thank the NASA AMSR-E Science Team, National Marine Data and Information Service, and the GlobColor (ESA DUE) for providing the data used in this study. The SSW and SST satellite data are publicly available through remote sensing systems and were sponsored by the NASA AMSR-E Science Team and NASA Earth Science MEaSUREs Program (https://www.remss.com/missions/amsr/). The ocean color satellite data are publicly available through the GlobColor dataset (https://hermes.acri.fr/). The tide height data were obtained from the National Marine Data and Information Service (http://global-tide.nmdis.org.cn).
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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