Lidar-Observed Diel Vertical Variations of Inland Chlorophyll a Concentration

Abstract

The diel vertical variations of chlorophyll a (Chl-a) concentration are thought of primarily as an external manifestation of regulating phytoplankton’s biomass, which is essential for dynamically estimating the biogeochemical cycle in inland waters. However, information on these variations is limited due to insufficient measurements. Undersampled observations lead to delayed responses in phytoplankton assessment, impacting accurate evaluations of carbon export and water quality in dynamic inland waters. Here, we report the first lidar-observed diel vertical variations of inland Chl-a concentration. Strong agreement with r² of 0.83 and a root mean square relative difference (RMSRD) of 9.0% between the lidar-retrieved and in situ measured Chl-a concentration verified the feasibility of the Mie–fluorescence–Raman lidar (MFRL). An experiment conducted at a fixed observatory demonstrated the lidar-observed diel Chl-a concentration variations. The results showed that diel variations of Chl-a and the formation of subsurface phytoplankton layers were driven by light availability and variations in water temperature. Furthermore, the facilitation from solar radiation-regulated water temperature on the phytoplankton growth rate was revealed by the high correlation between water temperature and Chl-a concentration anomalies. Lidar technology is expected to provide new insights into continuous three-dimension observations and be of great importance in dynamic inland water ecosystems.

1. Introduction

Phytoplankton impels biogeochemical processes in inland water environments [1,2], with major impacts on carbon and energy flux, water trophic status, and global climate change to a large extent [3,4,5,6]. Chlorophyll a (Chl-a) concentration is commonly identified as a proxy for phytoplankton biomass [7], and the vertical distributions of phytoplankton in waters tend to be heterogeneous and dynamic in many cases [8,9,10]. Basic physical and physiological processes leading to this phenomenon include swimming behaviors, buoyancy control of phytoplankton, and photoacclimation of pigment content, among which light plays a critical role [11,12]. The diel fluctuation of light is considered an important environmental cue for the underlying heterogeneity pattern [13,14]. Sunlight influences photosynthesis and water temperature, driving the macromolecule biosynthesis that is essential for the growth of phytoplankton. The alternations of day and night affect vertical physiological variations of phytoplankton in lakes and oceans [6,15]. As a consequence, observations of the diel variations of Chl-a profiles are of great significance to fully understand the role of phytoplankton in biogeochemical cycling and to dynamically estimate water quality and trophic status, primary production, and harmful algae blooms in inland water environments [3,7,16,17,18].

Most previous observations of these diel inhomogeneous phenomena in inland waters were carried out through in situ measurement or buoy detection, which advanced research on the diel distribution of Chl-a [6,19,20,21]. However, these approaches are limited by either temporal resolution or complex instrument arrangement, leading to an inadequate understanding of diel vertical variations of Chl-a in lakes or reservoirs. Though passive satellite remote sensing can detect Chl-a concentration in inland waters on a large scale with high efficiency and multispectral information, it cannot operate at night and can only detect information within one optical depth during the day, omitting vertical features of Chl-a [22,23,24,25]. Hence, there is an increasing demand for high spatiotemporal, convenient, and full-day detection of diel vertical distribution of Chl-a concentration in inland waters.

Oceanic lidar is an emerging active optical remote sensing technique [15,26,27,28], which can provide continuous and highly spatiotemporal vertical bio-optical information of phytoplankton on the upper water column to three optical depths day and night [29,30]. Yet, the application of lidar in inland waters has been almost blank in the past decades, especially quantitative detection of Chl-a concentration [12,31]. Different from studies in oceans, the optical complexity of inland waters, which is not only contributed by phytoplankton but also by inorganic suspended particulate matter and colored dissolved organic matter, restricts quantitative observations utilizing lidar [22,24]. Fortunately, a recent study has demonstrated that an integrated lidar, Mie-fluorescence-Raman lidar (MFRL), has the potential to profile Chl-a concentration in inland waters [16]. It will greatly promote the observations of diel vertical variations of phytoplankton in lakes and reservoirs.

In this paper, we report the first lidar-observed diel vertical variations of inland Chl-a concentration and analyze the effects of light-induced variations in water temperature on the variations of Chl-a. Here, we assume that the contribution ratio of the diffuse attenuation coefficient from the upper water column’s biogenic components can be estimated using the surface biogenic component contributions within acceptable error bounds. A diel experiment was conducted at a fixed observatory in a large subtropical reservoir. Chl-a concentration retrieved from MFRL developed by Zhejiang University (ZJU–MFRL) was validated with the in situ data. The availability of light and vertical variations of water temperature were recorded to further analyze the inherent relationships with the diel vertical variations of Chl-a. This study can promote the comprehension of diel activities of phytoplankton and the applications of MFRL in inland waters in future works.

2. Materials and Methods

2.1. Mie-Fluorescence-Raman Lidar System

Mie-fluorescence-Raman lidar utilized in this study was developed by Zhejiang University (ZJU–MFRL). The MFRL system originated from an elastic lidar, which had been thoroughly validated by Monte Carlo simulations and analytical models, and was subsequently upgraded to its current form [32,33]. Its simplified schematic diagram is shown in Figure 1a. ZJU–MFRL is composed of a transmitting system, a receiving system, and a control system. The transmitting system primarily includes a 532 nm diode-pumped Nd:YAG solid-state laser, a reflector, a beam expander, and a Gran laser prism [34,35]. The transmitting direction and the divergence angle of the laser are adjusted by the reflector and the beam expander, respectively. The Gran laser prism is used to improve the polarization degree [33]. Three channels are employed in the receiving system for receiving Mie scattering, fluorescence scattering, and Raman scattering. The central wavelengths of optical filters for the three channels are 532 nm, 685 nm, and 650 nm, respectively. Photodetectors are utilized to convert light signals to electrical signals [36,37]. The control system comprises an acquisition card, computer, relevant software, and auxiliary modules. The acquisition card records digital electrical signals from analog electrical signals output by the photodetectors with a sampling rate of 400 MSa/s and a bandwidth of 200 MHz. Real-time remote control and adjustment can be achieved through the software module. The auxiliary system drives the power supply and uninterrupted power supply (UPS) to ensure a stable power supply. Additionally, a thermo electric cooler (TEC) and a hygrothermograph are utilized to monitor and control the system’s temperature and humidity. A position module and external antenna provide geographic coordinates, speed, and instrument attitude angles of the system. Detailed parameters of ZJU–MFRL are displayed in

Table 1. ZJU-MFRL system parameters.

Transmitting System	Value	Receiving System	Value
Laser wavelength	532 nm	Diameter	50 mm
Pulse energy	5 mJ	Field of view	200 mrad
Pulse width	10 ns	Filter bandwidth	10 nm @532/650/685 nm
Repetition frequency	10 Hz	Electrical width	100 MHz
Divergence angle	1 mrad	Sampling frequency	400 MSa/s

. The lidar system was installed on a fixed observatory with an angle of inclination away from the influence of strong specular reflection of the water surface, as shown in Figure 1b,c.

2.2. Study Area and Auxiliary Measurements

The diel experiment was conducted at Jiekou station (29°44′N, 118°433′E) in Xinanjiang Reservoir (29°22′–29°50′N, 118°36′–119°14′E) from 17:24, 23 September to 16:51, 24 September 2021. Xinanjiang Reservoir is the largest subtropical artificial reservoir in China and a crucial water source for the Yangtze River Delta region, owning the characteristics of diverse nutrient levels, turbidity, and patterns of vertical distributions of Chl-a, which can provide advantages for detecting diel phytoplankton variations [20]. Jiekou station, located at the entrance of the riverine zone where the Xinanjiang River flows into (Figure 2), serves as the main inlet of Xinanjiang Reservoir with an average water depth of 16 m. Xinanjiang River is the principal inflow of Xinanjiang Reservoir, and the average annual inflow from Xinanjiang River accounts for over 70% of the total inflow [7,38], bringing rich nutrients, which makes phytoplankton activities vigorous at Jiekou station [39].

Synchronous in situ measurements were conducted to validate the retrieved results from MFRL. Vertical distribution of chlorophyll fluorescence (ChlF) profiles was collected by a multiparameter water quality sonde (YSI-EXO2 Multiparameter Sonde, YSI Inc., Yellow Springs, OH, USA) equipped with a chlorophyll fluorescence (ChlF) sensor every three hours starting from 20:02, 23 September to 16:51, 24 September. It meant that a total of eight sets of ChlF profiles measured by YSI-EXO2 were recorded and used to validate the lidar results subsequently. The profiles were binned to a depth resolution of 0.5 m for further analysis. However, due to the limitations of the ChlF sensor [40,41,42], the calibration of ChlF was necessary to be done by discrete Chl-a measured via spectrophotometry. Two sets of spectrophotometry results were collected to calibrate the YSI-EXO2 data. The procedures for implementing spectrophotometry are as follows. Discrete measurements of Chl-a were determined through water samplings taken from 1 m, 5 m, and 7 m below the water surface around 20:02, 23 September, and 08:17, 24 September, respectively. The water samples were initially filtered through Whatman GF/F filters. Subsequently, the samples were stored in a refrigerator at temperatures below −20 °C until arriving at the laboratory in 24 h (GB17378.7-2007 [43], Chinese Water Quality Standards). Upon returning to room temperature, Chl-a was extracted using 90% acetone according to NASA protocols and measured using a laboratory spectrophotometer (UV-1800, SHIMADZU Co., Ltd., Kyoto, Japan) at wavelengths of 630 nm, 647 nm, 664 nm, and 750 nm through absorption photometry (

Table A1. Chl-a concentrations from different depths measured at two sampling moments by UV-1800 in the laboratory.

Water Depth (m)	Sampling Moment 1	Sampling Moment 2
	20:02, 23 September 2021	08:17, 24 September 2021
	Chl-a (μg/L)	Chl-a (μg/L)
1	16.85	11.75
5	16.51	11.23
7	9.92	9.65

) [44]. ChlF profiles were calibrated by Chl-a measured through spectrophotometry with r² of 0.87 (Figure A1), indicating the accuracy of in situ detection in this work [16,45]. The vertical distribution of water temperature profiles was measured by a high-frequency buoy attached to a YSI-EXO2 multiparameter sonde equipped with a temperature sensor. The buoy was measured every three hours at the top of the hour. The comprehensive sampling results with wider coverage time were conducive to subsequent pattern analysis.

2.3. MFRL Bio-Optical Properties Retrieval Model

An MFRL bio-optical properties retrieval model was proposed for the lidar attenuation coefficient k_lidar and Chl-a concentration based on the Fernald method and bio-optical model [16,46,47]. Herein, k_lidar was retrieved first from Mie signals. The values of the diffusion attenuation coefficient K_d were approximated by the values of k_lidar. The wide field of view of 200 mrad in MFRL ensured that k_lidar was very close to the values of K_d [48,49]. Then, K_d can be calculated from the Mie signal as

$$\begin{array}{cc}\hfill K_{\mathrm{d}}(z)& =K_{\mathrm{d},\mathrm{p}}(z)+K_{\mathrm{d},\mathrm{w}}(z)\hfill \\ & =(1-R_S)K_{\mathrm{d},\mathrm{w}}(z)+{\displaystyle \frac{D(z)\Phi (z)}{\frac{D(z_c)}{K_{\mathrm{d},\mathrm{p}}(z_c)+R_S{K}_{\mathrm{d},\mathrm{w}}(z_c)}+2{\displaystyle {\int }_z^{z_c}D(z)\Phi (z)\mathrm{d}z}}}\hfill \end{array}$$

(1)

where z is the depth, D(z) is the range-corrected Mie signal, K_d,p is the diffuse attenuation coefficient of the biogenic and inorganic components, K_d,w is the diffuse attenuation coefficient of the pure water (0.0452 m⁻¹) [50], R_S is the ratio between the lidar ratios of the suspended matter R (50 sr) and water molecules R_w (233 sr) [16,50], $\Phi (z)$ can be expressed as $\exp \left[2(R_S-1){\displaystyle {\int }_z^{z_c}{K}_{\mathrm{d},\mathrm{w}}(z)\mathrm{d}z}\right]$, z_c is the boundary depth, and K_d,p(z_c) can be estimated by the slope method [51].

In Case 1 water, K_d was utilized directly to characterize Chl-a through the bio-optical model, as shown in (2)

$$K_{\mathrm{d},\mathrm{bio}}(\lambda )=\chi (\lambda )\times Chl{a}^{e(\lambda )}$$

(2)

where Chla represents Chl-a concentration, K_d,bio(λ) is the particle diffuse attenuation coefficient calculated as a function of Chl-a, χ(λ) and e(λ) are scaling factors, which are 0.0474 and 0.6667 at 532 nm, respectively [47]. The total K_d is generally regarded as the sum of K_d,w, K_d,bio, and K_d,x, which represent the optical properties of pure water, the combined impacts of all biogenic components, and the influences of all inorganic components, respectively. Here, we assume that the contribution ratio of the diffuse attenuation coefficient from the upper water column’s biogenic components can be estimated using the surface biogenic component contributions within acceptable error bounds. In other words, the ratio K_d,bio/(K_d − K_d,w) can be regarded as a constant that can be acquired from surface values to some extent. Consequently, Chl-a concentrations can be derived after calibration at the water surface using the Raman channel signal and fluorescence channel signal, which can be depicted as

$$Chla(z)=F\left\{{\displaystyle \frac{K_{\mathrm{d},\mathrm{bio}}(0)}{K_{\mathrm{d}}(0)-K_{\mathrm{d},\mathrm{w}}(0)}}\left[K_{\mathrm{d}}(z)-K_{\mathrm{d},\mathrm{w}}(z)\right]\right\}$$

(3)

where z is depth, F{*} is the bio-optical model to transfer the attenuation to the Chl-a, K_d,bio(0) is the Chl-a component of the diffuse attenuation coefficient from the surface Chl-a, which is calculated from the bio-optical model.

2.4. Statistical Analysis

Statistical analysis was conducted to verify the accuracy of the lidar-retrieved Chl-a concentration. The root-mean-square relative difference (RMSRD) was utilized here for reflecting the ZJU-MFRL performance realistically, that is

$$\mathrm{RMSRD}(\%)=100\sqrt{{\displaystyle \frac{{\displaystyle {\sum }_{i=1}^N{(x_i/{\tilde{x}}_i-1)}^2}}{N}}}$$

(4)

where N is the total number of sampling points, $x_i$ is the lidar-measured value, and ${\tilde{x}}_i$ is the in situ-measured value. In addition, Pearson’s correlation coefficient and paired sample t-test were utilized to analyze correlation. Notations and their definitions and dimensions used in this paper are shown in

Table A2. Used notations, definitions and dimension in the manuscript.

Notations	Definition	Dimension
klidar	Lidar attenuation coefficient	m−1
Kd	Diffusion attenuation coefficient	m−1
Kd,bio	Diffusion attenuation coefficient due to biogenic components	m−1
Kd,w	Diffusion attenuation coefficient due to water	m−1
Kd,x	Diffusion attenuation coefficient due to inorganic components	m−1
Kd,p	Diffusion attenuation coefficient due to biogenic and inorganic components	m−1
χ(λ)	A linear factor that relates Chl to Kd,bio	N/A
e(λ)	An exponential factor that relates Chl to Kd,bio	N/A
D(z)	The range-corrected Mie signal	W·m2
z	Water depth	m
zc	Boundary depth in the retrieval process	m
R	Lidar ratios of the suspended matter	sr
Rw	Lidar ratios of the water molecules	sr
RS	The ratio between the lidar ratios of the suspended matter and water molecules	N/A
Chla	Chl-a concentration	μg/L
F{*}	The bio-optical model that transfers the attenuation to the Chl-a	N/A
N	The total number of sampling points	N/A
xi	The lidar-measured Chl-a concentration	μg/L
${\tilde{x}}_i$	The in situ measured Chl-a concentration	μg/L

.

3. Results

3.1. Consistency Check

The performance of ZJU-MFRL was estimated from the detection accuracy of Chl-a concentration. In situ measurements of Chl-a concentration were carried out at eight different moments (T1-T8) to validate the reliability of lidar-retrieved Chl-a concentration, as shown in Figure 3. In the general case, the results showed that the lidar-retrieved Chl-a concentration matched relatively well with the in situ data in terms of variation trends and values at almost all moments. It could be found that there were relatively obvious overestimations of approximately 3.7 μg/L with RMSRD of 14.7% at moment T2 and of approximately 3.0 μg/L with RMSRD of 18.1% at moment T4. These overestimations may be due to the overestimations of K_d caused by a biased lidar ratio and multiple scattering effects on lidar retrieval. An underestimated lidar ratio, along with spot divergence that exceeds the received field of view with depth, may both contribute to an overestimation of K_d [49,52]. In contrast, a slight underestimation of approximately 2.0 μg/L with RMSRD of 6.1% occurred at moment T6. This deviation may be generated by the increased phytoplankton content and relatively stable inorganic matter at the depth of ~2.5 m, which slightly deviated from our hypothesis [16].

Furthermore, the consistency between Chl-a concentration from ZJU-MFRL and in situ measurement was analyzed in Figure 4. There was a total of 72 sampling points verified with 0.83 and RMSRD of 9.0%, and most retrieved results were within the 20% error band, indicating the dispersion of the two groups of data was rather small. One should be noted that some biases were a bit obvious when the in situ values were relatively low (~10 μg/L), which may indicate that the relatively stable inorganic matter would have effects on the derived results at lower Chl-a concentrations under the retrieval method in this paper [9,16]. The slope and the ordinate at the origin from the regression relationship also showed that Chl-a was slightly overestimated or underestimated by MFRL accordingly when it was relatively low or high, which was consistent with the analysis about Figure 3. Overall, the detection results from ZJU-MFRL remarkably demonstrated that MFRL could restore the original spatiotemporal distribution of Chl-a concentrations.

3.2. Diel Vertical Variations of Inland Chl-a Concentration

The diel continuous measurement with ZJU-MFRL was carried out from 23 September to 24 September 2021, and the depth-resolved Chl-a concentrations retrieved from MFRL data are shown in Figure 5a. Sunset and sunrise were at 17:59 on 23 September and 05:53 on 24 September, respectively. The black regions were not considered due to the low SNR. Though the maximum penetration depth for MFRL was approximately 5.5 m, the detection optical depth was almost up to three optical depths (red line in Figure 5a), beyond which the lidar signal was generally not usable due to low SNR, indicating that the penetration depth basically reached the detection limit of lidars [29]. The relatively shallow penetration performance here was the result of the relatively high Chl-a concentration and relatively low water transparency at Jiekou station [12,16]. During the experiment period, Chl-a concentrations had noticeable variations on both temporal and spatial scales. The subsurface phytoplankton layer was first observed just after sunset, with a depth of ~4 m and a magnitude of ~20 μg/L. As the night deepened, the vertical distribution of Chl-a gradually became relatively homogeneous in the upper water column and developed towards the form that decreased with depth. This state remained until just before sunrise the next day, when Chl-a concentration fluctuated around 13 μg/L. As the sun rose, Chl-a concentration at all depths showed an obvious decline, and by around 08:00 the values of Chl-a had dropped to ~11 μg/L. Soon after, Chl-a concentrations gradually rebounded with a relatively uniform vertical distribution. Then, the second observed subsurface phytoplankton layer occurred after 15:00, and it lasted for more than an hour with a depth of ~4.5 m and a magnitude of ~22 μg/L. At the end of the experiment, there was a sporadic period of subsurface phytoplankton layer with Chl-a concentrations becoming lower gradually.

Lidar-retrieved Chl-a concentrations at different depths were also plotted according to the timeline in Figure 5b. Chl-a concentrations from the detected upper (0.7–1 m), the middle (2–3 m), and the bottom (4–5 m) layer were depicted to clearly observe the subtle and rapid vertical variations of Chl-a, respectively. The values and variations of Chl-a from the upper and middle layers were almost consistent throughout the experiment, except for the periods in which subsurface phytoplankton emerged. However, when compared to the bottom layer, these variations were not exactly the same. Chl-a concentrations from the bottom layer showed greater fluctuation than those from the upper water column during the detection period, which was relatively higher when the subsurface phytoplankton layer occurred and relatively lower at almost other times.

3.3. The Relationships between Vertical Variations of Phytoplankton and Water Column Temperature

Light is essential for photosynthesis, which is present in periodic seasonal and diel cycles. Meanwhile, light is also responsible for the rise in water temperature, affecting the growth and division of phytoplankton [1,53]. To further understand the potential causes of the diel vertical variations of phytoplankton, water temperature together with its anomaly were displayed in Figure 6a,b. The water temperature, measured every three hours, was subtracted by its mean values at each depth to obtain the anomaly. Just after the sunset, the water temperature decreased with depth in the upper layer. Yet, there was an obvious uplift at ~4 m between 18:00 and 19:00, 23 September. The first observed subsurface phytoplankton layers in Figure 5 might be closely related to the temperature anomaly, as shown in Figure 6b. Thereafter, as the night fell, the water temperature dropped over time and depth in the absence of solar radiation. The downward trends of Chl-a concentration in the upper, middle, and bottom layers were almost consistent with it at this time. In addition, the relatively lower values of Chl-a in the bottom layer correspond to the relatively lower water temperature. Although the sun rose at 05:53 on 24 September, the water temperature did not rise immediately, and the negative temperature anomaly was at its lowest until around 08:00. At the same time, Chl-a concentration at all depths reached the lowest values after sunrise. Then, with the strengthening of the solar radiation, the water temperature together with Chl-a concentration increased from the water surface. Afterward, at around 15:00, the water temperature from 3 m to 5 m started to be a bit higher than before, with obvious positive temperature anomalies appearing. It was at the same time that the second subsurface phytoplankton layer occurred.

Anomaly of Chl-a concentration was also calculated together with that of water temperature in Figure 6c to look for the variations in both over time. For each sampling moment, the two anomalies showed the same variation trend. At ~19:00 on September 23, the Chl-a concentration anomaly was relatively high (15.6%) and the temperature anomaly also displayed a relatively high level (0.4%). The two anomalies fell together over time until around 07:00 on September 24, with a Chl-a concentration anomaly of −26.6% and a water temperature anomaly of −1.0%. Subsequently, as illustrated in Figure 5b and Figure 6b, anomalies were both rising until the evening came, with the growth in Chl-a concentration (22.0%) and the increase of water temperature (1.9%) at ~16:00. It was interesting to find that the longest box chart of Chl-a concentration anomaly appeared around 16:00, when the second subsurface phytoplankton layer formed.

The relationship between the anomalies of Chl-a concentration and water temperature was further analyzed in Figure 7. In the general case, the diel variation of Chl-a concentration was positively correlated with that of water temperature. The results showed that their variations were highly correlated (r = 0.72, p < 0.01), demonstrating that diel variations of Chl-a were possible outcomes of water temperature changes due to solar radiation. Meanwhile, it was distinctly observed that the closer to sunrise, the greater the negative anomalies, and the closer to sunset, the greater the positive anomalies.

4. Discussion

Lidar detection of phytoplankton has flourished in recent years. However, in stark contrast to the relatively broad applications in oceans [49,51,54,55], there were only a handful of applications of lidar in inland waters in the past decades, especially quantitative detection of Chl-a concentration. Chen et al. utilized shipborne lidar to detect subsurface phytoplankton layers in artificial reservoirs [31]. They conducted a qualitative comparison with in situ results, demonstrating the feasibility of using lidar to detect phytoplankton layers in inland waters. Moore et al. reported a cyanobacteria bloom in a natural lake and analyzed the extensive relationship between vertical phytoplankton populations and cross-polarized channel signal strength measured by airborne polarization lidar qualitatively [12]. This pioneering work demonstrated the ability of lidar to characterize the various forms of vertical Chl-a profiles in inland waters. Furthermore, it has been proven that the Mie-fluorescence-Raman lidar has the potential to observe vertical Chl-a concentration distributions quantitatively in inland waters in our previous study [16]. Herein, we modified the bio-optical model proposed by Morel and Maritorena on the basis that the vertical variations of the optical properties can respond to the vertical variations of Chl-a [12,31,47]. By obtaining the contribution ratio from Raman normalized fluorescence signals, we corrected for the impacts of nonalgal factors within an acceptable range of error. Then, the feasibility of MFRL was further proven. The correlation of Chl-a concentration between ZJU-MFRL and in situ measurements in this paper was a bit lower than that of 0.92 from the previous four-month shipboard experiment, which may be due to the relatively concentrated distribution of the data, spreading over 10 μg/L to 18 μg/L (Figure 4). Nevertheless, the RMSRD of 9.0% here was relatively better than that of 16.6%, which may be a result of our hypothesis being more in line with the water bio-optical properties in this experiment. Although there were some relatively obvious overestimations at moments T2 and T4 in Figure 3, the vertical trends of Chl-a for lidar and in situ instruments were consistent, which indicated our analysis and conclusions did not deviate from the right direction. The penetration depth of lidar technology is also a notable problem. Due to the SNR of lidar systems, the detection depth of lidar is usually within three optical depths [29], ranging from tens of meters in the relatively clean ocean to several meters in rather turbid lakes [12,16,49]. At such penetration depths, lidars are sometimes difficult to make complete observations of biogeochemical phenomena, such as SPL, in water columns. This poses a challenge to the evolution of future lidar transmission and detection systems. Anyway, the combined utilization of Mie, fluorescence, and Raman channel signals complements their weakness in Chl-a concentration detection in inland water, enabling the quantitative diel vertical measurements and further cooperation with other synchronous technologies [29,56,57,58]. Meanwhile, MFRL also has the advantages compared with other different platform-based or complex design lidars due to its high spatial and temporal resolution, easy deployment, and lower cost [23,26,59,60,61].

The underlying pattern in the diel vertical variations of Chl-a concentration is another focus in this paper. Different phytoplankton species exhibit varied responses to diel changes [19,53,62]. In the general case, the phytoplankton community in the water column is of great diversity, making it difficult to estimate the response of all phytoplankton to diel variations in a given area based on a single population alone. Previous studies have shown that for specific phytoplankton populations, such as certain cyanobacteria and diatoms, Chl-a reaches its minimum near sunrise and its maximum near sunset [13,53]. The results presented in this paper are consistent with this trend, aiding in assessing regional phytoplankton compositions and understanding the ecological implications of diel phytoplankton variations. Therein, light is the fundamental factor driving this phenomenon. On one hand, light allows phytoplankton to photosynthesize during daytime, promoting biomass accumulation and phytoplankton growth and further boosting the accumulation of Chl-a [21,63]. As shown in Figure 7, Chl-a concentration is obviously regulated by the alternation of day and night. On the other hand, solar radiation causes variations in water temperature. Due to the dynamics of the water column, the water body would exhibit mixing, thermal stratification, or temperature anomalies at certain depths. Proper water temperature is conducive to phytoplankton growth and can accelerate the phytoplankton growth rate [5,64]. In this study, the significant fluctuation in bottom Chl-a concentration observed clearly indicates that this is an effect of the corresponding water temperature (Figure 5b and Figure 6b). That is, the Chl-a concentration at different depths changes positively with water temperature. Thermal stratification is also intimately linked with the formation of subsurface phytoplankton layers [20]. For subtropical monomictic reservoirs like Xinanjiang Reservoir, the thermal stratification of the upper water column is gradually eroded in autumn, turning into a mixing distribution [65,66,67]. Likewise, the vertical distributions of phytoplankton tend to be uniform in the upper water column in most cases in this season, accompanying some subsurface phytoplankton layers. The distribution and anomalies of water temperature provided a sound explanation for the lidar-observed diel vertical variations of Chl-a concentration. Therefrom, we can see that lidar captured the high spatiotemporal resolution changes in great detail, which could provide new insights into bio-optical vertical structures in inland waters. This dataset would complement and promote studies in diel variations of phytoplankton in inland waters.

5. Conclusions

In this paper, we report the first MFRL observation about diel vertical distributions of Chl-a profiles in inland water. A more particular knowledge of the performance of MFRL was acquired in this diel observation. Although some biases were present in the MFRL-detected Chl-a profiles compared to the in situ data due to some assumptions adopted in this study, the strong agreement with r² of 0.83 and RMSRD of 9.0% between the lidar-retrieved and in situ measured Chl-a concentration verified the feasibility of the MFRL bio-optical retrieval method, and the diel vertical Chl-a profiles observation was realized at approximate cost. This result indicated that Mie–fluorescence–Raman lidars can monitor the diel vertical distribution of phytoplankton exclusively and continuously in inland waters. Passive remote sensing data can provide large-scale and high-frequency views for near-surface environmental changes. However, it cannot offer adequate vertical information at night, leading to incomplete cognition of diel phytoplankton variations [15,26]. Buoy detection in inland waters can obtain accurate Chl-a profiles and other synchronous environmental information. Yet, the observation interval is generally recorded in hours, which may ignore the dynamic variations occurring in a short period [21,38,68]. The continuous monitoring ability and high time resolution of MFRL can complement these shortcomings and provide new insights into diel bio-optical vertical structure observations. More experiments will be carried out in more water bodies to further expand the applications of this technology in the coming works.

This study also described the variations of phytoplankton under the day-night alternation. Despite the upper water column tending towards mixing in autumn, an obvious subsurface phytoplankton layer together with maximum Chl-a values occurred during the experiment period with a depth of ~4 m near sunset. Moreover, the minimal Chl-a values were observed nearly two hours after sunrise, indicating changes in water temperature due to solar radiation affected phytoplankton growth rates. The water temperature variations also agreed well with the Chl-a concentration variations. These results revealed that light availability and water temperature were powerful driving factors for the vertical diel variations of Chl-a. Light and water temperature influence photosynthesis in periodic seasonal and diel cycles. Although seasonality of light and water temperature is typically resolved in ocean biogeochemical/ecosystem models because of its significance for seasonal succession and biogeography of phytoplankton, the diel cycle is often not explicitly addressed. The mechanisms of phytoplankton competition [1], energy storage [6], and physiological behaviors [13] are not yet well understood. Mie–fluorescence–Raman lidars create a new approach to observing diel heterogeneous Chl-a distribution. More detailed research will be conducted in the future in combination with other advanced techniques, contributing to clarifying the subtle temporal variations.