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Lidar technology is increasingly applied to the inversion of oceanic biological parameters and optical properties based on empirical and semi-empirical bio-optical models. However, these models cannot be directly applied to inland waters due to their complex composition, and research on the biological parameters and optical properties of inland waters remains limited. In this study, the Fernald method was employed to retrieve the vertical distribution of optical properties in Qiandao Lake for the first time using shipborne lidar data obtained in June 2019. By quantifying the depth-resolved optical contributions of biological components, the vertical distributions of chlorophyll-a concentration were mapped with greater precision. The lidar-estimated optical properties exhibited characteristic spatiotemporal distributions, which were closely related to water quality. At the inflow of Xin'an River, the attenuation and scattering coefficient showed a gradual increase with depth. At the north–south-oriented reservoir area and the outflow of Qiandao Lake, an apparently continuous subsurface layer with the maximum signal occurred at approximately 3.5 m. The vertical distributions of chlorophyll-a profiles were consistently classified as subsurface chlorophyll maxima, with the maximum value of chlorophyll-a concentration fluctuating between 4 and 12 μg/L. The subsurface phytoplankton layer was observed at water depths ranging from 1.5 to 3.5 m, with a thickness of 3 to 6 m. Furthermore, the influences of lidar ratio Sp(z) and reference value bbp(zm) were discussed as significant sources of inversion error in the Fernald method. These results indicate that lidar technology holds great potential for the long-term monitoring of lakes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The traditional way to detect the vertical structure of seawater optical properties and chlorophyll-a is mainly through shipboard discrete observations or biogeochemical-argo profiling floats, which requires considerable time to cover a limited area. In this study, the vertical distribution of seawater optical properties and chlorophyll-a concentration across two different optically contrasted sea areas from the East China Sea (ESC) to the South China Sea (SCS) were obtained for the first time using a shipboard integrated Mie–Raman–fluorescence Light Detection and Ranging (LiDAR) for large-scale observations, with a total observation distance of over 3700 km. More than 74000 LiDAR profiles were obtained from September 5 to September 15, 2020. In general, the LiDAR-estimated inherent optical properties (IOPs) and chlorophyll-a values decreased from turbid water in the ECS to clear water in the SCS. Subsurface scattering layers were often observed at depths ranging from 10 to 20 m along the SCS coast. Subsequently, the LiDAR-derived results were compared against in situ measurements. In addition, the diurnal hourly variation in IOPs and chlorophyll-a by LiDAR at a fixed coastal station was monitored for the first time, which was relatively lower in the early morning and midday yet was higher in the evening, while the relative tide height showed the reverse change trend, which revealed that the tide possibly impacted the diurnal variation in IOPs and chlorophyll-a on the SCS coast. Overall, our results indicate that the LiDAR remote sensing technique is effective and feasible to monitor large-scale and long-term subsurface phytoplankton structure over different optically contrasted sea regions, and integration of multiple detection mechanisms will enhance the monitoring capacity. [ABSTRACT FROM AUTHOR]

Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-a profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas.

Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-a profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas. [ABSTRACT FROM AUTHOR]

The vertical distribution of subsurface phytoplankton in the ocean contains key information about not only ocean ecology but also water column optical properties relevant to remote sensing. The existing methods for measuring this vertical distribution are mainly field bio-optical and biogeochemical observations, which are time- and labor-intensive and do not provide sufficient spatial coverage. Passive satellite remote sensing provides global observations of phytoplankton but offers no information on subsurface vertical structure. In this study, we made the first quantitative measurements of vertical distribution of the subsurface phytoplankton layer (SPL) in the South China Sea (SCS) using airborne lidar. A total of five lidar flight experiments were conducted between 2017 and 2019 in the SCS, and approximately 2.5 terabytes of data were obtained. A hybrid retrieval method combining the Klett method for k lidar and the perturbation method for β π was developed. The lidar-retrieved chlorophyll- a concentrations and in situ data show good agreement (R2 greater than 0.8). The mean absolute percentage error for the lidar-retrieved chlorophyll- a concentrations is less than 15%. SPLs were observed both in Sanya Bay and in the open sea near Lingshui city, Hainan Province, China. The SPLs were observed at depths between 50 and 70 m in the open sea near Lingshui city, and between 5 and 30 m in Sanya Bay. The SPL depths have spatiotemporal variability, and we analyzed the possible factors (monsoon, seafloor depth, and sea surface temperature) that influence this spatiotemporal variability. The results show that lidar technology has a great potential for wide-range and long-term monitoring of SPLs, and is a good complement for discrete in situ observations and passive satellite remote sensing. • Subsurface phytoplankton layer below 50 m was observed by lidar for the first time. • First quantification of SPL vertical structure in SCS using lidar • A hybrid retrieving method for lidar was developed. [ABSTRACT FROM AUTHOR]