Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters.

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

Experimental demonstration of spectral suppression effect improvement for multi-longitudinal mode high-spectral-resolution lidar.

The multi-longitudinal mode high-spectral-resolution lidar (MLM-HSRL) is an effective technique for detecting atmospheric optical characteristics of aerosols. Due to the excessive longitudinal mode numbers, the current MLM-HSRL cannot obtain a well spectral suppression effect, which seriously affects the retrieval accuracy of the optical characteristic parameters. In this paper, a new index called Longitudinal Mode Rejection Ratio (LMRR) has been proposed to evaluate the spectral suppression effect of the MLM-HSRL; a novel mismatch error and mode control (MEMC) technique is proposed to improve the spectral suppression effect of the MLM-HSRL, which contributes to developing the scientific potential of the MLM-HSRL for aerosol remote sensing. Based on our self-developed MLM laser, through controlling the longitudinal mode frequency-pulled shift of the MLM laser, adjusting the total mismatch error, and reducing the longitudinal mode numbers, we realize the LMRR index improved from about 5 to over 30, and the working stability of the system is also promoted by decreasing the longitudinal mode numbers. The experiment well improves the spectral suppression effect and verifies the effectiveness of the proposed MEMC technique. To the best of our knowledge, for the first time, the study addresses the conundrum of the lower spectral suppression effect for the MLM-HSRL. This work would help to provide a powerful support for the high-precision, long-term, and stable operation of the MLM-HSRL in the future.

High-spectral-resolution LIDAR based on a few-longitudinal mode laser for aerosol and cloud characteristics detection.

A novel implementation of high-spectral-resolution LIDAR based on a passively Q-switched few-longitudinal mode laser (PQFLM-HSRL) is proposed, and the prototype is built for detecting aerosol and cloud characteristics. The spatial-temporal distributions of the aerosol and cloud are continuously observed by the PQFLM-HSRL for the first time, to the best of our knowledge. Based on observation, we present the retrieval results of backscatter coefficient, particle linear depolarization ratio, and LIDAR ratio, and these intensive parameters are used to classify the aerosol and cloud into different types. Particularly, we have observed mix-phased clouds. The resulting aerosol optical depths (AODs) are highly consistent with CE-318, the Sun photometer measurements of the local National Meteorological Station (NMS), which verify the retrieval accuracy and the system stability. In addition, the retrieved AODs also characterize the ambient air quality, which show a high correlation with the measured PM2.5 concentrations. The implementation of the PQFLM-HSRL provides a new method for atmospheric feature detection, which shows superior scientific potential for further study on climate change and environmental health.

Comprehensive, Continuous, and Vertical Measurements of Seawater Constituents with Triple-Field-of-View High-Spectral-Resolution Lidar.

Measuring the characteristics of seawater constituent is in great demand for studies of marine ecosystems and biogeochemistry. However, existing techniques based on remote sensing or in situ samplings present various tradeoffs with regard to the diversity, synchronism, temporal-spatial resolution, and depth-resolved capacity of their data products. Here, we demonstrate a novel oceanic triple-field-of-view (FOV) high-spectral-resolution lidar (HSRL) with an iterative retrieval approach. This technique provides, for the first time, comprehensive, continuous, and vertical measurements of seawater absorption coefficient, scattering coefficient, and slope of particle size distribution, which are validated by simulations and field experiments. Furthermore, it depicts valuable application potentials in the accuracy improvement of seawater classification and the continuous estimation of depth-resolved particulate organic carbon export. The triple-FOV HSRL with high performance could greatly increase the knowledge of seawater constituents and promote the understanding of marine ecosystems and biogeochemistry.