Abstract Aerosols and clouds greatly affect the Earth’s radiation budget and global climate. Light detection and ranging (lidar) has been recognized as a promising active remote sensing technique for the vertical observations of aerosols and clouds. China launched its first space-borne aerosol-cloud high-spectral-resolution lidar (ACHSRL) on April 16, 2022, which is capable for high accuracy profiling of aerosols and clouds around the globe. This study presents a retrieval algorithm for aerosol and cloud optical properties from ACHSRL which were compared with the end-to-end Monte-Carlo simulations and validated with the data from an airborne flight with the ACHSRL prototype (A2P) instrument. Using imaging denoising, threshold discrimination, and iterative reconstruction methods, this algorithm was developed for calibration, feature detection, and extinction coefficient (EC) retrievals. The simulation results show that 95.4% of the backscatter coefficient (BSC) have an error less than 12% while 95.4% of EC have an error less than 24%. Cirrus and marine and urban aerosols were identified based on the airborne measurements over different surface types. Then, comparisons were made with U.S. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiles, Moderate-resolution Imaging Spectroradiometer (MODIS), and the ground-based sun photometers. High correlations ( R &gt; 0.79) were found between BSC (EC) profiles of A2P and CALIOP over forest and town cover, while the correlation coefficients are 0.57 for BSC and 0.58 for EC over ocean cover; the aerosol optical depth retrievals have correlation coefficient of 0.71 with MODIS data and show spatial variations consistent with those from the sun photometers. The algorithm developed for ACHSRL in this study can be directly employed for future space-borne high-spectral-resolution lidar (HSRL) and its data products will also supplement CALIOP data coverage for global observations of aerosol and cloud properties. 

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Development of China’s first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration

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Simulation and retrieval for spaceborne aerosol and cloud high spectral resolution lidar of China

Identification of typical dust sources in Tarim Basin based on multi-wavelength Raman polarization lidar

In order to achieve a high signal-to-noise ratio by using small laser energy and telescope aperture, we present a detection method based on Rayleigh-Brillouin scattering (RBS) for the measurement of atmospheric temperature without response functions and calibration procedures by using high spectral resolution lidar (HSRL). Different from the traditional HSRL, a Fabry-Perot interferometer (FPI) with a continuous tunable cavity and polarization optical scheme are employed in a high spectral resolution filter. In order to continuously change the resonant frequency of the FPI, an electro-optical crystal of potassium dideuterium phosphate (DKDP) with two ring electrodes is used as a continuous tunable cavity in the FPI. At each scanned frequency point corresponded with the resonant frequency of the FPI, the received signals of four discrete points on RBS are obtained. Atmospheric temperature is inverted by using a RBS model. The polarization optical scheme is used to suppress the solar background light, and improve the utilization of return signals. In detection experiment of atmospheric temperature, the detection height is 2 km at night and 1.5 km during the day by using a pulsed energy of 30 mJ and telescope diameter of 250 mm. The results are in good agreement with the data detected by radiosonde.

Absolute detection of atmospheric temperature by using a scanning Fabry-Pérot interferometer in high spectral resolution lidar

Simulating return signals of a spaceborne high-spectral resolution lidar channel at 532 nm

Capturing the as-is status of buildings in the form of 3D spatial data (e.g., point cloud or mesh) with the use of 3D sensing technologies is becoming predominant in the Architecture, Engineering, and Construction (AEC) industry. Although the act of acquiring this data has been progressively becoming more accurate and eﬃcient with the availability of oﬀ-the-shelf solutions in the market, the act of extracting from it as-is information has not seen similar advancements. State-of-the-art practice requires experts to manually interact with the spatial data in a labo-rious and time-consuming process. We propose Semantic Spray ( Semspray ), a Virtual Reality (VR) application that provides users with intuitive and handy tools to produce semantic information on as-is 3D spatial data (mesh) of buildings. The goal is to perform this task accurately and more eﬃciently by allowing users to experience, interact with, and immerse themselves in the data at diﬀerent scales. Semspray is a combination of two labeling modes: user-dynamic and user-static . In the user-dynamic mode, the user is fully immersed in the 3D mesh and has the ability to walk and teleport themselves within the model; in the user-static , the user can comfortably sit on a chair and handle a small-scale version of the 3D mesh to perform the labeling, in a similar manner to hand-held painting. We evaluated SemSpray ’s performance with a user study of ten participants and found that the combination of the diﬀerent modes is able to keep the user entertained and to limit the side-eﬀect of VR on the sensory organs, including nausea, headache, and dizziness.

https://www.research-collection.ethz.ch/bitstream/20.500.11850/586092/2/EC32022_175.pdf

Zhao Yiming

Papers

Simulating return signals of a spaceborne high-spectral resolution lidar channel at 532 nm

SemDpray: Virtual reality as-is semantic information labeling tool for 3D spatial data