Here we use the state-of-the-art invariant imbedding T-matrix method to theoretically assess the backscattering linear depolarization ratio (LDR) of nonspherical particles in a super-ellipsoidal shape space. Super-ellipsoids have inherent flexibility to model the particle aspect ratio, roundness, and concavity, these being salient characteristics of most atmospheric particles (e.g., sea salt and dust aerosols). The complex refractive index of super-ellipsoids was set up with the real part ranging from 1.1 to 2.0 and the imaginary part from 10⁻⁷ to 0.5. To constrain the computational burden, the maximum size parameters for spheroids and super-ellipsoids were set as 100 and 50, respectively. From the LDRs of spheroids, we found that enhanced LDRs (>~60%) are common for optically soft particles. However, as the real part of the refractive index increases (larger than ~1.33), the enhanced LDRs (>~60%) are in high probability observed for nearly-spherical particles, and then disappear as the refractive index exceeds 1.7. To produce the enhanced LDRs, the imaginary part of the refractive index should also be less than ~0.01 such that the backscattered waves from particle-to-air transmission have sizable contributions, as the external reflection of spheroids produces no depolarization. This finding has particular relevance to LiDAR observations of atmospheric particles because the refractive index of most aerosols and hydrometeors at the LiDAR wavelength (e.g., 0.532μm) locates in this region, and aerosols and hydrometeors could have nearly-spherical morphologies. From the LDRs for general super-ellipsoids, we found that the enhanced LDRs (>~60%) exist for nearly-spherical particles with the aspect ratio close to unity, but disappear for super-ellipsoids with an aspect ratio at unity. In addition, the LDRs trend to decrease as the real part of the refractive index increases for convex super-ellipsoids, but show different features for concave super-ellipsoids. Furthermore, super-ellipsoids with different roundness parameters have a distinct dependence on the aspect ratio, which is significantly different from spheroids. The results presented here provide comprehensive references for understanding the LDR change of atmospheric aerosols as the particle shape and refractive index for interpreting LiDAR backscattering signals.

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 > 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.

Lidar techniques present a distinctive ability to resolve vertical structure of optical properties within the upper water column at both day- and night-time. However, accuracy challenges remain for existing lidar instruments due to the ill-posed nature of elastic backscatter lidar retrievals and multiple scattering. Here we demonstrate the high performance of, to the best of our knowledge, the first shipborne oceanic high-spectral-resolution lidar (HSRL) and illustrate a multiple scattering correction algorithm to rigorously address the above challenges in estimating the depth-resolved diffuse attenuation coefficientK_dand the particulate backscattering coefficientb_bpat 532 nm. HSRL data were collected during day- and night-time within the coastal areas of East China Sea and South China Sea, which are connected by the Taiwan Strait. Results include vertical profiles from open ocean waters to moderate turbid waters and first lidar continuous observation of diel vertical distribution of thin layers at a fixed station. The root-mean-square relative differences between the HSRL and coincident in situ measurements are 5.6% and 9.1% forK_dandb_bp, respectively, corresponding to an improvement of 2.7–13.5 and 4.9–44.1 times, respectively, with respect to elastic backscatter lidar methods. Shipborne oceanic HSRLs with high performance are expected to be of paramount importance for the construction of 3D map of ocean ecosystem.

One of the challenges in surface defects evaluation of large fine optics is to detect defects of microns on surfaces of tens or hundreds of millimeters. Sub-aperture scanning and stitching is considered to be a practical and efficient method. But since there are usually few defects on the large aperture fine optics, resulting in no defects or only one run-through line feature in many sub-aperture images, traditional stitching methods encounter with mismatch problem. In this paper, a feature-based multi-cycle image stitching algorithm is proposed to solve the problem. The overlapping areas of sub-apertures are categorized based on the features they contain. Different types of overlapping areas are then stitched in different cycles with different methods. The stitching trace is changed to follow the one that determined by the features. The whole stitching procedure is a region-growing like process. Sub-aperture blocks grow bigger after each cycle and finally the full aperture image is obtained. Comparison experiment shows that the proposed method is very suitable to stitch sub-apertures that very few feature information exists in the overlapping areas and can stitch the dark-field microscopic sub-aperture images very well.

Cloud and aerosol contribute with great uncertainty in Earth’s radiative budget. There is an urgent need for global 3-D observation of these atmospheric constituents. High-spectral-resolution Lidar (HSRL) can obtain vertical atmosphere profile with high accuracy, hence several space-borne HSRLs are planned to launch in few years. However, as far as we know, the performance evaluation of space-borne HSRL has not been reported yet. In this paper, we present the characteristics of a new designed space-borne HSRL for aerosol and cloud optical property profiling (ACHSRL), which is part of the Aerosol & Carbon Detection Lidar (ACDL) developed in China. The ACHSRL is essentially similar to the famous Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), which is on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). Moreover, the ACHSRL employs an iodine absorption filter as the spectral discriminator. The atmospheric optical properties data observed by CALIOP is used to estimate the performance of ACHSRL. We chose the level 2 profile data (version 4.10) in South Japan in June 2015 to compare the detection uncertainty of ACHSRL and CALIOP. The simulation calculates the uncertainties of ACHSRL and makes a statistic analysis. The analysis result demonstrates that 73.63% of the backscatter coefficient uncertainties are below 40% for ACHSRL. By contrast, the number is 30.72% for CALIOP. As for absolute extinction coefficient errors, the statistics shows that 76.01% of the extinction coefficient uncertainties are lower than 0.2 km⁻¹ for ACHSRL, while that for CALIOP are 56.97%. The assessment shows that ACHSRL could measure the particulate optical properties with better accuracy and compared with CALIOP. The estimation in this study reveals that the next generation space-borne HSRLs have a promising future.

A compact quadriwave lateral shearing interferometer (QWLSI) with strong adaptability and high precision is proposed based on a novel randomly encoded hybrid grating (REHG). By performing the inverse Fourier transform of the desired $\pm 1$ Fraunhofer diffraction orders, the amplitude and phase distributions of the ideally calculated quadriwave grating can be obtained. Then a phase chessboard is introduced to generate the same phase distribution, while the amplitude distribution can be achieved using the randomly encoding method by quantizing the radiant flux on the ideal quadriwave grating. As the Faunhofer diffraction of the REHG only contains the $\pm 1$ orders, no order selection mask is ever needed for the REHG-LSI. The simulations and the experiments show that the REHG-LSI exhibits strong adaptability, nice repeatability, and high precision.