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In recent years, the applications of lidars for remote sensing of the environment have been expanding and deepening. Among them, continuous-wave (CW) range-resolved (RR) S-lidars (S comes from Scheimpflug) have proven to be a new and promising class of non-contact and non-perturbing laser sensors. They use low-power CW diode lasers, an unconventional depth-of-field extension technique and the latest advances in nanophotonic technologies to realize compact and cost-effective remote sensors. The purpose of this paper is to propose a generalized methodology to justify the selection of a set of non-energetic S-lidar parameters for a wide range of applications and distance scales, from a bench-top test bed to a 10-km path. To set the desired far and near borders of operating range by adjusting the optical transceiver, it was shown how to properly select the lens plane and image plane tilt angles, as well as the focal length, the lidar base, etc. For a generalized analysis of characteristic relations between S-lidar parameters, we introduced several dimensionless factors and criteria applicable to different range scales, including an S-lidar-specific magnification factor, angular function, dynamic range, "one and a half" condition, range-domain quality factor, etc. It made possible to show how to reasonably select named and dependent non-energetic parameters, adapting them to specific applications. Finally, we turned to the synthesis task by demonstrating ways to achieve a compromise between a wide dynamic range and high range resolution requirements. The results of the conducted analysis and synthesis allow increasing the validity of design solutions for further promotion of S-lidars for environmental remote sensing and their better adaptation to a broad spectrum of specific applications and range scales. [ABSTRACT FROM AUTHOR]

The article proposes a methodology for analyzing the performance of S-lidars (S comes from Scheimpflug) as a new class of environmental remote sensors operating under conditions of wide variability of optical weather and sky background brightness. The novelty of the problem statement, the methods used and the results obtained are determined by their application to laser sensing systems with unconventional design principles and the consequent need to revise the traditional ways of assessing their potential capabilities. The research method is based on a dimensionless-parametric approach, which allows comparing phenomena and systems of different scales and combining complementary characteristics and parameters. Effects of the dimensionless optical weather factor on lidar potential are shown being investigated under various environmental conditions, from the clear atmosphere through haze and mist to fog when probing in Vis/SWIR spectral bands and using Si/InGaAs detector arrays. It is shown exactly how and to what extent the significant differences in their spectral sensitivity and internal noise parameters are susceptible to the wide spectral and energy variability of the sky background brightness observed at very different angles to the Sun. A detailed analysis of the two most important influencing factors within the system, "S-Lidar instrument + Optical weather + External background source", taking into account their wide variability, allowed us to describe their joint nonlinear influence and, thus, to anticipate the imposed limitations. The proposed dimensionless-parametric concept for predicting the potential capabilities of S-lidars with Si/InGaAs arrays is aimed at expanding applications of this rapidly developing class of remote sensors in a wide variety of environments. [ABSTRACT FROM AUTHOR]

In order to conduct more thorough research on the structural characteristics of the atmosphere and the distribution and transmission of atmospheric pollution, the use of remote sensing technology for multi-dimensional detection of the atmosphere is needed. A light-weight, low-volume, low-cost, easy-to-use and low-maintenance mini Infrared Lidar (mIRLidar) sensor is developed for the first time. The model of lidar is established, and the key optical parameters of the mIRLidar are optimized through simulation, in which wavelength of laser, energy of pulse laser, diameter of telescope, field of view (FOV), and bandwidth of filter are included. The volume and weight of the lidar system are effectively reduced through optimizing the structural design and designing a temperature control system to ensure the stable operation of the core components. The mIRLidar system involved a 1064 nm laser (the pulse laser energy 15 μJ, the repetition frequency 5 kHz), a 100 mm aperture telescope (the FOV 1.5 mrad), a 0.5 nm bandwidth of filter and an APD, where the lidar has a volume of 200 mm × 200 mm × 420 mm and weighs about 13.5 kg. It is shown that the lidar can effectively detect three-dimensional distribution and transmission of aerosol and atmospheric pollution within a 5 km detection range, from Horizontal, scanning and navigational atmospheric measurements. It has great potential in the field of meteorological research and environmental monitoring. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LIDAR) is an active remote sensing system. Lidar echo signal is non-linear and non-stationary, which is often accompanied by various noises. In order to filter out the noise and extract valid signal information, a suitable method should be chosen for noise reduction. Some denoising methods are commonly used, such as the wavelet transform (WT), the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and their improved algorithms. In this paper, a new denoising method named the WT-VMD joint algorithm based on the sparrow search algorithm (SSA), for lidar signal is selected by comparative experiment analysis. It is shown that this method is the most suitable one with the maximum signal-to-noise ratio (SNR), the minimum root-mean-square error (RMSE), and a relatively small indicator of smoothness when it is used in three kinds (50, 100, and 1000 pulses) of simulate lidar signals. The SNR is increased by 138.5%, 77.8% and 42.8% and the RMSE is decreased by 81.8%, 72.0% and 68.8% when being used to the three kinds of cumulative signal without pollution. Then, the SNR is increased by 83.3%, 60.4% and 24.0% and the RMSE is decreased by 70.8%, 66.0% and 50.5% when being used to the three kinds of cumulative signal with aerosol and clouds. The WT-VMD joint algorithm based on SSA is used in the denoising process for the actual lidar signal, showing extraordinary denoising effect and will improve the inversion accuracy of the lidar signal. [ABSTRACT FROM AUTHOR]

The satellite-based estimation of dry PM 2.5 mass concentration near the surface is a big challenge in the aerosol remote sensing fields, but urgently needed by the environmental monitoring. We present an experimental validation of a physical PM 2.5 remote sensing (PMRS) method which is not dependent on geographical location, based on ground-based remote sensing measurements at Jinhua City, a typical middle size city in East of China. The PMRS method is designed to employ currently available satellite remote sensing parameters as many as possible, including aerosol optical depth (AOD), fine mode fraction (FMF), planetary boundary layer height (PBLH) and atmospheric relative humidity (RH), and thus be capable of deriving PM 2.5 from instantaneous remote sensing measurements under different pollution levels. The key processes of the PM 2.5 method including size cutting, volume visualization, bottom isolation and particle drying are directly validated by comparing with reference parameters. We found that the size cutting of the PMRS method has a significant bias (about 0.86) resulting from the use of fine mode fraction to estimate PM 2.5 among all size of aerosol particles, which should be systematically corrected. The validation results of the volume visualization and particle drying of the PMRS method are quite satisfied while the bottom isolation procedure brings currently the maximum uncertainty to the PM 2.5 remote sensing. The improved PMRS method shows good performance on the remote sensing of hourly PM 2.5 with an average error of about 38 μg/m 3 and relative error of about 31%. The correlation coefficient between remote sensing and in situ hourly PM 2.5 is about 0.67 with a linear slope of 1.03 and intercept of 11 μg/m 3 while the means are very close (110.7 μg/m 3 versus 118.6 μg/m 3 ). The validation study also helps find out future improvement directions and demonstrates the possible application to ground-based remote sensing data. [ABSTRACT FROM AUTHOR]