Multiple-scattering aerosol lidar inversion method

1993 ◽  
Vol 71 (1-2) ◽  
pp. 39-46 ◽  
Author(s):  
Luc R. Bissonnette ◽  
Daniel L. Hutt
Keyword(s):  
Multiple Scattering ◽  
Solution Method ◽  
Aerosol Particles ◽  
Simultaneous Detection ◽  
Inversion Method ◽  
Forward Peak ◽  
Lidar System ◽  
Scattering Phase ◽  
Scattering Phase Function ◽  
Aerosol Backscatter

The multiple-scattering contributions to lidar aerosol backscatter returns are measured by simultaneous detection at four concentric fields of view. A solution method is proposed to calculate, from the ratios of the lidar returns at the different fields of view, the range-resolved scattering coefficient. The method also provides the effective size of the aerosol particles responsible for the forward peak of the scattering phase function. Solutions from measurements performed in fog and clouds with a 1.054 μm lidar system are presented.

scholarly journals A Novel Fast Multiple-Scattering Approximate Model for Oceanographic Lidar

2021 ◽  
Vol 13 (18) ◽  
pp. 3677
Author(s):  
Zhenhua Zhang ◽  
Peng Chen ◽  
Zhihua Mao ◽  
Dapeng Yuan
Keyword(s):  
Multiple Scattering ◽  
Phase Function ◽  
Forward Scattering ◽  
Sea Surface ◽  
Lidar System ◽  
Angle Scattering ◽  
Scattering Phase ◽  
Scattering Phase Function ◽  
Rough Sea Surface ◽  
Rough Sea

An effective lidar simulator is vital for its system design and processing algorithms. However, laser transmission is a complex process due to the effects of sea surface and various interactions in seawater such as absorption, scattering, and so on. It is sophisticated and difficult for multiple scattering to accurately simulate. In this study, a multiple-scattering lidar model based on multiple-forward-scattering-single-backscattering approximation for oceanic lidar was proposed. Compared with previous analytic models, this model can work without assuming a homogeneous water and fixed scattering phase function. Besides, it takes consideration of lidar system and environmental parameters including receiver field of view, different scattering phase functions, particulate sizes, stratified water, and rough sea surface. One should note that because the scattering phase function is difficult to determine accurately, the simulation accuracy may be reduced in a complex oceanic environment. The Cox–Munk model used in our method simulates capillarity waves but ignores gravity waves, and the pulse stretching is not included. The wide-angle scattering occurs in the dense subsurface phytoplankton, which sometimes makes it hard to use this model. In this study, we firstly derived this method based on an analytical solution by convolving Gaussians of the forward-scattering contribution of layer dr and the energy density at R in the small-angle-scattering approximation. Then, the effects of multiple scattering and water optical properties were analyzed using the model. Meanwhile, the validation with Monte Carlo model was implemented. Their coefficient of determination is beyond 0.9, the RMSE is within 0.02, the MAD is within 0.02, and the MAPD is within 8%, which indicates that our model is efficient for oceanographic lidar simulation. Finally, we studied the effects of FOV, SPF, rough sea surface, stratified water, and particle size. These results can provide reference for the design of the oceanic lidar system and contribute to the processing of lidar echo signals.

Fast Lidar and Radar Multiple-Scattering Models. Part I: Small-Angle Scattering Using the Photon Variance–Covariance Method

2008 ◽  
Vol 65 (12) ◽  
pp. 3621-3635 ◽  
Author(s):  
Robin J. Hogan
Keyword(s):  
Multiple Scattering ◽  
Small Angle ◽  
Wide Angle ◽  
Photon Distribution ◽  
Spatial Variance ◽  
Angle Scattering ◽  
Photon Direction ◽  
Small Footprint ◽  
Scattering Phase ◽  
Scattering Phase Function

Abstract A fast, approximate method is described for the calculation of the intensity of multiply scattered lidar returns from clouds. At each range gate it characterizes the outgoing photon distribution by its spatial variance, the variance of photon direction, and the covariance of photon direction and position. The result is that for an N-point profile the calculation is O(N) efficient yet it implicitly includes all orders of scattering, in contrast with the O(Nm/m!) efficiency of models that explicitly consider each scattering order separately for truncation at m-order scattering. It is also shown how the shape of the scattering phase function near 180° may be taken into account for both liquid water droplets and ice particles. The model considers only multiple scattering due to small-angle forward-scattering events, which is suitable for most ground-based and airborne lidars because of their small footprint on the cloud. For spaceborne lidar, it must be used in combination with the wide-angle multiple scattering model described in Part II of this two-part paper.

scholarly journals Size distribution and scattering phase function of aerosol particles retrieved from sky brightness measurements

1994 ◽  
Vol 99 (D5) ◽  
pp. 10341 ◽  
Author(s):  
Y. J. Kaufman ◽  
A. Gitelson ◽  
A. Karnieli ◽  
E. Ganor ◽  
R. S. Fraser ◽  
...  
Keyword(s):  
Size Distribution ◽  
Phase Function ◽  
Aerosol Particles ◽  
Scattering Phase ◽  
Sky Brightness ◽  
Scattering Phase Function

scholarly journals A new airborne Polar Nephelometer for the measurement of optical and microphysical cloud properties. Part II: Preliminary tests

1997 ◽  
Vol 15 (4) ◽  
pp. 460-470 ◽  
Author(s):  
O. Crépel ◽  
J.-F. Gayet ◽  
J.-F. Fournol ◽  
S. Oshchepkov
Keyword(s):  
Wind Tunnel ◽  
Phase Function ◽  
Focal Point ◽  
Liquid Water Content ◽  
Ice Crystals ◽  
Inversion Method ◽  
Water Droplets ◽  
Scattering Phase ◽  
Cloud Particles ◽  
Scattering Phase Function

Abstract. A new optical sensor, the airborne Polar Nephelometer, has been tested in an open wind tunnel. The wind tunnel was operated in cloudy conditions including either cloud water droplets or ice crystals, or a mixture of these particles. The sensor is designed to measure the optical and microphysical parameters of cloud particles sized from a few micrometers to about 500 µm diameter. Basically, the probe measures the scattering phase function of an ensemble of cloud particles which intersect a collimated laser beam near the focal point of a paraboloidal mirror. From the measured scattering phase function the retrieval of the droplet-size spectra and subsequent derived quantities such as liquid water content and size parameters can be calculated using an inversion method. The particle phase discrimination (water droplets/ice particles) can be derived from the shape of the scattering phase function and the sensitivity of the probe allows the detection of small ice crystals (typically of 5 µm diameter). The paper describes the preliminary results obtained by the prototype version of the Polar Nephelometer in various cloudy conditions. These results are compared with direct microphysical measurements obtained by usual PMS probes also mounted in the wind tunnel. Complementary results obtained in a cold chamber are presented in order to illustrate the reliability of the Polar Nephelometer in the presence of small ice crystals.

scholarly journals High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2123 ◽  
Author(s):  
Leran Han ◽  
Chunmei Wang ◽  
Tao Yu ◽  
Xingfa Gu ◽  
Qiyue Liu
Keyword(s):  
Soil Moisture ◽  
High Precision ◽  
Satellite Data ◽  
Solution Method ◽  
Pearson Correlation ◽  
Optimal Solution ◽  
Background Knowledge ◽  
Optical Data ◽  
Inversion Method ◽  
Synthetic Aperture Radar Data

This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.

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