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A Semianalytic Monte Carlo Simulator for Spaceborne Oceanic Lidar: Framework and Preliminary Results
- Source :
- Remote Sensing, Vol 12, Iss 2820, p 2820 (2020), Remote Sensing; Volume 12; Issue 17; Pages: 2820
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- Spaceborne lidar (light detection and ranging) is a very promising tool for the optical properties of global atmosphere and ocean detection. Although some studies have shown spaceborne lidar’s potential in ocean application, there is no spaceborne lidar specifically designed for ocean studies at present. In order to investigate the detection mechanism of the spaceborne lidar and analyze its detection performance, a spaceborne oceanic lidar simulator is established based on the semianalytic Monte Carlo (MC) method. The basic principle, the main framework, and the preliminary results of the simulator are presented. The whole process of the laser emitting, transmitting, and receiving is executed by the simulator with specific atmosphere–ocean optical properties and lidar system parameters. It is the first spaceborne oceanic lidar simulator for both atmosphere and ocean. The abilities of this simulator to characterize the effect of multiple scattering on the lidar signals of different aerosols, clouds, and seawaters with different scattering phase functions are presented. Some of the results of this simulator are verified by the lidar equation. It is confirmed that the simulator is beneficial to study the principle of spaceborne oceanic lidar and it can help develop a high-precision retrieval algorithm for the inherent optical properties (IOPs) of seawater.
- Subjects :
- 010504 meteorology & atmospheric sciences
Light detection
Scattering
Science
Monte Carlo method
spaceborne oceanic lidar
semianalytical Monte Carlo
Ranging
IOPS
lidar signal
01 natural sciences
010309 optics
Atmosphere
Lidar
0103 physical sciences
System parameters
General Earth and Planetary Sciences
Environmental science
atmosphere-ocean
0105 earth and related environmental sciences
Remote sensing
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 12
- Issue :
- 2820
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....92bbe630663fcd5e4b8ad91faf00fcbb