Parallel estimation of path-integrated concentration and vapor absorptivity using topographic backscatter lidar

R. E. Warren; R. G. Vanderbeek

doi:10.1117/12.620454

30 August 2005 Parallel estimation of path-integrated concentration and vapor absorptivity using topographic backscatter lidar

R. E. Warren, R. G. Vanderbeek

Author Affiliations +

Proceedings Volume 5887, Lidar Remote Sensing for Environmental Monitoring VI; 58870Q (2005) https://doi.org/10.1117/12.620454
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

Topographic backscatter lidar that uses solid surfaces to provide the return signals is a well known vapor estimation technique either though the two-wavelength DIAL (differential absorption lidar) paradigm or a multiple wavelength generalization. All algorithms known to the authors for estimating the path-integrated concentration, or CL, require prior knowledge of the wavelength dependence of the absorptivity of the vapor materials of interest for generating the CL estimates. However, for many applications it is not feasible to process the data in the traditional way. In addition, for some materials the absorptivity may be only approximately known. For these reasons it is often desirable to estimate the spectral structure of the absorptivity using the same data set used to estimate the vapor CL. This paper describes a method for simultaneously estimating the spectral dependence of the absorptivity of a set of Q materials in parallel with the timedependence of the corresponding CLs using a time series of topographic backscatter lidar data collected at M wavelengths. For processing efficiency we provide dynamic estimates of the CLs through a Kalman filter. The fluctuating transmitted energy is also included in the state vector. This inclusion automatically accomplishes transmitter energy normalization optimally. Absorptivity is estimated through a sequential least-squares method. The basic idea is to run two estimators in parallel: a Kalman filter for CL and transmitter energy, and a sequential least-squares estimator for absorptivity. These algorithms exchange information continuously over the data processing stream. The approach is illustrated on simulated and real topographic backscatter lidar data collected by ECBC.

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R. E. Warren and R. G. Vanderbeek "Parallel estimation of path-integrated concentration and vapor absorptivity using topographic backscatter lidar", Proc. SPIE 5887, Lidar Remote Sensing for Environmental Monitoring VI, 58870Q (30 August 2005); https://doi.org/10.1117/12.620454

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