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Related Experiment Video

Updated: May 20, 2026

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
08:09

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Published on: September 12, 2017

Error reduction methods for integrated-path differential-absorption lidar measurements.

Jeffrey R Chen1, Kenji Numata, Stewart T Wu

  • 1NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA. jeffrey.r.chen@nasa.gov

Optics Express
|July 10, 2012
PubMed
Summary
This summary is machine-generated.

New methods improve differential-absorption optical-depth (DAOD) measurements using lidar. We reduced errors from laser noise and surface variations, enhancing atmospheric constituent analysis.

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Area of Science:

  • Atmospheric Science
  • Lidar Technology
  • Optical Remote Sensing

Background:

  • Differential-absorption optical-depth (DAOD) measurements are crucial for monitoring atmospheric constituents.
  • Existing methods using direct-detection integrated-path differential-absorption lidars are susceptible to various error sources.
  • Accurate DAOD measurements are essential for climate modeling and atmospheric research.

Purpose of the Study:

  • To develop and present novel modeling and error reduction techniques for DAOD measurements.
  • To quantify and mitigate errors arising from laser frequency noise and surface variations.
  • To improve the accuracy and reliability of lidar-based atmospheric constituent analysis.

Main Methods:

  • Quantified errors from laser frequency noise, analyzing line center fluctuation and spectral line shape.
  • Introduced a correction factor to remove significant DAOD bias.
  • Implemented strategies including altimetry knowledge, 'log after averaging', and fixed surface spot targeting to reduce surface-related errors.

Main Results:

  • Established experimentally verified relationships between laser noise and DAOD errors.
  • Successfully removed a significant DAOD bias using the developed correction factor.
  • Demonstrated reduction of surface height and reflectance variation errors to tolerable levels.

Conclusions:

  • The proposed methods significantly enhance the accuracy of DAOD measurements.
  • Error reduction techniques effectively address limitations posed by laser noise and surface variability.
  • Improved DAOD measurements will benefit atmospheric constituent monitoring and research.