Inversion of oceanographic profiling lidars by a perturbation to a linear regression

Area of Science:

• Oceanography
• Remote Sensing
• Biogeochemistry

Background:

• Airborne oceanographic lidar provides valuable data on subsurface ocean properties.
• Accurate retrieval of optical properties from lidar signals is crucial for ecological studies.
• Existing methods for lidar data inversion can be complex and sensitive to noise.

Purpose of the Study:

• To develop a simple and robust inversion method for airborne oceanographic lidar profiles.
• To improve the accuracy of backscatter estimation from lidar data.
• To validate the method using field data and compare it with satellite observations.

Main Methods:

• A two-step inversion process involving linear regression and perturbation analysis.
• Application of the inversion technique to airborne lidar data collected off the coast of Florida.
• Correlation analysis between derived lidar backscatter and satellite-derived surface chlorophyll concentration.

Main Results:

• The inversion method provides a simple and robust way to estimate backscatter from lidar data.
• Expected errors in backscatter estimates for thin plankton layers are less than 10% over 90% of the ocean.
• A high correlation (0.92) was found between lidar backscatter at 5m depth and satellite-measured surface chlorophyll concentration.

Conclusions:

• The developed inversion technique is effective for analyzing airborne oceanographic lidar data.
• The method offers improved accuracy for detecting and quantifying features like plankton layers.
• The strong correlation with satellite data highlights the potential for integrating lidar and ocean color measurements for enhanced ocean monitoring.