Journal article
A Deep Learning Approach to Lidar Signal Denoising and Atmospheric Feature Detection
Remote sensing (Basel, Switzerland), Vol.17(24), 4060
01/01/2025
DOI: 10.3390/rs17244060
Abstract
What are the main findings? A deep learning-based denoising algorithm using U-Net CNNs significantly improves the signal-to-noise ratio of ICESat-2 daytime lidar data. The method enables accurate daytime cloud–aerosol discrimination and layer detection at native spatial resolution. What are the implications of the main findings? The approach allows fast processing of photon-counting lidar data, enhancing the utility of daytime observations and improving sensitivity to optically thin atmospheric features. This methodology supports the development of smaller, lower-power spaceborne lidar systems capable of delivering high-quality atmospheric data products comparable to larger instruments. Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments.
Details
- Title: Subtitle
- A Deep Learning Approach to Lidar Signal Denoising and Atmospheric Feature Detection
- Creators
- Joseph Gomes - University of IowaMatthew McGill - University of IowaPatrick Selmer - Earth System Science Interdisciplinary CenterShi Kuang
- Resource Type
- Journal article
- Publication Details
- Remote sensing (Basel, Switzerland), Vol.17(24), 4060
- DOI
- 10.3390/rs17244060
- ISSN
- 2072-4292
- eISSN
- 2072-4292
- Publisher
- MDPI AG
- Language
- English
- Date published
- 01/01/2025
- Academic Unit
- Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering
- Record Identifier
- 9985093886702771
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