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Airborne Validation Experiment of 1.57-μm Double-Pulse IPDA LIDAR for Atmospheric Carbon Dioxide Measurement
- Source :
- Remote Sensing, Vol 12, Iss 12, p 1999 (2020)
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- The demand for greenhouse gas measurement has increased dramatically due to global warming. A 1.57-μm airborne double-pulse integrated-path differential absorption (IPDA) light detection and ranging (LIDAR) system for CO2 concentration measurement was developed. The airborne field experiments of this IPDA LIDAR system were conducted at a flight altitude of approximately 7 km, and the weak echo signal of the ocean area was successfully received. The matched filter algorithm was applied to the retrieval of the weak signals, and the pulse integration method was used to improve the signal-to-noise ratio. The inversion results of the CO2 column-averaged dry-air mixing ratio (XCO2) by the scheme of averaging after log (AVD) and the scheme of averaging signals before log were compared. The AVD method was found more effective for the experiment. The long-term correlation between the changing trends of XCO2 retrieved by the IPDA LIDAR system and CO2 dry-air volume mixing ratio measured by the in-situ instrument reached 92%. In the steady stage of the open area (30 km away from the coast), which is almost unaffected by the residential areas, the mean value of XCO2 retrieved by the IPDA LIDAR system was 414.69 ppm, with the standard deviation being 1.02 ppm. Compared with the CO2 concentration measured by the in-situ instrument in the same period, bias was 1.30 ppm. The flight path passed across the ocean, residential, and mountainous areas, with the mean value of XCO2 of the three areas being 419.35, 429.29, and 422.52 ppm, respectively. The gradient of the residential and ocean areas was 9.94 ppm, with that of the residential and mountainous areas being 6.77 ppm. Obvious gradients were found in different regions.
- Subjects :
- CO2 concentration
IPDA LIDAR
airborne experiment
long-term correlation
Science
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 12
- Issue :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.06e8752119c04e84bc60d8a3f8d3300c
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/rs12121999