In theory, lidar overlap factor can be derived from the difference between the particle backscatter coefficient retrieved from lidar elastic signal without overlap correction and the actual particle backscatter coefficient, which can be obtained by other measured techniques. The side-scatter signal using a CCD camera is testified to be a powerful tool to detect the particle backscatter coefficient in near ground layer during night time. In experiment, by combining side-scatter and back-scatter signals the geometric form factor for vertically-pointing Mie lidar in 532 nm channel is determined successfully, which is corrected by an iteration algorithm combining the retrieved particle backscatter coefficient using CCD side-scatter method and Fernald method. In this study, the method will be expanded to 1064 nm channel in dual-wavelength Mie lidar during routine campaigns. The experimental results in different atmosphere conditions demonstrated that the method present in this study is available in practice.

High-precision observations provide an efficient way to calculate greenhouse gas emissions from agricultural fields and their spatial and temporal distributions. Two high-resolution laser heterodyne radiometers (LHRs) were deployed in the suburb of Hefei (31.9°N 117.16°E) for the remote sensing of atmospheric CO2, CH4 and N2O above rice paddy fields. The atmospheric transmittance spectra of CO2, CH4 and N2O were measured simultaneously in real time, and the atmospheric total column abundance was retrieved from the measured data based on the optimal estimation algorithm, with errors of 0.7 ppm, 4 ppb and 2 ppb, respectively. From July to October, the abundance of CO2 in the atmospheric column that was influenced by emissions from rice fields increased by 0.7 ppm CH4 by 30 ppb, and by 4 ppb N2O. During the rice growth season, rice paddy fields play a role in carbon sequestration. CH4 and N2O emissions from paddy fields are negatively correlated. The method of baking rice paddy fields reduces CH4 emissions from rice fields, but N2O emissions from rice fields are usually subsequently increased. The measurement results showed that LHRs are highly accurate in monitoring atmospheric concentrations and have promising applications in monitoring emissions from rice paddy fields. In the observation period, rice paddy fields can sequester carbon, and CH4 and N2O emissions from rice fields are negatively correlated. The LHRs have strong application prospects for monitoring emissions from agricultural fields.

No abstract