Effects of the surface albedo on short-wave infrared detection of atmospheric CO2

Abstract

The greenhouse gas carbon dioxide, for which short-wave infrared remote sensing detection is carried out by using satellite sensors to measure the Earth's atmosphere scattering solar radiation, and makes use of the inversion algorithm to achieve measurements. Most of the solar radiation enter the satellite sensors after surface reflection, so the surface albedo which reflects the surface features is one of the important parameters which affect the accuracy of the detection. Aiming at the great demands of high precision carbon dioxide for greenhouse gas, this study first investigate the effects of the Earth's surface albedo on the observed spectra. Simulation results show that the increase in the surface albedo will enhance the observed spectral intensity, especially larger in the O2-A band than in the 1.6 μm band. In other words, the surface albedo has a greater impact on O2-A ban. In the actual satellite inversio, the surface types of actual observation pointare uncertain, which will result in the error of surface albedo. Effect of surface albedo on the inverted XCO2 is analyzed when the surface albedo is changed by changing the type of surfac. Two observation cases are analyzed in detail. One is on April 23, 2009 for the desert surface, and another on May 21, 2013 for the grass surfac. Results show that when the O2-A band surface albedo approximates to the real surface albedo valu, the relative error of the inverted XCO2 is the smaller. If the relative changes of the O2-A band surface albedo exceed 0.25 in the grass surfac or 0.35 in the desert surface, the relative error of the inverted XCO2 will be greater than 1%, not satisfying the design requirement of the inversion system. In contrast, the changesin 1.6 μm band surface albedo have negligible effect on the inverted XCO2. This study shows the importance of surface albedo in the process of satellite remote sensin, and provides an important theoretical basis and guidance for improving the accuracy of remote sensing detectio. All these are significantly contributed to the hyperspectral satellite observation of the greenhouse gas, the investigation of global CO2 distributions, and the prediction and monitoring of the climate change.