Assessment of Cloud Screening With Apparent Surface Reflectance in Support of the ICESat-2 Mission

Abstract

Cloud detection/screening is a fundamental step in satellite data analysis. For the Ice, Cloud, and land Elevation Satellite (ICESat) and its successor ICESat-2, clouds can significantly affect the accuracy of the surface elevation retrievals. This paper proposes a new method for cloud screening in support of the ICESat-2 mission with focus on the polar ice sheet regions. The method utilizes the apparent surface reflectance (ASR) at the backscattering direction as the cloud screening test. The basis of this method is that clouds produce a strong signal by significantly decreasing the ASR. We show that depending on the height and microphysics of the cloud, the ASR decreases 8%-17% for clouds with an optical depth of 0.1 and 57%-85% for clouds with an optical depth 1.0. Data from ICESat's 1064-nm channel is used to demonstrate the feasibility of the method. It is shown that cloud detectability is a function of surface reflectance variability. Generally, the smaller the surface reflectance variability, the more accurate is cloud detection. Unlike ICESat, which used a 1064-nm laser, ICESat-2 adopts a 532-nm photon counting system for its laser altimeter. With both modeling studies and results from the Moderate Resolution Imaging Spectroradiometer (MODIS), we demonstrate that the ASR variability is much smaller for the 532-nm channel than that for the 1064-nm channel. Hence, the 532-nm channel is better suited for cloud screening than the 1064-nm channel.