Global Cloud Property Models for Real Time Triage Onboard Visible-Shortwave Infrared Spectrometers

New methods for optimizing data storage and transmission are required as orbital imaging spectrometers collect and downlink larger data volumes due to increases in optical efficiency and resolution. For missions investigating Earth surface reflectance, excising cloud-contaminated data during acquisition can significantly improve the overall science yield for missions with a fixed downlink budget. Algorithms that consider threshold-based screening are able to operate at the acquisition rate of the instrument but require an accurate and comprehensive prediction of cloud and surface brightness. Previous studies have not conducted a comprehensive analysis of a global dataset to provide appropriate thresholds for screening clouds or to predict performance. To address this concern, we present an analysis with the Hyperion imaging spectrometer’s historical archive of global Earth reflectance data. We selected a diverse subset spanning space (in latitude including the tropics, midlatitudes, arctic, and Antarctic), time (2005-2017), and wavelength (400 – 2500 nm) to assure that the distributions of cloud data are representative of all cases. We fit models of cloud reflectance properties gathered from the subset to predict globally applicable thresholds. The distributions relate cloud reflectance properties to various surface types (land, water, and snow) and latitudinal zones. Models based on this dataset will be used to screen clouds onboard orbital imaging spectrometers, effectively doubling the yield of usable science data per downlink