Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers

• Published in: Atmospheric Measurement Techniques
• Main Authors: M. W. Sandford, D. R. Thompson, R. O. Green, B. H. Kahn, R. Vitulli, S. Chien, A. Yelamanchili, W. Olson-Duvall
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: Environmental engineering; TA170-171; Earthwork. Foundations; TA715-787; Antarctic; Arctic; Hyperion; Antarc*
• Online Access: https://doi.org/10.5194/amt-13-7047-2020; https://doaj.org/article/a358f3cafc0f42168fbd562af4e48f3a

New methods for optimizing data storage and transmission are required as orbital imaging spectrometers collect ever-larger data volumes due to increases in optical efficiency and resolution. In Earth surface investigations, storage and downlink volumes are the most important bottleneck in the mission's total data yield. Excising cloud-contaminated data on board, during acquisition, can increase the value of downlinked data and significantly improve the overall science performance of the mission. Threshold-based screening algorithms can operate at the acquisition rate of the instrument but require accurate and comprehensive predictions of cloud and surface brightness. To date, the community lacks a comprehensive analysis of global data to provide appropriate thresholds for screening clouds or to predict performance. Moreover, prior cloud-screening studies have used universal screening criteria that do not account for the unique surface and cloud properties at different locations. To address this gap, we analyzed the Hyperion imaging spectrometer's historical archive of global Earth reflectance data. We selected a diverse subset spanning space (with tropical, midlatitude, Arctic, and Antarctic latitudes), 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 locally and globally applicable thresholds. The distributions relate cloud reflectance properties to various surface types (land, water, and snow) and latitudinal zones. We find that taking location into account can significantly improve the efficiency of onboard cloud-screening methods. Models based on this dataset will be used to screen clouds on board orbital imaging spectrometers, effectively doubling the volume of usable science data per downlink. Models based on this dataset will be used to screen clouds on board NASA's forthcoming mission, the Earth Mineral Dust Source Investigation (EMIT).