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