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Cloud cover has an overall negative impact on hyperspectral and multispectral Earth observing sensors; the Hyperspectral Infrared Imaging or HyspIRI mission carries such instruments. A key feature of HyspIRI is its ability to revisit the same point on the equator every 19 days. This allows for better knowledge of the planet's seasonal ecosystem changes. Understanding the likelihood and frequency in which clouds will cover the scenes imaged is necessary to better quantify the science return of HyspIRI and other related missions. Current cloud prediction models are too conservative and only sample small time frames of the available satellite data. This results in a low degree of accuracy with respect to cloud-sensor obscuration and predicts far less of a science return than actual. This study uses a 2007-2009 data set from the Moderate Resolution Imaging Spectrometer (MODIS) on-board the Terra satellite to produce a more accurate prediction of the effects cloud cover has on HyspIRI. The NASA Goddard Spaceflight Center developed Giovanni application was used to extract MODIS data at one-degree spatial resolution. This data created a monthly cloud mask that was averaged into three month blocks to represent seasons. Results show the seasonal data collection probability for HyspIRI's Visual Shortwave Infrared Imaging Spectrometer.