Cloud cover avoidance in space-based remote sensing acquisition

John R. Gasch; Kenneth A. Campana

doi:10.1117/12.410357

23 August 2000 Cloud cover avoidance in space-based remote sensing acquisition

John R. Gasch, Kenneth A. Campana

Proceedings Volume 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI; (2000) https://doi.org/10.1117/12.410357
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

The Landsat 7 mission is the first of the Landsat series of remote sensing satellites to employ automated techniques of cloud cover avoidance in its mission of acquiring a global database of high resolution (15-30m) multi-spectral images. Cloud avoidance enables the mission to concentrate its limited assets toward the acquisition of higher quality scenes by repelling away from scenes where there is higher than nominal predicted cloud density. Thus, the mission has higher probability of acquiring scenes of greater value for land use studies. The timely availability of reliable global cloud cover forecasts from the National Centers for Environmental Prediction (NCEP) makes this operationally feasible. This paper will describe the general implementation and mission operational considerations for employing cloud avoidance in daily mission planning and scheduling. The algorithms employed in the scheduler’s priority computations will be described, along with the proof of concept in the form of modeled and actual results obtained by Landsat 7 against the historical cloud contamination statistics obtained by other remote sensing satellites in its class. The paper will also describe the cloud cover prediction methods currently employed by NCEP as well as plans for future enhancements to the cloud prediction model. In conclusion, the paper will explore the applicability of employing cloud avoidance in future, and possibly existing, remote sensing satellite missions.

Citation Download Citation

John R. Gasch and Kenneth A. Campana "Cloud cover avoidance in space-based remote sensing acquisition", Proc. SPIE 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, (23 August 2000); https://doi.org/10.1117/12.410357

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