On the effectiveness of cloud cover avoidance methods in support of the Super-spectral Mission for Land Applications

The driving application of the Super-spectral Mission for Land Applications (SMLA) is precision farming. With its optical instrument a large amount of small and scattered targets need to be imaged frequently during the growing season. This paper discusses cloud cover avoidance methods to improve end-to-end system efficiency while maintaining the effective revisit time performance. With the concept of selective imaging only selected parts of the data recorded during the track over the Area Of Interest (AOI) are stored in the on-board memory. This selection can be made based on for example meteorological satellite cloud maps acquired just prior to the pass over the AOI. The purpose is to acquire data with a higher degree of usability. Cloud editing is an on-board process of cloud detection and subsequent discarding data representing cloud cover. Also in this case it is possible to downlink more usable image data. The effectiveness of the methods has been assessed by simulations using a high-resolution cloud database. It can be concluded that in both cases the amount of usable data can be almost doubled at the cost of a slight Increase of effective revisit time.