Spatial degradation of satellite data

Abstract

Two aspects of spatial degradation of satellite data are examined. The first describes a technique for spatially degrading high-resolution satellite data to produce comparable data sets over a range of coarser resolutions. In this study seven spatial resolution data sets are produced from Landsat Multispectral Scanner (MSS) data resulting in spatial resolutions ranging from 79 m to 4 km applying a spatial filter designed to simulate sensor response. The simulation is demonstrated for part of the Superior National Forest, Minnesota. The second part of the paper examines spatial degradation of coarse resolution data to provide data compression for the production of global-scale data sets. The on-board sampling approach adopted by the National Oceanographic and Atmospheric Administration (NOAA) to produce the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data from the 1 km Large Area Coverage (LAC) data, is compared to other sampling procedures. Six sampling procedures were compared for seven terrain types. The GAC sampling procedure provided a relatively poor representation of the 1 km data, particularly for heterogeneous areas. Coefficients of determination for the GAC sampling compared to the original data ranged from 0.49−0.76. Sampling procedures incorporating averaging resulted in a decrease in the variance as compared with the original data. Sampling procedures adopting single-value selection had higher variances and produced data values directly comparable with those from the original data. Sampling scheme design should consider data fidelity requirements as well as the engineering constraints of on-board processing.