Some key issues related to space-based compression design are discussed. Various system considerations as well as potential compression options are also presented. A brief overview of a previously reported robust lossy transform coding algorithm is given followed by a study of its performance sensitivities. These sensitivities include (1) performance sensitivity to commonly observed anomalies in the data including band misalignment and dead/ saturated pixels, (2) impact of geometric distortion (processed versus unprocessed data) on compression performance, (3) performance sensitivity to different grouping of bands for spectral decorrelation, and (4) impact of compression on spectral fidelity. In addition, the impact of compression on the results of exploitation of environmental data including automated cloud study will be considered. It is shown that preprocessing to correct any geometric distortion noticeably improves the compression performance. Different groupings of bands also influence the performance. The loss of spectral fidelity, measured by the deviation from the original correlation coefficient matrix, is very insignificant regardless of the image and the coding bit rates. For the available bit rate, it is possible to trade off the compression-induced error between the spectral and spatial resolutions. In the implementation scenarios investigated, it was found that compression at rates approaching 16:1 has a minor impact on the exploitation and assessment of the ultimately derived automated cloud analysis. Additional work is needed to evaluate the impact of compression on other products, such as sea surface temperature. The results to date suggest that lossy compression may play a role in the efficient transmission of environmental information and in its subsequent exploitation.