The Advanced Baseline Imager (ABI) is being designed for future Geostationary Operational Environmental Satellites (starting with GOES-R in 2012). As with the current GOES Imager, this instrument will be used for a wide range of qualitative and quantitative weather and environmental applications. The ABI will improve over the existing GOES Imager with more spectral bands, higher spatial resolution and faster imaging (and more geographical areas scanned). The ABI will improve the spatial resolution from nominally 4 to 2 km for the infrared bands and 1 to 0.5 km for at least one visible band. There will be a five-fold increase of the coverage rate. The ABI expands the spectral band number from five to at least 12. Up to 18 bands on the ABI are being investigated. Every product that is being produced from the current GOES Imager will be improved with data from the ABI.

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The next-generation NOAA/NESDIS GOES-R hyperspectral sounder, now referred to as the HES (Hyperspectral Environmental Suite), will have hyperspectral resolution (over one thousand channels with spectral widths on the order of 0.5 wavenumber) and high spatial resolution (less than 10 km). Hyperspectral sounder data is a particular class of data requiring high accuracy for useful retrieval of atmospheric temperature and moisture profiles, surface characteristics, cloud properties, and trace gas information. Hence compression of these data sets is better to be lossless or near lossless. Given the large volume of three-dimensional hyperspectral sounder data that will be generated by the HES instrument, the use of robust data compression techniques will be beneficial to data transfer and archive. In this paper, we study lossless data compression for the HES using 3D integer wavelet transforms via the lifting schemes. The wavelet coefficients are processed with the 3D set partitioning in hierarchical trees (SPIHT) scheme followed by context-based arithmetic coding. SPIHT provides better coding efficiency than Shapiro's original embedded zerotree wavelet (EZW) algorithm. We extend the 3D SPIHT scheme to take on any size of 3D satellite data, each of whose dimensions need not be divisible by 2 N , where N is the levels of the wavelet decomposition being performed. The compression ratios of various kinds of wavelet transforms are presented along with a comparison with the JPEG2000 codec.

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