Given the unprecedented size of ultraspectral sounder data, there is a special process of radiance thinning in assimilating this data to reduce the data volume with minimal loss of atmospheric information. Considering the potential correlation between the selected data by radiance thinning and the unselected data, a lossless compression method for ultraspectral sounder data is proposed based on key information extraction and spatial–spectral prediction. Sensitive channels are first selected by stepwise iteration based on information entropy to maintain critical atmospheric information, and then auxiliary channels are further selected based on information content and correlation constraints to facilitate prediction. All of the selected channels are spatially thinned to generate key information, which is then used to predict original ultaspectral sounder data by spatially bicubic interpolation and spectrally sparse reconstruction. The residual errors are processed by the least-squares linear prediction to further reduce data redundancy. Together with the key information, the final residual errors are then fed into a range coder after positive mapping and histogram packing. Experimental results with IASI-L1C data show that the proposed method achieves an average compression ratio of 2.68, which is 4.7% higher than that of the typical methods, including JPEG-LS, JPEG-2000, M-CALIC, CCSDS-122.0, CCDS-123.0, and HEVC.