To deal with the huge volume of data produced by hyperspectral sensors, the Canadian Space Agency (CSA) has developed two simple and fast algorithms for compressing hyperspectral data, namely Successive Approximation Multistage Vector Quantization (SAMVQ) and Hierarchical Self-Organizing Cluster Vector Quantization (HSOCVQ). The CSA intends to use these algorithms, which are capable of providing high compression rates, on-board a proposed Canadian hyperspectral satellite. It has been shown that both SAMVQ and HSOCVQ are near-lossless compression algorithms as their designs restrict compression errors to levels consistent with the level of the intrinsic noise in the original hyperspectral data. Although both of them are more bit-error resistant than the traditional compression algorithms, when the bit-error rate (BER) exceeds 10-6, the compression fidelity starts to drop apparently. This paper explores the benefits of employing forward error correction on top of data compression, by SAMVQ or HSOCVQ, to deal with higher BERs. In particular, it is shown that by proper use of convolutional codes, the resilience of compressed hyperspectral data against bit errors can be improved by close to two orders of magnitude.