In order to solve the problem of low illumination and signal-to-noise ratio of hyperspectral (multispectral) images under short exposure conditions, a hyperspectral (multispectral) image enhancement model was proposed based on the improved residual U-Net (Res-UNet). The Res-UNet was taken as the backbone network, atrous spatial pyramid pooling and coordinate attention were used to enhance the feature aggregation ability of the model, and the Z-score loss function was introduced to improve the reconstruction ability of the model on spectral curves. The performance of the proposed model was evaluated using image enhancement quality metrics and a downstream task (drought leaf segmentation accuracy). The peak signal-to-noise ratio, structural similarity and spectral angular mapping of the improved model reach 0.9852, 39.71 and 3.120, which are better than the comparative algorithms. And its segmentation accuracy for arid leaves is higher than that of various mainstream algorithms. The result shows the effectiveness of this model for plant low-light multispectral image enhancement, which can provide information support for various spectral image downstream tasks.