低空间分辨率（LR）遥感数据因其成本较低而被广泛采用，但其有限的分析精度限制了其在精准农业中的充分应用。相比之下，获取高空间分辨率（HR）数据往往需要巨额投入。为解决这一局限，本研究提出一种无监督退化感知多通道超分网络（UDAMSR），无需配对HR-LR训练数据即可增强LR光谱图像。研究主要贡献包括：① 原有框架通过专用队列层和重建层扩展以处理多光谱和高光谱（HIS）立方体图像，并集成基于对比学习的退化感知模块以应对未知现实退化；② 通过图像质量指标、光谱一致性分析及作物遥感任务（如叶绿素含量检测）性能进行综合评估；③ 采用三种成像设备、两种空间尺度（近地面和无人机）及两个地理区域的数据评估模型泛化能力。结果显示，所提方法在综合评估中表现最优，平均峰值信噪比（(PSNR) ̅）为32.78，平均均方根误差（(RMSE) ̅）为6.93，平均结构相似性指数（(SSIM) ̅）为0.89，平均光谱角映射器（(SAM) ̅）为0.131。该方法能有效降低空间分辨率下降对叶绿素检测精度的影响。泛化能力评估表明，所提方法在不同空间尺度、地理区域、设备类型及数据类型间均展现出强大的泛化能力。这些结果表明，UDAMSR提供了一种稳健、高效且经济的软件解决方案，能够弥补硬件限制，并支持在多种应用场景中实现高质量的作物表型检测。

Low spatial resolution (LR) remote sensing data is widely adopted because of its lower cost, although its limited analytical precision constrains its full use in precision agriculture. By contrast, the acquisition of high spatial resolution (HR) data often requires substantial expense. To address this limitation, this study proposes an unsupervised degradation-aware multi-channel super-resolution network (UDAMSR) to enhance LR spectral images without requiring paired HR-LR training data. The main contributions are as follows: ① the original framework is extended with dedicated queue and reconstruction layers to process multispectral and hyperspectral image (HIS) cubes, and a contrast-learning-based degradation-aware module is integrated to address unknown real-world degradation; ② comprehensive evaluation is conducted using image quality metrics, spectral consistency analysis, and performance in crop remote sensing tasks, such as chlorophyll content estimation; ③ the generalization capability of the model is assessed using data from three imaging devices, two spatial scales (near-ground and unmanned aerial vehicle (UAV)), and two geographic regions. The results show that the proposed method achieves the best overall performance in the comprehensive evaluation, with a mean peak signal-to-noise ratio ($\bar{PSNR}$) of 32.78, a mean root mean squared error ($\bar{RMSE}$) of 6.93, a mean structural similarity index ($\bar{SSIM}$) of 0.89, and a mean spectral angle mapper ($\bar{SAM}$) of 0.131. The method effectively reduces the degradation in chlorophyll detection accuracy caused by spatial resolution reduction. The evaluation of generalization capability further shows that the proposed method demonstrates strong generalization across different spatial scales, geographic regions, devices, and data types. These results indicate that UDAMSR provides a robust, efficient, and cost-effective software solution that can compensate for hardware limitations and support high-quality crop phenotyping detection in diverse application scenarios.