植被覆盖度(FVC)是描述地表植被分布的定量指标之一。通过遥感卫星(如Landsat和Sentinel-2)获取大尺度下的高空间分辨(如10 m级) FVC，能为全球生态系统研究提供重要基础数据。然而，由于云雾干扰以及卫星重返时间分辨率有限等问题，高空间分辨FVC在时域上存在大量缺失。本文考虑协同30 m Landsat-8和10 m Sentinel-2数据，实现二者在时域上的互补。为解决二者空间分辨率不一致的问题，本文提出了一种基于FVC-Net的深度学习方法，通过融合10 m Sentinel-2归一化植被指数(NDVI)数据，将30 m Landsat FVC降尺度至10 m。FVC-Net方法构建双分支结构下的通道注意力模块用于FVC和NDVI的多尺度特征采集与融合，随后利用空间注意力模块将选择的特征进行细节增强，以有效描述不同获取时间下的10 m NDVI与30 m FVC之间的非线性映射关系。实验中，与四种典型非深度学习方法和三种深度学习方法相比，FVC-Net获得了更高精度的融合结果。FVC-Net有望应用于全球尺度下的30 m Landsat FVC产品的降尺度，为相关领域研究提供更为精细的数据支撑。

Objective: Fractional vegetation cover (FVC) is an important indicator to characterize the spatial distribution of vegetation on the land surface. Remote Sensing Satellites (such as Landsat and Sentinel-2) can acquire fine spatial resolution FVC data at 10 m level, which are crucial source for researches on global ecosystem. However, due to cloud contamination and limited temporal resolution of the satellites, a large amount of fine spatial resolution FVC data are not available in the temporal domain. This paper considers the collaboration of 30 m Landsat-8 and 10 m Sentinel-2 to increase the temporal frequency of the observations. Method: To deal with the difference in the spatial resolution, a deep learning-based method named FVC-Net is proposed in this paper. FVC-Net fuses 30 m Landsat FVC with 10 m Sentinel-2 normalized difference vegetation index (NDVI) directly, producing 10 m Landsat FVC. Specifically, a two-branch network based on multi-scale attention mechanism is designed, in which the channel enhancement blocks are used in both FVC and NDVI branches for feature extraction and fusion. Then, the spatial attention blocks are used to increase the spatial details of the fused FVC features. The scheme designed in FVC-Net can help to characterize the non-linear relationship between 10 m NDVI and 30 m FVC effectively. Result: In the experiments, the proposed FVC-Net was compared with four typical non-deep learning-based and four deep learning-based fusion methods. It was found that FVC-Net is consistently more accurate than the eight benchmark methods. The 10 m FVC results can display more spatial details than original 30 m FVC. Conclusion: The proposed FVC-Net is an effective solution to downscale 30 m Landsat FVC to 10 m by fusion with 10 m Sentinel-2 NDVI, which can effectively overcome the differences between Sentinel-2 and Landsat data at different time points. FVC-Net has the potential to be applied to downscale the current 30 m Landsat FVC products at the global scale, of which the predictions can support the researches in the related fields greatly.