Fractional vegetation cover (FVC) is considered one of the most important vegetation parameters and is relevant to characterizing vegetation status and ecosystem function. An FVC with a fine spatial resolution of 30 m is essential for monitoring vegetation change and regional studies, while an FVC with a coarse spatial resolution of hundreds to thousands of metres plays an important role in global change studies. However, high spatial resolution data usually have low temporal resolution and are often affected by cloud cover. The objective of this study is to propose a practical way to generate spatiotemporally consistent FVC products at Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) scales, which are 30 m and 250 m, respectively. The geostatistical neighbourhood similar pixel interpolator (GNSPI) was first used to fill in the missing values caused by unscanned gaps and clouds/shadows on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data and to generate spatially continuous Landsat reflectance. Then, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was used to generate time series Landsat reflectance data with the same temporal resolution as that of Global LAnd Surface Satellite (GLASS) FVC generated from MODIS data. The high temporal resolution Landsat reflectance was preliminarily used to estimate FVC at the Landsat scale. Finally, MultiResolution Tree (MRT) was employed to fuse the Landsat FVC and GLASS FVC to generate spatiotemporally consistent FVC products at different scales. The results show that the missing Landsat-7 ETM+ data were filled well and spatial texture features were well preserved. The temporal resolutions of the Landsat and GLASS FVC products became consistent with an interval of one day at most. After MRT fusion, most of the root mean square error (RMSE) between the GLASS FVC and aggregated Landsat FVC dramatically decreased. The accuracy of the Landsat FVC validated by the ground-measured FVC improved after MRT fusion (before MRT: RMSE = 0.1031, R² = 0.9172, bias = −0.0697; after MRT: RMSE = 0.0958, R² = 0.9173, bias = −0.054). In addition, in the GNSPI-filled unscanned gaps and the ESTARFM-generated images, the Landsat FVC accuracy also improved slightly (before MRT: RMSE = 0.1065, R² = 0.9011, bias = −0.0644; after MRT: RMSE = 0.1022, R² = 0.9023, bias = −0.051). The accuracy of the GLASS FVC also improved (before MRT: RMSE = 0.0913, R² = 0.884, bias = −0.0504; after MRT: RMSE = 0.0673, R² = 0.9483, bias = −0.0444). Therefore, MRT could decrease the inconsistencies of different scales and reduce uncertainties in the FVC. In addition, MRT could fill in the missing data of the Landsat FVC directly, but there were a certain number of outliers in the fusion results, and the spatial transition was poor.

植被覆盖度（FVC）被认为是最重要的植被参数之一，与表征植被状况和生态系统功能有关。具有30 m的精细空间分辨率的FVC对于监测植被变化和区域研究至关重要，而具有数百至数千米的粗糙空间分辨率的FVC在全球变化研究中发挥着重要作用。但是，高空间分辨率的数据通常具有较低的时间分辨率，并且通常会受到云量的影响。这项研究的目的是提出一种实用的方法，以分别在Landsat和中分辨率成像光谱仪（MODIS）的比例尺（分别为30 m和250 m）上生成时空一致的FVC产品。地统计邻域相似像素插值器（GNSPI）首先用于填补Landsat-7增强型专题制图仪Plus（ETM +）数据上未扫描的间隙和云/阴影所引起的缺失值，并生成空间连续的Landsat反射率。然后，使用增强的时空自适应反射融合模型（ESTARFM）生成时间序列Landsat反射率数据，该时间序列的时间分辨率与从MODIS数据生成的全球陆地和地面卫星（VCASS）FVC的时间分辨率相同。初步使用了高时间分辨率的Landsat反射率来估算Landsat尺度的FVC。最后，采用MultiResolution Tree（MRT）将Landsat FVC和GLASS FVC融合在一起，以生成不同规模的时空一致的FVC产品。结果表明，缺失的Landsat-7 ETM +数据被很好地填充，并且空间纹理特征得到了很好的保留。Landsat和GLASS FVC产品的时间分辨率最长间隔为一天。MRT融合后，GLASS FVC和汇总Landsat FVC之间的大多数均方根误差（RMSE）大大降低。MRT融合后，经地面测量的FVC验证的Landsat FVC的精度有所提高（MRT之前：RMSE = 0.1031，R²  = 0.9172，偏差= -0.0697；MRT后：RMSE = 0.0958，R ²  = 0.9173，偏差= -0.054）。此外，在充满GNSPI的未扫描间隙和ESTARFM生成的图像中，Landsat FVC精度也略有提高（在MRT之前：RMSE = 0.1065，R ²  = 0.9011，偏差= -0.0644；在MRT之后：RMSE = 0.1022，R ²  = 0.9023，偏差= -0.051）。GLASS FVC的精度也有所提高（MRT之前：RMSE = 0.0913，R ²  = 0.884，偏差= -0.0504; MRT之后：RMSE = 0.0673，R ² = 0.9483，偏差= -0.0444）。因此，MRT可以减少不同规模的不一致性，并减少FVC中的不确定性。此外，MRT可以直接填充Landsat FVC的缺失数据，但是融合结果中存在一定数量的离群值，并且空间转换很差。