High spatiotemporal resolution maps of surface vegetation from remote sensing data are desirable for vegetation and disturbance monitoring. However, due to the current limitations of imaging spectrometers, remote sensing datasets of vegetation with high temporal frequency of measurements have lower spatial resolution, and vice versa. In this research, we propose a space-time dynamic linear model to fuse high temporal frequency data (MODIS) with high spatial resolution data (Landsat) to create high spatiotemporal resolution data products of a vegetation greenness index. The model incorporates the spatial misalignment of the data and models dependence within and across land cover types with a latent multivariate Matérn process. To handle the large size of the data, we introduce a fast estimation procedure and a moving window Kalman smoother to produce a daily, 30-m resolution data product with associated uncertainty.

中文翻译：

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使用时空动态线性模型的遥感植被指数的多传感器融合

来自遥感数据的地表植被的高时空分辨率地图对于植被和干扰监测是可取的。然而，由于目前成像光谱仪的局限性，测量时间频率高的植被遥感数据集空间分辨率较低，反之亦然。在这项研究中，我们提出了一种时空动态线性模型，将高时频数据 (MODIS) 与高空间分辨率数据 (Landsat) 融合，以创建植被绿度指数的高时空分辨率数据产品。该模型结合了数据的空间错位，并通过潜在的多元 Matérn 过程模拟了土地覆盖类型内部和之间的依赖关系。为了处理大数据，