Multisensor Fusion of Remotely Sensed Vegetation Indices Using Space-Time Dynamic Linear Models
提出一种时空动态线性模型，融合MODIS的高时间频率数据和Landsat的高空间分辨率数据，生成每日30米分辨率的植被绿度指数产品，并附带不确定性估计，适用于植被与干扰监测。
Abstract 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.