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Multisensor fusion of remotely sensed vegetation indices using space‐time dynamic linear models
Author(s) -
Johnson Margaret C,
Reich Brian J,
Gray Josh M
Publication year - 2021
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12495
Subject(s) - remote sensing , vegetation (pathology) , enhanced vegetation index , temporal resolution , image resolution , fuse (electrical) , normalized difference vegetation index , land cover , sensor fusion , kalman filter , environmental science , computer science , geography , vegetation index , artificial intelligence , climate change , land use , geology , oceanography , medicine , physics , civil engineering , engineering , pathology , quantum mechanics , electrical engineering
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.