Reconstructing earth observation vegetation index records with a Bayesian spatiotemporal dynamic model

Long-term vegetation index records derived from Earth observation facilitate the characterization of ecosystem response to climate variability and change. The presence of atmospheric components and radiometric inconsistencies lead to gaps and artificial jumps in the time series, making such characterization difficult. Compositing over days or weeks minimizes these effects to some degree, but further processing is often performed. In this paper, we develop a spatiotemporal dynamic linear model (DLM) that produces more consistent vegetation index data, while preserving adequate temporal resolution to support accurate global change research. The technique takes the stochastic partial differential equations (SPDE) approach, but employs the Integrated Nested Laplace Approximation (INLA) to decrease computational demand. The new routine was tested on the monthly Vegetation Index and Phenology (VIP) Lab Enhanced Vegetation Index-two (EVI2) version 3 products at 10 km resolution. VIP-EVI2 is derived from red and near-infrared top of atmosphere reflectance, which is measured by the Advanced Very High Resolution Radiometer (AVHRR) on board several National Oceanic and Atmospheric Administration satellites. Performance of the procedure was compared to an adaptive Savitzky-Golay (S-G) filter, forward filtering and backward sampling (FFBS), and a spatiotemporal dynamic model based on a Gibbs sampler. The inter-comparison was made by descriptive analysis, cross-validated root mean squared error, and generalized coefficient of efficiency. Overall, the SPDE showed a higher level of fidelity compared to the alternative techniques. If computational resources are not heavily restricted, the new gap-filling and smoothing procedure provides a viable alternative to standard routines.