Reconstructing Global Earth Observation Based Vegetation Index Records with Stochastic Partial Differential Equations Approach

Long-term Earth observation based vegetation index records have been used extensively by researchers to assess vegetation response to global climate variability and change. However, the records exhibit multiple temporal gaps due to spectral and radiometric inconsistencies that inhibit accurate assessment of land surface vegetation dynamics. Here, we propose a new reconstruction procedure that approximates Bayesian time series model by using integrated nested Laplace approximations (INLA) to overcome Bayesian computational limitations. The technique was tested on the vegetation index and phenology (VIP) Lab enhanced vegetation index-two (VIP-EVI2) version 3 15-day 5 km resolution record. VIP EVI2 is a reconstructed record with inverse distance weighting function and linear interpolation (IDW EVI2). VIP-EVI2 is derived from red and near-infrared (NIR) top of canopy (TOC) reflectance, detected by the Advanced Very High Resolution Radiometer (AVHRR). The INLA-EVI2 was compared globally and locally with an adaptive Savitzky-Golay (SG-EVI2) filter. The global evaluation was done by descriptive analysis, goodness-of-fit by Kolmogorov-Smirnov (K-S) test, annual trend analysis by Thiel Sen (T-S) slope. The local comparison was done by evaluating the ability of IDW-EVI2, SG-EVI2, and INLA-EVI2 to estimate in situ Leaf Area Index (LAI) measurements taken over several years and for major field crops across the globe. Locally, INLA-EVI2 estimated the in situ data more correctly than SG-EVI2 as indicated by R2 and RMSE. Globally, the INLA-EVI2 recorded a better goodness-of-fit, more stable and consistent trends than SG-EVI2. Based on these findings, if computational resources are unlimited, the INLA approach provides a viable alternative to standard reconstruction procedures.