The irregular timing and spatial variation in zoonotic arbovirus spillover from vertebrate hosts to humans and livestock present challenges to predicting their occurrence from year to year and within their broader geographic range, compromising effective prevention and control strategies. The objective of this study was to quantify effects of landscape composition and configuration and dynamic temperature and precipitation values on the 2018 spatiotemporal distribution of eastern equine encephalitis virus (EEEV) (Togaviridae, Alphavirus) and West Nile virus (WNV) (Flaviviridae, Flavivirus) sentinel chicken seroconversion in northeastern Florida using Earth Observation (EO) data and a modeling framework that incorporated joint spatial and temporal effects. We investigated environmental effects using Bernoulli generalized linear mixed effects models (GLMMs) including a site level random effect, and then added spatial random effects and spatiotemporal random effects in subsequent runs. Models were executed using integrated nested Laplace approximation (INLA) and a stochastic partial differential equation (SPDE) approach in R-INLA. GLMMs that included a spatiotemporal random effect performed better relative to models that included only spatial random effects and better than non-spatial models. Results indicated strong spatiotemporal structure in seroconversion for both viruses, but EEEV exhibited more punctuated and compact structure at the beginning of the sampling season, while WNV exhibited more gradual and diffuse structure across the study area toward the end of the sampling season. Percentage of cypress/tupelo wetland land cover within 3500 m of coop sites and edge density of forest land cover within 500 m had a strong positive effect on EEEV seroconversion, while the best fitting model for WNV was the intercept only model with spatiotemporal random effects. Lagged temperature and precipitation variables included in our study did not have a strong effect on seroconversion for either virus when accounting for temporal autocorrelation, demonstrating the utility of capturing this structure to avoid Type I errors. Predictive accuracy on out-of-sample data for EEEV seroconversion demonstrates the potential to develop a temporally dynamic framework to predict arbovirus transmission.