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As West Nile Virus (WNV) and St. Louis Encephalitis (SLE) become more prevalent across North America, there is an increased risk of fatal neuroinvasive cases. In order for public health officials to prepare for these cases and potentially intervene, the ability to forecast mosquito borne disease outbreaks is paramount. In practice, however, such vector borne diseases are notoriously difficult to predict due to their seemingly sporadic spatial and temporal outbreak patterns. Recent research has demonstrated that mosquito abundance is causally related to WNV/SLE prevalence, providing a practical starting point for developing mosquito-borne disease forecasting systems. When focusing on building mosquito population models, understanding the reproduction environment of Culex mosquitos (WNV and SLE's primary vectors) is key: they rely on warmth, water, and vegetation to reproduce. Previous work has shown that global-coverage multispectral imagery (MSI) (i.e., Landsat 8, Sentinel- 2) is a valuable resource for characterizing vegetation health as a predictor of mosquito population, but it is limited in that it may not provide the spatial resolution necessary to distinguish between, e.g., a well-fertilized lawn (poor Culex habitat) and a stand of trees (good Culex habitat). The backscatter information collected by synthetic aperture radar (SAR) imagery provides opportunity to distinguish between broader categories of vegetation type, potentially helping to fill this gap. This research uses publicly available global-coverage MSI and SAR imagery (Landsat 8, Sentinel-2, and Sentinel-1) to explore if vegetation type, in tandem with vegetation health, improves our ability to forecast mosquito populations. Vegetation characterization is done over the Greater Toronto Area from 2014 to 2017, and we derive weekly time series from MSI, spectral indices, and SAR for this time period. We then quantify the strength of vegetation health and type as a predictor of Culex abundance.
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