Inter-satellite variability of grassland curing maps produced by different satellite sensors – Victoria, Australia

Abstract

Grassland fires are a serious problem in Victoria, Australia due to large quantity of dry grass. Grassland curing degree (GCD) measures the dryness of the grass and is an important factor for assessing grassland fire danger. Grassland curing maps (GCMs) display the spatial distribution of GCDs, but the quality of GCMs varies depending on the spatial resolution of the observing satellite remote sensing system. The higher the spatial resolution, the finer the GCD details and more spatial variations the GCM can reveal. In this study, GCD calculation algorithm named MapVictoria based on MODIS data is tested for Landsat 8 Sentinel 2; GCMs generated from these three satellites are contrasted by their GCD differences, defined here as inter-satellite variability (ISV). ISV is used to identify areas where higher resolution satellite GCMs should be used. Results show that spatial resolution difference (ΔSR), seasonality and geographical locations affect the magnitude of the ISV. Based on these findings, this paper provides recommendations to decision makers on where and when to use which satellite for grassland observations.

Keywords

• grassland curing
• remote sensing
• grassland fires
• australia
• variability