Bioregional benchmarking of vegetation cover dynamics across grazing landscapes through climate-informed remote sensing

Human activities have fundamentally altered the functioning of most terrestrial ecosystems, particularly grazing ecosystems where vegetation-dependent processes underpin both human well-being and ecological resilience. Despite the importance of ground cover for sustaining these processes, the long-term dynamics of vegetation cover—and the degree to which temporal variability is explained by climatic drivers—remain poorly quantified at farm and paddock scales. Existing approaches typically focus on photosynthetic vegetation or rainfall alone, with limited integration of temperature effects, non-photosynthetic components, or consistent benchmarking across environmental and land-use contexts. Here, we applied a remote-sensing framework to quantify long-term relationships between total vegetation cover (i.e., the sum of photosynthetic and non-photosynthetic components) and climate, expressed as interactions between accumulated rainfall and temperature using Landsat-derived fractional cover and SILO gridded climate data. Using 40 years of Landsat-derived fractional cover data, we characterised vegetation cover dynamics using three complementary metrics: (1) long-term mean vegetation cover, (2) the coefficient of determination (R 2 ), defined as the proportion of temporal vegetation-cover variability explained by the selected climate predictors, and (3) temporal trends in climate-adjusted residuals indicating vegetation-cover change not explained by the climate model. We further introduced a bioregional benchmarking approach that contextualises paddock-scale patterns against comparable land uses within shared environmental settings. Across more than 140,000 km 2 of Australian grazing landscapes, interactions between monthly temperature and accumulated rainfall (1–18 months) frequently explained a large proportion of vegetation cover variability (R 2 > 0.5). Significant residual trends were detected across more than one-quarter of the landscape, with decreasing climate-adjusted vegetation-cover signals more prevalent than increasing signals. These residual trends identify areas requiring contextual interpretation rather than direct attribution to degradation. All three metrics varied markedly among bioregions and land use classes, highlighting the importance of region-specific benchmarking for interpretation. Our results demonstrate the value of integrating long-term Earth observation data with climate modelling to detect ecosystem change signals and support evidence-based decision-making in grazing ecosystems.