Satellite-derived vegetation indices (VIs) provide a powerful means to quantify habitat variation in long-term ecological studies, but their reliability as proxies for forage availability in wild herbivore populations remains underexplored. We used three decades of Landsat satellite imagery (1991–2023) to generate a 30m resolution dataset of a proxy measure for annual vegetation greenness – the Normalized Difference Vegetation Index (NDVI) – for the Isle of Rum, Scotland, home to a long-term study of wild red deer (Cervus elaphus). We validated the NDVI data against field-collected vegetation biomass data and compared it with a coarser-resolution (500m) MODIS Enhanced Vegetation Index (EVI) metric. Landsat NDVI was positively correlated with both biomass and EVI, supporting its ecological relevance as a measure of forage availability. All three metrics have increased over the last three decades, indicating a long-term greening trend, with the higher resolution Landsat dataset revealing a variation in the rate of change among vegetation types, including grassland habitats preferred by deer. These findings suggest an increase in foraging availability over time and offer a possible mechanism underpinning observed demographic shifts, such as earlier calving. Our approach provides a transferable framework for integrating satellite data with individual-based field studies, demonstrating how remote sensing can enhance ecological inference in long-term wildlife research.

Long-term, individual-level studies can provide valuable insights into the effects of climate and landscape change on the ecology and population dynamics of wild animals. However, many such studies lack environmental data collected at sufficient spatial and temporal scales to determine how populations respond to changing conditions. In these cases, the retrospective use of satellite-derived data can provide a way to recover past environmental information. Here, we used 27 years of data from an insectivorous passerine in southeastern Australia, the superb fairy-wren Malurus cyaneus, to assess how climate variation influences vegetation productivity and, indirectly, superb fairy-wren life history traits through potential changes in trophic interactions. Specifically, we combined long-term individual-level monitoring of superb fairy-wrens and local weather records with Landsat satellite imagery, from which we derived measures of vegetation productivity using the Normalised Difference Vegetation Index (NDVI) as a proxy for food availability through arthropod abundance. We found a complex set of associations between NDVI and different components of weather, when considering both concurrent and lagged effects. Our analyses of the causes of seasonal variation in superb fairy-wren life history traits demonstrated that NDVI was associated with: (i) temporal variation in breeding success, with years with high spring and summer NDVI values having relatively high average breeding success; and (ii) spatial variation in adult mortality in autumn and winter, with superb fairy-wren territories with low autumn–winter NDVI values having higher average mortality rates. Notably, autumn–winter NDVI values were found to have remained relatively consistent over time, and therefore cannot explain recently observed increases in adult autumn–winter mortality. Our study illustrates the potential of using long-term Landsat satellite imagery to investigate whether associations between animal life history traits and climate are mediated by vegetation productivity and to what extent temporal trends are influenced by climate change.