Data from: Landsat-based greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations

Remote sensing is an invaluable tool for tracking decadal-scale changes in
vegetation greenness in response to climate and land use changes. While
the Landsat archive has been widely used to explore these trends and their
spatial and temporal complexity, its inconsistent sampling frequency over
time and space raises concerns about its ability to provide reliable
estimates of annual vegetation indices such as the annual maximum NDVI,
commonly used as a proxy of plant productivity. Here we demonstrate for
seasonally snow-covered ecosystems, that greening trends derived from
annual maximum NDVI can be significantly overestimated because the number
of available Landsat observations increases over time, and mostly that the
magnitude of the overestimation varies along environmental gradients.
Typically, areas with a short growing season and few available
observations experience the largest bias in greening trend estimation. We
show these conditions are met in late snowmelting habitats in the European
Alps, which are known to be particularly sensitive to temperature
increases and present conservation challenges. In this critical context,
almost 50% of the magnitude of estimated greening can be explained by this
bias. Our study calls for greater caution when comparing greening trends
magnitudes between habitats with different snow conditions and
observations. At a minimum we recommend reporting information on the
temporal sampling of the observations, including the number of
observations per year, when long term studies with Landsat observations
are undertaken.