Impact of data density and endmember definitions on long-term trends in ground cover fractions across European grasslands

Long-term monitoring of grasslands is pivotal for ensuring continuity of
many environmental services and for supporting food security and
environmental modelling. Remote sensing provides an irreplaceable source
of information for studying changes in grasslands. Specifically, Spectral
Mixture Analysis (SMA) allows for quantification of physically meaningful
ground cover fractions of grassland ecosystems (i.e., green vegetation,
non-photosynthetic vegetation, and soil), which is crucial for our
understanding of change processes and their drivers. However, although
popular due to straightforward implementation and low computational cost,
‘classical’ SMA relies on a single endmember definition for each targeted
ground cover component, thus offering limited suitability and
generalization capability for heterogeneous landscapes. Furthermore, the
impact of irregular data density on SMA-based long-term trends in
grassland ground cover has also not yet been critically addressed. We
conducted a systematic assessment of i) the impact of data density on
long‑term trends in ground cover fractions in grasslands; and ii) the
effect of endmember definition used in ‘classical’ SMA on pixel- and
map-level trends of grassland ground cover fractions. We performed our
study for 13 sites across European grasslands and derived the trends based
on the Cumulative Endmember Fractions calculated from monthly composites.
We compared three different data density scenarios, i.e., 1984-2021
Landsat data record as is, 1984-2021 Landsat data record with the monthly
probability of data after 2014 adjusted to the pre‑2014 levels, and the
combined 1984-2021 Landsat and 2015-2021 Sentinel-2 datasets. For each
site we ran SMA using a selection of site‑specific and generalized
endmembers, and compared the pixel- and map-level trends. Our results
indicated no significant impact of varying data density on the long-term
trends from Cumulative Endmember Fractions in European grasslands.
Conversely, the use of different endmember definitions led in some regions
to significantly different pixel- and map-level long‑term trends raising
questions about the suitability of the ‘classical’ SMA for complex
landscapes and large territories. Therefore, we caution against using the
‘classical’ SMA for remote‑sensing‑based applications across broader
scales or in heterogenous landscapes, particularly for trend analyses, as
the results may lead to erroneous conclusions.