Innovation from the source of the National Research, Development and Innovation Fund,
the Hungari...(NKFIH FK-146600) Támogató: Kulturális és Innovációs Minisztérium Nemzeti
Kutatási Fejlesztési és Innovációs Alap
TKP2021-NVA-29(TKP2021-NVA-29) Támogató: Nemzeti Kutatási, Fejlesztési és Innovációs
Hivatal
(KDP-2021)
(143697)
Monitoring the condition of grasslands is essential given their vital role in food
security, carbon sequestration and other ecosystem services. Harvested aboveground
biomass (HAB) and aboveground net primary production (ANPP) are among the most important
grassland state indicators. However, spatially explicit production estimates are largely
lacking, and grassland area estimations also remain uncertain. This study addresses
these gaps for drought-prone Central European grasslands over 2017–2024. We synthesized
grassland extent data, collected extensive field measurements on biomass (BM), and
used remote sensing-based biophysical proxies to build an ensemble of six linear models
for spatial extrapolation at 10 m resolution. Bayesian framework was used for the
linear model fitting that also considers uncertainty of the observations. The ensemble
mean ANPP was 310.7 ± 19 gBM m−2, with modest interannual variability. Upscaled country-wide
mean ANPP was 34.3 ± 13.3 Mt year−1. The results indicate that, within the frame of
the present study, the remote sensing-based linear model selection has a larger influence
on the country totals than the grassland area database selection. The results highlight
that both grassland area uncertainty and model construction are major sources of uncertainty
in biomass estimation that have to be addressed in future studies.
