RT Journal Article
T1 Vegetation greenness sensitivity to precipitation and its oceanic and terrestrial component in selected biomes and ecoregions of the world
A1 Stojanovic, Milica
A1 Sorí Gómez, Rogert
A1 Guerova, Guergana
A1 Vázquez Domínguez, Marta
A1 Nieto Muñiz, Raquel Olalla
A1 Gimeno Presa, Luis
K1 2502.03 Bioclimatología
AB In this study, we conducted a global assessment of the sensitivity of vegetation greenness (VGS) to precipitation and to the estimated Lagrangian precipitation time series of oceanic (PLO) and terrestrial (PLT) origin. The study was carried out for terrestrial ecosystems consisting of 9 biomes and 139 ecoregions during the period of 2001–2018. This analysis aimed to diagnose the vegetative response of vegetation to the dominant component of precipitation, which is of particular interest considering the hydroclimatic characteristics of each ecoregion, climate variability, and changes in the origin of precipitation that may occur in the context of climate change. The enhanced vegetation index (EVI) was used as an indicator of vegetation greenness. Without consideration of semi-arid and arid regions and removing the role of temperature and radiation, the results show the maximum VGS to precipitation in boreal high-latitude ecoregions that belong to boreal forest/taiga: temperate grasslands, savannas, and shrublands. Few ecoregions, mainly in the Amazon basin, show a negative sensitivity. We also found that vegetation greenness is generally more sensitive to the component that contributes the least to precipitation and is less stable throughout the year. Therefore, most vegetation greenness in Europe is sensitive to changes in PLT and less to PLO. In contrast, the boreal forest/taiga in northeast Asia and North America is more sensitive to changes in PLO. Finally, in most South American and African ecoregions, where PLT is crucial, the vegetation is more sensitive to PLO, whereas the contrast occurs in the northern and eastern ecoregions of Australia.
PB Remote Sensing
SN 20724292
YR 2023
FD 2023-09-26
LK http://hdl.handle.net/11093/5202
UL http://hdl.handle.net/11093/5202
LA eng
NO Remote Sensing, 15(19): 4706 (2023)
NO Agencia Estatal de Investigación | Ref. PID2021-122314OB-I00
DS Investigo
RD 13-sep-2024