Publications

Dubinin, M; Osem, Y; Yakir, D; Paz-Kagan, T (2023). Satellite-based assessment of water use and leaf area efficiencies of dryland conifer forests along an aridity gradient. SCIENCE OF THE TOTAL ENVIRONMENT, 902, 165977.

Abstract
Dryland forests worldwide are increasingly threatened by drought stress due to climate change. Understanding the relationships between forest structure and function is essential for managing dryland forests to adapt to these changes. We investigated the structure-function relationships in four dryland conifer forests distributed along a semiarid to subhumid climatic aridity gradient. Forest structure was represented by leaf area index (LAI) and function by gross primary productivity (GPP), evapotranspiration (ET), and the derived efficiencies of water use (WUE = GPP/ET) and leaf area (LAE = GPP/LAI). Estimates of GPP and ET were based on the observed relationships between high-resolution vegetation indices from VEN mu S and Sentinel-2A satellites and flux data from three eddy covariance towers in the study regions between November 2015 to October 2018. The red-edge-based MERIS Terrestrial Chlorophyll Index (MTCI) from VEN mu S and Sentinel-2A showed strong correlations to flux tower GPP and ET measurements for the three sites (R-cal (2)> 0.91, R-val(2) > 0.84). Using our approach, we showed that as LAI decreased with decreasing aridity index (AI) (i.e., dryer conditions), estimated GPP and ET decreased (R-2 > 0.8 to LAI), while WUE (R-2 = 0.68 to LAI) and LAE increased. The observed global-scale patterns are associated with a variety of forest vegetation characteristics, at the local scale, such as tree species composition and density. However, our results point towards a canopy-level mechanism, where the ecosystem-LAI and resultant proportion of sun-exposed vs. shaded leaves are primary determinants of WUE and LAE along the studied climatic aridity gradient. This work demonstrates the importance of high-resolution (spatially and spectrally) remote sensing data conjugated with flux tower data for monitoring dryland forests and understanding the intricate structure-function interactions in their response to drying conditions.

DOI:
10.1016/j.scitotenv.2023.165977

ISSN:
1879-1026