Regional-scale wilting point estimation using satellite SIF,
radiative-transfer inversion, and soil-vegetation-atmosphere transfer
simulation: A grassland study
Abstract
Although water availability strongly controls gross primary production
(GPP), the impact of soil moisture content (wilting point) is poorly
quantified on regional and global scales. In this study, we used 10-year
observations of solar-induced chlorophyll fluorescence (SIF) from the
GOSAT satellite to estimate the wilting point of a semiarid grassland on
the Mongolian Plateau. Radiative-transfer model inversion and
soil-vegetation-atmosphere transfer simulation were jointly conducted to
distinguish the drought impacts on physiology from changes in
leaf-canopy optical properties. We modified an existing inversion
algorithm and the widely used SCOPE model to adequately evaluate dryland
features, e.g., sparse canopy and strong convection. The modified model
with retrieved parameters and calibrated to GOSAT SIF predicts realistic
GPP values. We found that (1) the SIF yield estimated from GOSAT shows a
clear sigmoidal pattern in relation to drought, and the estimated
wilting point matches ground-based observations within
~0.01 m3 m-3 for the soil moisture content, (2) tuning
the maximum carboxylation rate improves SIF prediction after considering
changes in leaf-canopy optical properties, implying that GOSAT detected
drought stress in leaf-level photosynthesis, and (3) the surface energy
balance has significant impacts on the grassland’s SIF; the modified
model reproduces observed SIF radiance well (mean bias = 0.004 mW m-2
nm-1 sr-1 in summer), whereas the original model predicts substantially
low values under weak horizontal wind (unstable) conditions. Some
model-observation mismatches in the SIF suggest that more research is
needed for fluorescence parametrization (e.g., photoinhibition) and
additional observation constraints.