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Regional-scale wilting point estimation using satellite SIF, radiative-transfer inversion, and soil-vegetation-atmosphere transfer simulation: A grassland study
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  • Tomoki Kiyono,
  • Hibiki Noda,
  • Tomo'omi Kumagai,
  • Haruki Oshio,
  • Yukio Yoshida,
  • Tsuneo Matsunaga,
  • Kouki Hikosaka
Tomoki Kiyono
National Institute for Environmental Studies

Corresponding Author:kiyono.tomoki@nies.go.jp

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Hibiki Noda
National Institute for Environmental Studies
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Tomo'omi Kumagai
Nagoya University
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Haruki Oshio
Tokyo Institute of Technology
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Yukio Yoshida
National Institute for Environmental Studies
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Tsuneo Matsunaga
National Institute for Environmental Studies (in Japan)
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Kouki Hikosaka
Tohoku University
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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.