Estimating global transpiration from TROPOMI SIF with angular normalization and separation for sunlit and shaded leaves

Gross primary productivity (GPP) is more accurately estimated by total canopy solar-induced chlorophyll fluorescence ( SIF total ) compared to raw sensor observed SIF signals ( SIF obs ). The use of two-leaf strategy, which distinguishes between SIF from sunlit ( SIF sunlit ) and shaded ( SIF shaded ) leaves, further improves GPP estimates. However, the two-leaf strategy, along with SIF corrections for bidirectional effects, has not been applied to transpiration (T) estimation. In this study, we used the angular normalization method to correct the bidirectional effects and separate SIF sunlit and SIF shaded . Then we developed SIF sunlit and SIF shaded driven semi-mechanistic and hybrid models, comparing their T estimates with those from a SIF obs driven semi-mechanistic model at both site and global scales. All three types of SIF-driven T models integrate canopy conductance ( g c ) with the Penman-Monteith model, differing in how g c is derived: from a SIF obs driven semi-mechanistic equation, a SIF sunlit and SIF shaded driven semi-mechanistic equation, and a SIF sunlit and SIF shaded driven machine learning model. When evaluated against partitioned T using the underlying water use efficiency method at 72 eddy covariance sites and two global T remote sensing products, a consistent pattern emerged: SIF sunlit and SIF shaded driven hybrid model > SIF sunlit and SIF shaded driven semi-mechanistic model > SIF obs driven semi-mechanistic model. The SIF sunlit and SIF shaded driven hybrid model demonstrated a notable proficiency under high vapor pressure deficit and low soil water content conditions. The SIF obs driven semi-mechanistic model tends overestimate T at low T values, and this issue is significantly alleviated by the SIF sunlit and SIF shaded driven semi-mechanistic and hybrid models. Our findings demonstrate that correcting the bidirectional effects and using the two-leaf strategy on GPP estimation can improve T estimation and provide a new global T product incorporating vegetation physiological signal.