Research On Canopy Conductance Model Based On Solar-induced Chlorophyll Fluorescence

The stomatal behavior plays a crucial role in regulating the coupling of carbon and water cycles in vegetation,directly affecting photosynthesis and water use efficiency.Consequently,it significantly influences plant growth,development,and capacity to adapt to their environment.Exploring the canopy conductance（G_c）of vegetation at a large scale is of paramount importance in comprehending the carbon and water exchange between plants and the atmosphere,as well as elucidating the response and adaptation mechanisms of ecosystems to global change.Currently,most simulation studies of G_c focus on the coupling model of stomatal conductance at the leaf level and photosynthesis.The prerequisite for obtaining accurate G_c is to optimize the photosynthesis model and couple it into the stomatal conductance model.Solar-induced chlorophyll fluorescence（SIF）,as a byproduct of photosynthesis,offers distinctive benefits in the physiological detection of vegetation photosynthesis and prompt identification of various stresses.Therefore,based on the critical role of stomata in regulating the carbon-water coupling process and inherent relationship between SIF and photosynthesis,this study establishes an ecosystem canopy conductance estimation model,G_c-SIF,driven by environmental factors(T_air,PAR,VPD,SWC,etc.)and plant physiological characteristics.The model is validated with ground flux observation data and applied to estimate the global canopy conductance for daily time scales from 2019 to 2020,investigating the spatiotemporal characteristics of G_c and the environmental factors affecting it in different drought regions globally.The primary computational processes and findings of this study include:（1）Construction and verification of G_c-SIF modelThis study utilized the reformulated mechanistic light response（r MLR）model based on SIF observations to calculate net photosynthesis rates and coupled it with the Ball Berry-Leuning stomatal conductance model,resulting in the development of the G_c-SIF model.This model estimates ecosystem canopy conductance by taking into account SIF observations and environmental factors.The G_c-SIF model is validated by comparing it with the G_c-EC model obtained from the Penman-Monteith formula and latent heat flux data collected from a fluorescence observation system and an EC flux tower at the Yangling Agricultural Experimental Station.The results showed good consistency between G_c-SIF and G_c-EC at half-hourly and daily time scales,with R² values of 0.79 and 0.75,respectively.Moreover,the response relationships between G_c-SIF and various environmental factors are consistent with other relevant research results,indicating that the G_c-SIF model constructed in this study has high fitting accuracy and can be used for simulating or estimating canopy transpiration.（2）Simulation and verification of global G_c-SIFThis study introduced remote sensing TROPOMI-SIF data,optimized some parameters,and upscaled the G_c-SIF model to the regional level to simulate global G_c-SIF at a spatial resolution of 5 kilometers for daily time scales from 2019 to 2020.The simulation results showed that G_c-SIF has significant regional differences and seasonal variations and can respond to phenomena such as abnormally high temperatures.The overall spatial distribution pattern shows obvious regional differences and latitudinal gradients corresponding to the global vegetation pattern.The daily average Gc-SIF values globally range from 0 to 0.35 mol m^-2 s^-1,with higher values in the central United States,Western Europe,and northeastern China and lower values in some arid and semi-arid areas.The G_c-SIF model shows a significant increase in most regions of the Northern Hemisphere from spring to summer,followed by a decrease from summer to autumn,and is generally lower in winter.The simulation results were validated using G_c-EC data from 14 FLUX tower sites,which revealed that the regression fitting of G_c-SIF and G_c-EC varied among different vegetation types but exhibited a significant positive correlation（p<0.01）.The average R² of the linear regression fitting for each site’s data is 0.41,and the overall fitting R² is 0.54,indicating that the G_c-SIF model used in this study has a good simulation effect on a global scale.（3）Environmental factors affecting global G_c-SIFThis study divided the world into five zones based on the Aridity Index:Hyper Arid,Arid,Semi-Arid,Dry sub-humid,and Humid,and analyzed the differences in G_c-SIF and their response to environmental factors in different regions.The results showed significant differences in G_c-SIF among different aridity zones globally,with lower average values in drier regions.The response patterns of G_c-SIF to major environmental factors were similar but not identical across different aridity zones.Generally,higher values of air temperature(T_air),photosynthetically active radiation（PAR）,and vapor pressure deficit（VPD）would increase plant transpiration rate and decrease leaf water potential,resulting in a decrease in G_c-SIF.This phenomenon was more pronounced in Hyper Arid regions,indicating that vegetation in these areas is more susceptible to water stress.The effect of soil water content（SWC）on increasing G_c-SIF had a certain threshold.From the correlation analysis between Gc-SIF and environmental factors at the daily scale from 2019 to 2020,G_c-SIF was positively correlated with T_air,PAR,and VPD in regions with sufficient water and suitable for vegetation growth,while it was negatively correlated in tropical where plants are more vulnerable to heat stress.The seasonal variation of SWC in most regions did not cause significant changes in G_c-SIF.The results of the geographic detector model analysis showed that SWC and its interaction with other environmental factors had a stronger explanatory power for the spatial distribution of G_c-SIF,while the explanatory power of PAR was weaker,indicating that soil moisture limitation was a more important factor affecting the spatial distribution of plant stomatal conductance.This study introduced the SIF as a probe for plant photosynthesis into the BBL stomatal conductance model,realizing the simulation of ecosystem G_c based on SIF observations.The model’s ability to simulate G_c was verified using flux observation SIF and remote sensing SIF at the site and global scales respectively.The study also explored the environmental factors affecting G_c,providing a reference for large-scale evapotranspiration modeling based on remote sensing and is of great significance for evaluating eco-hydrological processes under climate change.