Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence?

Jochem Verrelst, Juan Pablo Rivera, Christiaan van der Tol, Federico Magnani, Gina H. Mohammed, J. Moreno

Remote Sensing of Environment · 2015 · 297 citations · 88 references

DOIFull text

Open access

Concepts

Abstract

This study provides insight into the key variables that drive sun-induced chlorophyll fluorescence (SIF) emanating from vegetation canopies , based on a global sensitivity analysis (GSA) of the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model. An updated version of the SCOPE model was used here (v1.53) which contains novel leaf physiological modules for determination of the steady state fluorescence yield: a photosynthesis model coupled with (a) submodels having empirically derived relationships, identified as TB12 for unstressed and TB12-D for drought conditions and (b) a mechanistic (MD12) submodel based on theoretical relationships. By inspecting Sobol's total order (main effect and all the interactions) sensitivity index ( S Ti ) rankings, the influential and non-influential variables were determined. Two experiments were conducted for the different leaf physiology modules in SCOPE considering (1) only vegetation variables, and (2) all SCOPE variables, i.e., including micrometeorological, aerodynamic and geometry variables. Considering TB12-D S Ti results using only vegetation input variables, the canopy-leaving broadband (641–800 nm) SIF variability was determined mainly by leaf optical properties and canopy structural variables. The most important variables were (with decreasing importance) leaf chlorophyll content (Cab), leaf inclination (LIDFa) and leaf area index (LAI). These three variables alone determined 77.9% of the SIF variability. V cmo , the variable related to photosynthetic capacity , determined 11.4% of overall SIF variability, and its importance declined considerably when moving from the first emission peak (SIF red ; with maximal relevance of 17.9% at 676 nm) to the second emission peak (SIF NIR ; e.g., 9.6% at 740 nm). Stronger relationships with V cmo were obtained when retrieving the full broadband SIF flux and calculating total fluorescence yield (F yield , determined as the integral of the hemispherical broadband SIF flux divided by the total absorbed PAR), of which 35% of the variability was influenced by V cmo . Using the TB12 submodel, the major drivers of SIF flux were similar to TB12-D except that V cmo accounted for very little (< 2%) variability. The MD12 submodel identified the components of long-term PSII photoprotection and photodamage as the dominant factors for SIF variability: these two variables alone accounted for 51.4% of the variability of SIF flux and 61% of F yield , whereas V cmo explained only 9.7% and 10.9% of variability in SIF flux and F yield , respectively. Analysis of the relative importance of all SCOPE variables revealed that in addition to the key vegetation variables, micrometeorological variables were important in driving SIF variability, especially incoming shortwave radiation (Rin) and to a lesser extent air temperature (Ta), atmospheric vapor pressure (ea) and atmospheric CO 2 concentration (Ca). Their impact further reduced the relative importance of V cmo . The GSA experiments led to the following conclusions: (1) explicit knowledge of key variables driving the SIF flux is essential in order to achieve unbiased SIF interpretation related to photosynthetic activity at local and global scales; (2) information related to photosynthetic activity is found more in the first emission peak (SIF red ) than in the second peak (SIF NIR ), and more in the full broadband SIF emission, which allows calculation of F yield , than in individual wavebands.

References

88