15:45 〜 16:00
[ACG39-08] Estimation of leaf-level SIF from observed SIF for the reliable GPP calculation by remote sensing data in a cool temperate-deciduous broadleaf forest
キーワード：太陽子誘発クロロフィル蛍光、光合成、放射伝達モデル
Photosynthesis and ecosystem respiration are major processes to control the global carbon cycle. Solar-induced fluorescence (SIF) has been extensively used to estimate the Gross Primary production (GPP) at the ecosystem scale because the observed SIF (OSIF) exhibits a good correlation with net photosynthesis, which is quantified by the gas exchange monitoring method at the leaf level and by the eddy covariance method at the ecosystem scale. The canopy-scale SIF can be observed by ground-based spectroscopy and satellite sensors (e.g. GOSAT, GOSAT-2, OCO-2, etc.). These satellite-derived SIF data have the potential to estimate the global GPP because fluorescence occurs in the photosystem and it reflects the photosynthesis activity.
In the modeling approach, the relationship between GPP and SIF at leaf scale has been expressed by the energy distribution model which uses quantum yield against absorbed energy and rate coefficients. The model expresses the energy distribution at the leaf level. Thus, it is necessary to calculate the leaf-level SIF(LSIF) from OSIF (which is ecosystem-scale) to estimate the GPP, however, there are several uncertainties in the calculation process.
In this study, we focus on the three factors in the uncertainties; the contribution rate of OSIF to broad-band SIF, the fraction of emissions from photosystem (PS) II, and escape ratio to observation direction from the canopy. These factors are important in the calculation of LSIF from OSIF. However, it is difficult to observe these factors at the ecosystem scale. Thus, we estimate the factors using observable index, especially Green Red vegetation index (GRVI) and absorbed photosynthetically active radiation (APAR). In our method, we assume that the LSIF bears the linear relationship with OSIF and estimate the seasonal variation of the coefficient and intercept in the formula using GRVI and APAR. In addition, we validate the parameter values using observation data in a cool-temperate deciduous forest in Takayama, central Japan (AsiaFlux: JP-TKY) at ground observation and satellite observation by GOSAT.
As a result, we confirmed that the calibrated model showed better agreement with ground-observed GPP than photosynthesis models (De Pury and Farqhuhar’s model). From the comparison with satellite-observed GPP, our estimation shows similar seasonal variation although the ratio between LSIF and OSIF is validated by ground observation data. Thus, our method can apply to the satellite-based SIF and has the potential for large-scale estimation of GPP.
In the modeling approach, the relationship between GPP and SIF at leaf scale has been expressed by the energy distribution model which uses quantum yield against absorbed energy and rate coefficients. The model expresses the energy distribution at the leaf level. Thus, it is necessary to calculate the leaf-level SIF(LSIF) from OSIF (which is ecosystem-scale) to estimate the GPP, however, there are several uncertainties in the calculation process.
In this study, we focus on the three factors in the uncertainties; the contribution rate of OSIF to broad-band SIF, the fraction of emissions from photosystem (PS) II, and escape ratio to observation direction from the canopy. These factors are important in the calculation of LSIF from OSIF. However, it is difficult to observe these factors at the ecosystem scale. Thus, we estimate the factors using observable index, especially Green Red vegetation index (GRVI) and absorbed photosynthetically active radiation (APAR). In our method, we assume that the LSIF bears the linear relationship with OSIF and estimate the seasonal variation of the coefficient and intercept in the formula using GRVI and APAR. In addition, we validate the parameter values using observation data in a cool-temperate deciduous forest in Takayama, central Japan (AsiaFlux: JP-TKY) at ground observation and satellite observation by GOSAT.
As a result, we confirmed that the calibrated model showed better agreement with ground-observed GPP than photosynthesis models (De Pury and Farqhuhar’s model). From the comparison with satellite-observed GPP, our estimation shows similar seasonal variation although the ratio between LSIF and OSIF is validated by ground observation data. Thus, our method can apply to the satellite-based SIF and has the potential for large-scale estimation of GPP.