Impacts of light use efficiency and fPAR parameterization on gross primary production modeling

• Published in: Agricultural and forest meteorology Vol. 189-190; pp. 187 - 197
• Main Authors: Cheng, Yen-Ben, Zhang, Qingyuan, Lyapustin, Alexei I., Wang, Yujie, Middleton, Elizabeth M.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-06-2014; Elsevier
• Subjects: Agricultural and forest climatology and meteorology. Irrigation. Drainage; Agronomy. Soil science and plant productions; Biological and medical sciences; Carbon; Carbon modeling; Estimates; fAPARchl; Forests; Fraction of photosynthetically active radiation; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; Land; Light use efficiency; Mathematical models; MODIS; Monitoring; Parametrization; USA, Nebraska; USA, New Hampshire; fAPARchl; Light use efficiency; Carbon modeling; Fraction of photosynthetically active radiation; Biometeorology; Light effect; Primary productivity; Photosynthetically active radiation; Environmental factor; Parameterization; Carbon; Modeling; Radiation use efficiency; Models; fAPAR
• ISSN: 0168-1923; 1873-2240
• DOI: 10.1016/j.agrformet.2014.01.006

•We utilized different approaches to derive LUE and fPAR and examined their capabilities for carbon modeling.•Both crops, maize and soybean, and deciduous forest were selected to study.•fAPARchl showed closer seasonal dynamics with GPP than MODIS MOD15A2 fPAR.•Estimated GPP using fAPARchl was closer to flux tower based GPP.•Using a site-specific LUEmax value derived from an analytic approach can improve GPP estimates.
This study examines the impact of parameterization of two variables, light use efficiency (LUE) and the fraction of absorbed photosynthetically active radiation (fPAR or fAPAR), on gross primary production (GPP) modeling. Carbon sequestration by terrestrial plants is a key factor to a comprehensive understanding of the carbon budget at global scale. In this context, accurate measurements and estimates of GPP will allow us to achieve improved carbon monitoring and to quantitatively assess impacts from climate changes and human activities. Spaceborne remote sensing observations can provide a variety of land surface parameterizations for modeling photosynthetic activities at various spatial and temporal scales. This study utilizes a simple GPP model based on LUE concept and different land surface parameterizations to evaluate the model and monitor GPP. Two maize–soybean rotation fields in Nebraska, USA and the Bartlett Experimental Forest in New Hampshire, USA were selected for study. Tower-based eddy-covariance carbon exchange and PAR measurements were collected from the FLUXNET Synthesis Dataset. For the model parameterization, we utilized different values of LUE and the fPAR derived from various algorithms. We adapted the approach and parameters from the MODIS MOD17 Biome Properties Look-Up Table (BPLUT) to derive LUE. We also used a site-specific analytic approach with tower-based Net Ecosystem Exchange (NEE) and PAR to estimate maximum potential LUE (LUEmax) to derive LUE. For the fPAR parameter, the MODIS MOD15A2 fPAR product was used. We also utilized fAPARchl, a parameter accounting for the fAPAR linked to the chlorophyll-containing canopy fraction. fAPARchl was obtained by inversion of a radiative transfer model, which used the MODIS-based reflectances in bands 1–7 produced by Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. fAPARchl exhibited seasonal dynamics more similar with the flux tower based GPP than MOD15A2 fPAR, especially in the spring and fall at the agricultural sites. When using the MODIS MOD17-based parameters to estimate LUE, fAPARchl generated better agreements with GPP (r2=0.79–0.91) than MOD15A2 fPAR (r2=0.57–0.84). However, underestimations of GPP were also observed, especially for the crop fields. When applying the site-specific LUEmax value to estimate in situ LUE, the magnitude of estimated GPP was closer to in situ GPP; this method produced a slight overestimation for the MOD15A2 fPAR at the Bartlett forest. This study highlights the importance of accurate land surface parameterizations to achieve reliable carbon monitoring capabilities from remote sensing information.