Leaf chlorophyll content (Chll) may serve as an observational proxy for the maximum rate of carboxylation (Vmax), which describes leaf photosynthetic capacity and represents the single most important control on modeled leaf photosynthesis within most Terrestrial Biosphere Models (TBMs). The parameterization of Vmax is associated with great uncertainty as it can vary significantly between plants and in response to changes in leaf nitrogen (N) availability, plant phenology and environmental conditions. Houborg et al. (2013) outlined a semi-mechanistic relationship between V max 25 (Vmax normalized to 25 °C) and Chll based on inter-linkages between V max 25 , Rubisco enzyme kinetics, N and Chll. Here, these relationships are parameterized for a wider range of important agricultural crops and embedded within the leaf photosynthesis-conductance scheme of the Community Land Model (CLM), bypassing the questionable use of temporally invariant and broadly defined plant functional type (PFT) specific V max 25 values. In this study, the new Chll constrained version of CLM is refined with an updated parameterization scheme for specific application to soybean and maize. The benefit of using in-situ measured and satellite retrieved Chll for constraining model simulations of Gross Primary Productivity (GPP) is evaluated over fields in central Nebraska, U.S.A between 2001 and 2005. Landsat-based Chll time-series records derived from the Regularized Canopy Reflectance model (REGFLEC) are used as forcing to the CLM. Validation of simulated GPP against 15 site-years of flux tower observations demonstrate the utility of Chll as a model constraint, with the coefficient of efficiency increasing from 0.91 to 0.94 and from 0.87 to 0.91 for maize and soybean, respectively. Model performances particularly improve during the late reproductive and senescence stage, where the largest temporal variations in Chll (averaging 35–55 μg cm−2 for maize and 20–35 μg cm−2 for soybean) are observed. While prolonged periods of vegetation stress did not occur over the studied fields, given the usefulness of Chll as an indicator of plant health, enhanced GPP predictabilities should be expected in fields exposed to longer periods of moisture and nutrient stress. While the results support the use of Chll as an observational proxy for V max 25 , future work needs to be directed towards improving the Chll retrieval accuracy from space observations and developing consistent and physically realistic modeling schemes that can be parameterized with acceptable accuracy over spatial and temporal domains.
|Original language||English (US)|
|Number of pages||17|
|Journal||International Journal of Applied Earth Observation and Geoinformation|
|State||Published - May 5 2015|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The research undertaken here was funded by the King Abdullah University of Science and Technology (KAUST). We appreciate the data provided by the Center for Advanced Land Management Information Technologies (CALMIT) and the Carbon Sequestration Program, University of Nebraska-Lincoln. This work was supported in part by International Incoming Marie Curie fellowship to Anatoly Gitelson.
This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO, GTOS, TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California, Berkeley and the University of Virginia.