Abstract
How well parameterization will improve gross primary production (GPP) estimation using the MODerate-resolution Imaging Spectroradiometer (MODIS) algorithm has been rarely investigated. We adjusted the parameters in the algorithm for 21 selected eddy-covariance flux towers which represented nine typical plant functional types (PFTs). We then compared these estimates of the MOD17A2 product, by the MODIS algorithm with default parameters in the Biome Property Look-Up Table, and by a two-leaf Farquhar model. The results indicate that optimizing the maximum light use efficiency (εmax) in the algorithm would improve GPP estimation, especially for deciduous vegetation, though it could not compensate the underestimation during summer caused by the one-leaf upscaling strategy. Adding the soil water factor to the algorithm would not significantly affect performance, but it could make the adjusted εmax more robust for sites with the same PFT and among different PFTs. Even with adjusted parameters, both one-leaf and two-leaf models would not capture seasonally photosynthetic dynamics, thereby we suggest that further improvement in GPP estimaiton is required by taking into consideration seasonal variations of the key parameters and variables.
Highlights
Gross primary production (GPP), which is the amount of light energy from the sun converted to chemical energy, determines the thermal, water and biogeochemical cycles in terrestrial ecosystems.even when eddy covariance (EC) flux data and remote sensing data are conjunctively introduced into various diagnostic models, uncertainties in modeled gross primary production (GPP) are still vast
By comparing GPP estimates with parameter adjustment in the MODerate-resolution Imaging Spectroradiometer (MODIS) algorithm across nine plant functional types (PFTs) at half-hourly, daily, monthly and seasonal scales, our multisite study illustrates as follows: (1) Large bias was observed in the MODIS GPP product, especially in deciduous forests and shrubs and grasslands
It is necessary to optimize the parameters in the look-up table used by the MODIS algorithm, but the optimized parameters should correspond to specific input data for applications, i.e., the optimized parameters cannot be applied to a simulation with changed driver data because errors from parameters and input data can accumulate
Summary
Gross primary production (GPP), which is the amount of light energy from the sun converted to chemical energy, determines the thermal, water and biogeochemical cycles in terrestrial ecosystems.even when eddy covariance (EC) flux data and remote sensing data are conjunctively introduced into various diagnostic models, uncertainties in modeled GPP are still vast. One of the widely accepted approaches is the light use efficiency model because of both its simple structure—which assumes that a fraction of the photosynthetically active radiation (PAR) absorbed by the vegetation canopy is used for plant primary production [9]—and its large amount of available input data, including EC measurements [12,13,14,15] and remotely sensed data [16,17,18] One application of this kind of model is the MODIS GPP algorithm [19]. Its latest product MOD17A2 Collection 5 (https://lpdaac.usgs.gov/products) is forced by the National Center for Environmental Prediction–Department of Energy (NCEP-DOE)
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