چکیده (انگلیسی):

Satellite remote sensing provides continuous observations of land surfaces, thereby offering opportunities for large-scale monitoring of terrestrial productivity. Production Efficiency Models (PEMs) have been widely used in satellite-based studies to simulate carbon exchanges between the atmosphere and ecosystems. However, model parameterization of the maximum light use efficiency (View the MathML source) varies considerably for croplands in agricultural studies at different scales. In this study, we evaluate cropland View the MathML source in the MODIS Gross Primary Productivity (GPP) model (MOD17) using in situ measurements and inventory datasets across the Midwestern US. The site-scale calibration using 28 site-years tower measurements derives View the MathML source values of 2.78 ± 0.48 gC MJ−1 (± standard deviation) for corn and 1.64 ± 0.23 gC MJ−1 for soybean. The calibrated models could account for approximately 60–80% of the variances of tower-based GPP. The regional-scale study using 4-year agricultural inventory data suggests comparable View the MathML source values of 2.48 ± 0.65 gC MJ−1 for corn and 1.18 ± 0.29 gC MJ−1 for soybean. Annual GPP derived from inventory data (1848.4 ± 298.1 gC m−2 y−1 for corn and 908.9 ± 166.3 gC m−2 y−1 for soybean) are consistent with modeled GPP (1887.8 ± 229.8 gC m−2 y−1 for corn and 849.1 ± 122.2 gC m−2 y−1 for soybean). Our results are in line with recent studies and imply that cropland GPP is largely underestimated in the MODIS GPP products for the Midwestern US. Our findings indicate that model parameters (primarily View the MathML source) should be carefully recalibrated for regional studies and field-derived View the MathML source can be consistently applied to large-scale modeling as we did here for the Midwestern US.