Natural Resources, School of


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Published in Agricultural and Forest Meteorology 201 (2015), pp. 111–119; doi: 10.1016/j.agrformet.2014.11.004


Copyright © 2014 Elsevier B.V. Used by permission.


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 (ε*GPP) varies considerably for croplands in agricultural studies at different scales. In this study, we evaluate cropland ε*GPP 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 ε*GPP values of 2.78 ± 0.48 gC MJ−1(± standard deviation) for corn and 1.64 ± 0.23 gC MJ−1for 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 ε*GPP 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−2y−1 for corn and 908.9 ± 166.3 gC m−2y−1 for soybean) are consistent with modeled GPP (1887.8 ± 229.8 gC m−2y−1 for corn and 849.1 ± 122.2 gC m−2y−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 ε*GPP) should be carefully recalibrated for regional studies and field-derived ε*GPP can be consistently applied to large-scale modeling as we did here for the Midwestern US.