Remote Sensing, Vol. 11, Pages 1688: Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest (Remote Sensing)

The Midwestern US is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production, and the carbon dynamics of this region are dominated by these production systems. An accurate regional estimate of gross primary production (GPP) is imperative and requires upscaling approaches. The aim of this study was to upscale corn and soybean GPP (referred to as GPPcalc) in four counties in Central Iowa in the 2016 growing season (DOY 145–269). Eight eddy-covariance (EC) stations recorded carbon dioxide fluxes of corn (n = 4) and soybean (n = 4), and net ecosystem production (NEP) was partitioned into GPP and ecosystem respiration (RE). Additional field-measured NDVI was used to calculate radiation use efficiency (RUEmax). GPPcalc was calculated using 16 MODIS satellite images, ground-based RUEmax and meteorological data, and improved land use maps. Seasonal NEP, GPP, and RE ( x ¯ ± SE) were 678 ± 63, 1483 ± 100, and −805 ± 40 g C m−2 for corn, and 263 ± 40, 811 ± 53, and −548 ± 14 g C m−2 for soybean, respectively. Field-measured NDVI aligned well with MODIS fPAR (R2 = 0.99), and the calculated RUEmax was 3.24 and 1.90 g C MJ−1 for corn and soybean, respectively. The GPPcalc vs. EC-derived GPP had a RMSE of 2.24 and 2.81 g C m−2 d−1, for corn and soybean, respectively, which is an improvement to the GPPMODIS product (2.44 and 3.30 g C m−2 d−1, respectively). Corn yield, calculated from GPPcalc (12.82 ± 0.65 Mg ha−1), corresponded well to official yield data (13.09 ± 0.09 Mg ha−1), while soybean yield was overestimated (6.73 ± 0.27 vs. 4.03 ± 0.04 Mg ha−1). The approach presented has the potential to increase the accuracy of regional corn and soybean GPP and grain yield estimates by integrating field-based flux estimates with remote sensing reflectance observations and high-resolution land use maps.