# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 4 Discussion
Moreover, finer scale heterogeneity in surface meteorology not resolved by the 4-km Gridmet spatial resolution may contribute to model GPP errors derived at 30-m resolution. Management factors, including fertilization and irrigation practices, are not directly considered in our model and may also introduce uncertainties into the resulting GPPM calculations. Despite the potential model uncertainties, the modeled crop production (P\({}_{\text{M}}\)) results were favorably correlated with the county-scale NASS annual crop production (P\({}_{\text{N}}\)) records for all seven major crop types in MT during the 2008 to 2015 study period (\(r\) = 0.96, relative bias = \(-\)7.8%, relative RMSE = 37.0%). The county level model performance is robust, and the spatial fidelity achieved with the fusion of sensors allows delineation of production variability within a county, which is an important indicator of potential imbalances within a region. However, the estimated crop yields (YieldM) showed lower correspondence with field-scale yield (YieldF) records (\(r\) = 0.44). Potential reasons for the lower model performance in estimated field-scale yields are discussed below, but when interpreting the performance metrics, it is important to bear in mind that the state-wise harvest index (HI\({}_{\text{GPP}}\)) used to derive YieldM from the 30-m GPP calculations was calibrated using county-level NASS annual crop yield data (YieldN), which represents a major source of model YieldM uncertainty at finer spatial scales. A second consideration is that the YieldF observations used for model validation were obtained from a very limited set of 10 m by 10 m plots at four farm locations, which may not adequately reflect the 30-m resolution YieldM estimates derived across MT using coarser scale satellite NDVI observations and ancillary data. Uncertainty in the CDL files used to delineate annual crop types and planted areas in this study may also contribute to low YieldM and YieldF correspondence. Indeed, the reported CDL accuracy from 2008 to 2015 across MT croplands varies from 69.6% (2010) to 85.0% (2014), while the CDL accuracy levels may be inflated because they do not account for edge effects [54]. CDL accuracy may also be degraded in regions with sparse or complex agriculture [55], which is characteristic of MT croplands. Alternatively, reported crop yields for spring wheat in Lethbridge, Canada, near the MT border, ranged from 1.5 to 1.8 tons/hectare [56]; reported spring wheat yields also ranged from 0.5 to less than 3 tons/hectare in arid and semi-arid areas of the Canadian Prairies [22]. These reported yields are similar to our model Yieldm results for MT spring wheat (1.27 \(\pm\) 0.27 tons/hectare). The mean modeled crop yield during 2008 to 2015 for barley in this study is 2.48 \(\pm\) 0.61 tons/hectare, which is also similar to reported barley yields on the Canadian Prairies (0.9 to 4.5 tons/hectare; [22]). For alfalfa, the resulting crop yield estimates range from 1.58 to 8.06 tons/hectare, while another study reported alfalfa yields from 2.97 to 14.72 tons/hectare [57], similar to our model results. Moreover, alfalfa yields reported by the Food and Agricultural Organization range from 5 to 17 tons/hectare under rainfed conditions with 500 to 800 mm annual rainfall [58]; these values are similar to our model yields for MT alfalfa and indicate that the calibrated HI\({}_{\text{GPP}}\) parameter used in this investigation is reasonable, though it is higher than the HI values for this crop type reported from prior limited studies. Thus, our model estimates of crop yield and production may be suitable for regional cropland productivity and yield assessments that can inform agricultural decisions. These findings are consistent with a similar regional analysis of smallholder agricultural productivity across Africa indicating that high-resolution satellite imagery can be used to make crop yield predictions that are roughly as accurate as survey-based measurements [59].