# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 3 Results
### Regional GPP Estimation over Montana  
The fused NDVI record was used with Gridmet daily meteorological data and the yearly NASS CDL as primary inputs to the LUE model (Equations (1) to (4)) to derive 30-m eight-day GPP (GPP\({}_{\rm M}\)) across MT from April to September for the seven major crop types and eight-year (2008 to 2015) study period. The mean GPP\({}_{\rm M}\) (2008 to 2015) results across the MT domain showed distinct spatial patterns corresponding to the different crop types. The GPP\({}_{\rm M}\) results showed higher productivity (>4 g C m\({}^{-2}\) day\({}^{-1}\)) in the northwest and central areas, where alfalfa and barley were located. Higher GPP\({}_{\rm M}\) values were also found in the southeast portion of MT associated with maize production. The central and northeast areas of MT showed slightly lower GPP\({}_{\rm M}\) (<2.8 g C m\({}^{-2}\) day\({}^{-1}\)) associated with durum wheat, spring wheat and winter wheat production. The GPP\({}_{\rm M}\) mean eight-day climatology derived from April to September over the 2008 to 2015 study period is compared with the MODIS GPP (MOD17A2H) mean eight-day climatology for the same period from randomly-selected pixels representing the different MT crop types in Figure 4. Here, both GPP\({}_{\rm M}\) and MODIS GPP were extracted as the spatial mean values within 1 \(\times\) 1 km\({}^{2}\) windows representing each major crop type. The GPP\({}_{\rm M}\) results showed consistent seasonal variations, but different magnitudes among the different MT crop types relative to the MOD17A2H global GPP product (Figure 4). The GPP\({}_{\rm M}\) for alfalfa showed stronger annual variations than the other crop types during the 2008 to 2015 study period, indicated by a larger temporal standard deviation. The GPP\({}_{\rm M}\) results also showed less temporal variability than MODIS GPP for barley, peas, spring wheat and winter wheat, relative to the other crop types. At US-FPe, the GPP\({}_{\rm M}\) mean eight-day climatology derived from the 2008 to 2015 record was compared with the GPP\({}_{\rm F}\) during the growing season (\(r\) = 0.77), but with a relatively large bias (1.73 g C m\({}^{-2}\) day\({}^{-1}\)) and RMSE (2.16 g C m\({}^{-2}\) day\({}^{-1}\), Figure 4); these results are consistent with the GPP\({}_{\rm M}\) depiction of a cereal cropland for this site relative to the GPP\({}_{\rm F}\) observations representing a less productive natural grassland [(51)]. The GPP\({}_{\rm M}\) results also showed stronger correspondence with the MODIS GPP record (\(r\) = 0.97, \(p\) < 0.05) at the US-FPe site than with the GPP\({}_{\rm F}\), including smaller positive GPP\({}_{\rm M}\) bias. The eight-day 30-m GPP\({}_{\rm M}\) results were compared with the eight-day MODIS (MOD17A2H) 500-m GPP record over the entire 2008 to 2015 study period for each selected MT crop type location, and  
Figure 3: Seasonal NDVI variations for randomly-selected pixels representing the major MT crop types as derived from the different sensor records for the 2008 to 2015 growing season (April to September), including: Landsat 5 (LS5) and Landsat 7 (LS7) after removing cloud contamination; the MODIS MOD9Q1 product (MODIS); fusion of MODIS MOD09Q1 with Landsat 5 (LS5_fused) and Landsat 7 (LS7_fused). The MT crop types represented include alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat, while the locations of the selected pixels representing each crop type are indicated in the upper left, with the background image representing the seven major MT crop types depicted from the 2015 Cropland Data Layer (CDL). summarized in Table 4. The eight-day GPPM results showed generally favorable correspondence (\(0.38\leq r\leq 0.92\)) with the MODIS GPP record for all seven major MT crop types.). For alfalfa, the GPPM results were relatively consistent with the MODIS GPP record from April to mid-June, but with a more productive bias between mid-July and early September (e.g., Figure 4).