# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 4 Discussion  
Data fusion approaches have been applied to synthesize images from multiple satellite remote sensing sources at different spatial and temporal scales [23; 24; 25; 26; 27; 28]. The simple linear regression model used in this study blended overlapping 250-m eight-day MODIS NDVI with finer spatial resolution (30-m), but less frequent Landsat NDVI data to generate a continuous NDVI record with 30-m resolution and eight-day temporal fidelity extending across MT from 2008 to 2015. A primary assumption of the data fusion approach is that the spatial pattern of the relationships between MODIS and Landsat NDVI are consistent over the sensor records. However, variations of MODIS and Landsat NDVI relationships may vary due to different acquisition dates, cloud contamination, aerosols, viewing angles and temporal compositing [24], or from sub-grid-scale spatial heterogeneity that is not resolved by the coarser MODIS footprint observations [26]; these factors can introduce uncertainties into the NDVI fusion results, which can propagate into model-derived _fPAR_ error. However, the _fPAR_ uncertainty is only one of several potential sources of LUE model GPP error. Other potential error sources include daily surface meteorological inputs and model BPLUT parameterizations defining maximum productivity rates and environmental response characteristics for different crop types. The LUE model BPLUT parameterization used in this study distinguishes two major cropland categories (cereal and broadleaf), while the model parameters were calibrated using a global network of tower eddy covariance measurement sites and are consistent with the SMAP L4C operational GPP product [16]. The SMAP L4C GPP product shows favorable accuracy and performance in global croplands [16], indicating that the BPLUT parameters are also suitable for MT croplands. Nevertheless, variability in plant characteristics can occur within these general cropland categories, which can lead to significant model GPP error [52]. However, both the MODIS MODI7 and SMAP L4C operational GPP products utilize a static land cover (LC) classification and 500-m resolution MODIS (MCD12Q1)_fPAR_ record as primary inputs to derive GPP at respective 500-m and 1-km spatial scales. In this study, a finer (30-m) resolution NASS Cropland Data Layer (CDL) and fused NDVI record was used to derive GPP. The GPP results from this study are thus expected to provide enhanced regional accuracy for agricultural applications due to the finer spatial delineation of cropland heterogeneity and model representation of dynamic cropland areas from the NASS CDL record. For example,  
Figure 8: The variations of estimated annual crop yields from this study (bars) and USDA NASS reported planted areas (lines) for each major crop type from 2008 to 2015 in Montana; vertical error bars denote spatial variability (standard deviation) in estimated yields. approximately 1.16 \(\times\) 10\({}^{5}\) hectares changed from cereal to broadleaf crop types, and 3.50 \(\times\) 10\({}^{5}\) hectares changed from broadleaf to cereal crops, while cropland area varied by 3.90 \(\times\) 10\({}^{5}\) hectares in MT from 2014 to 2015 based on the NASS CDL record; these changes are represented by our model results and influence the seasonal phenology, pattern and magnitude of estimated productivity and crop yields across the region. The resulting 30-m GPP simulations over MT croplands produced results that were generally similar, but more productive than the MODIS MOD17 (MOD17A2H, Collection 6) operational GPP product (Figure 4). However, the MODIS GPP product has been reported to have low productivity in croplands [18; 20; 53], indicating that the model results from this study benefit from a finer spatial resolution and a refined model cropland calibration. Though the Gridmet database shows favorable accuracy in relation to in situ weather station network measurements [49], the daily meteorological inputs may contribute uncertainties that propagate to cropland productivity model errors.