# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
Data fusion methods have been used to reduce the constraints of single sensor remote sensing by blending similar spectral information from Landsat and MODIS to generate harmonized multi-sensor observations, providing both relatively fine-scale spatial resolution and frequent temporal sampling [22; 25; 26; 27; 28; 29; 30]. The spatial and temporal adaptive reflectance fusion model (STARFM), developed by [25], has been widely used for blending surface reflectance data from Landsat and MODIS. The STARFM approach was modified and improved by [23] for more complex situations. However, the STARFM model is computationally expensive, which can impose a constraint on regional applications. Alternatively,a rule-based piecewise regression model based on MODIS and Landsat data was developed by [31] to derive 30-m NDVI (Normalized Difference Vegetation Index) maps over northeastern Colorado. A simple linear relationship between NDVI and _fPAR_ (fraction of photosynthetically-active radiation) was also used to downscale 250-m MODIS _fPAR_ data to 30-m resolution using Landsat TM NDVI data [32]. Similar vegetation information may be used as primary inputs to a satellite-based LUE model to derive GPP with enhanced spatial and temporal resolution suitable for agricultural applications. Although GPP represents the total amount of carbon accumulation in vegetation biomass from photosynthesis, most agricultural applications are concerned with estimating crop grain yield, which generally represents a much smaller portion of GPP. Crop yield can be estimated as the product of GPP and an empirical harvest index (HI) that defines the conversion ratio between crop GPP and grain yield, with HI varying for different crop types and environmental conditions [33; 34; 35; 36; 37]. MODIS GPP was converted to wheat yield for the 2001 and 2002 growing seasons in Montana and North Dakota by [38]. The resulting county-level estimated wheat yields from this study showed relatively low correlations with observed wheat yield in Montana (R\({}^{2}\) value of 0.46 and 0.33) and North Dakota (R\({}^{2}\) of 0.06 and 0.16), respectively; however, the model results explained 67% and 33% of the total variance in observed wheat yield over both states [38]. Inaccurate HI settings may propagate to significant errors in the final crop yield calculations; however, the integration of regional survey data can improve HI accuracy and reduce uncertainties in crop yield predictions. The U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) provides annual crop yield and harvested area information at the county level for specific crop types within the continental USA, which can be used as a benchmark for crop model calibration (e.g., HI) and validation. Google Earth Engine (GEE) is a cloud-based platform designed for efficient planetary-scale geospatial analysis. GEE has a large data catalog co-located with massive CPU that allows for interactive data exploration, providing an easy and quick way to process and analyze data [39]. GEE has been used for mapping crop yields with satellite data spanning multiple states and years (2000 to 2013) in the Midwestern United States [40]. GEE has also been used for other hydrological and ecological applications, including mapping global forest change [41], detecting global surface water change [42] and generating a dynamic Landsat-based NDVI product for the Conterminous United States [43]. In this paper, we show that a global satellite data-driven productivity model (i.e., the MODIS MOD17 algorithm) can be adapted for regional crop assessment. Our approach is significant because it enables crop monitoring over large regions through the modification and application of existing operational satellite remote sensing data and models. We demonstrate the model approach by characterizing the spatial extent and annual variability in cropland productivity (GPP) and yield over the state of Montana in the continental USA. Montana's agriculture is mostly rainfed, and production is very sensitive to climate variability.