# Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data
## 2 Materials and Methods
### Satellite Based Crop GPP and Yield Modeling  
#### 2.2.1 Developing 30-m MODIS-Landsat Fused NDVI Maps for MT from 2008 to 2015  
All data processing and modeling work in this study was conducted in the Google Earth Engine (GEE) framework, which has an extensive library of useful datasets and functions, while providing an efficient and quick way to process and analyze large geospatial datasets. The geospatial data used in this study are summarized in Table 1. The 30-m resolution surface spectral reflectance data records generated from the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS, ([PERSON] et al., 2015)) from Landsat 5 (2008 to 2011) and Landsat 7 (2012 to 2015) were extracted and processed over the MT domain. Through LEDAPS, lower order spectral imagery from Landsat 5 (or 7) were calibrated, converted to top-of-atmosphere reflectance and then atmospherically corrected using the MODIS/6S methodology to produce the surface reflectance product ([PERSON] et al., 2015). Cloud screening was applied to the reflectance data to remove cloud-contaminated pixels according to cloud mask information provided by the surface reflectance data products ([PERSON] et al., 2015). The surface reflectance data for Landsat Bands 3 (red: 0.63 to 0.69 \(\upmu\)m) and 4 (near-infrared: 0.76 to 0.90 \(\upmu\)m) were then selected to calculate the 30-m resolution NDVI for each image sequence. Simultaneously, the MODIS 250-m 8-day global surface reflectance product (MOD9Q1), after masking out cloud-contaminated pixels, was used to derive the MODIS NDVI record over the MT domain and 2008 to 2015 study period. Both Landsat and MODIS NDVI data were reprojected to a consistent geographic projection using the WGS4 datum. Since the MODIS (250-m 8-day) and Landsat (30-m) records spanned the same MT domain and period, we assumed that the NDVI retrievals from the different sensor records are consistent and comparable. Therefore, a pixel-wise linear regression model was applied to blend the Landsat and MODIS NDVI records to construct a fused NDVI record with consistent 30-m spatial resolution and 8-day temporal fidelity over the MT domain and for each year of record from 2008 to 2015 ([[https://code.earthengine.google.com/1](https://code.earthengine.google.com/1) fb49 cd39a59 bf1d6d653 bc04499c690]([https://code.earthengine.google.com/1](https://code.earthengine.google.com/1) fb49 cd39a59 bf1d6d653 bc04499c690)). The linear regression model describing the relationship between Landsat and MODIS NDVI was developed and applied on a pixel basis. The fused 30-m NDVI record was derived using the resulting slopes and intercepts from the linear regression model and the MODIS NDVI data. The underlying assumption of Landsat and MODIS spectral consistency used to derive the fused NDVI record was determined to have a suitable signal-to-noise ratio for our agricultural application, but may be insufficient for distinguishing subtler environmental trends.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Time Period** & **Spatial Resolution** & **Temporal Resolution** \\ \hline Landsat 5 surface reflectance & 2008 to 2011 & 30-m & 16 day \\ Landsat 7 surface reflectance & 2012 to 2015 & 30-m & 16 day \\ MOD9Q1 & 2008 to 2015 & 250-m & 8 day \\ MOD17A2H GPP & 2008 to 2015 & 500-m & 8 day \\ Flux tower based GPP & 2000 to 2006 & 1-km & daily \\ Cropland Date Layer & 2008 to 2015 & 30-m & annual \\ Gridded Surface Meteorological Dataset & 2008 to 2015 & 4-km & daily \\ USDA NASS crop yield/production data & 2008 to 2015 & County & annual \\ Crop yield field measurements & 2008 to 2015 & 10-m & annual \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of data used in this study.