# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 2 Data and Preprocessing
### Comparative Analysis of GPP Products  
MOD17 and GOSIF were chosen to assess the model's effectiveness. The spatial resolutions of the MOD17 and GOSIF are 500 m and 0.05\({}^{\circ}\), respectively, with both having a temporal resolution of eight days. MOD17 ([[https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv061/]([https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv061/), accessed on 22 April 2024) utilizes the LUE (light use efficiency) model, which integrates data such as PAR (photosynthetically active radiation), vegetation indices, temperature, and vapor pressure deficit to describe the photosynthesis process [36]. When calculating GPP, the LUE model combines these input data and uses the vegetation's light use efficiency to calculate the amount of carbon fixed by photosynthesis [37], as shown in Equation (1).  
\[\mathrm{GPP}=\mathrm{PAR}\times\mathrm{fPAR}\times\in \tag{1}\]  
where PAR is photosynthetically active radiation, FPAR is the fraction of PAR absorbed by vegetation, and \(\epsilon\) is the light use efficiency, representing the efficiency with which plants convert absorbed PAR into organic matter.  
GOSIF ([[http://globalecology.unh.edu/data/](http://globalecology.unh.edu/data/)]([http://globalecology.unh.edu/data/](http://globalecology.unh.edu/data/)), accessed on 22 April 2024), on the other hand, relies on OCO-2 satellite data and estimates GPP by measuring the byproduct of photosynthesis [38]. SIF is a measure of plant fluorescence emitted during photosynthesis  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product** & **Band** & \
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & **Source** \\ \hline GLASS & DSR & Daily & 0.05\({}^{\circ}\) & [[http://www.glass.umd.edu/](http://www.glass.umd.edu/)]([http://www.glass.umd.edu/](http://www.glass.umd.edu/)) (accessed on 16 April 2024) \\ \hline MCD43A4 & Band5 (1230–1250 nm) & Daily & 500 m & [[https://lpdaac.usgs.gov/products/mcd4](https://lpdaac.usgs.gov/products/mcd4)]([https://lpdaac.usgs.gov/products/mcd4](https://lpdaac.usgs.gov/products/mcd4)) 3a4v061/ (accessed on 19 April 2024) \\ \hline MOD11A1 & Band53 (10.780–11.280 μm) & Daily & 1 km & [[https://lpdaac.usgs.gov/products/mod1](https://lpdaac.usgs.gov/products/mod1)]([https://lpdaac.usgs.gov/products/mod1](https://lpdaac.usgs.gov/products/mod1)) 1a1v061/ (accessed on 19 April 2024) \\ \hline MODOCGA & Band11 (526–536 nm) & Daily & 1 km & [[https://lpdaac.usgs.gov/products/](https://lpdaac.usgs.gov/products/)]([https://lpdaac.usgs.gov/products/](https://lpdaac.usgs.gov/products/)) \\ \hline MOD13Q1 & EVI & 16 days & 250 m & [[https://lpdaac.usgs.gov/products/mod1](https://lpdaac.usgs.gov/products/mod1)]([https://lpdaac.usgs.gov/products/mod1](https://lpdaac.usgs.gov/products/mod1)) 3q1v061/ (accessed on 19 April 2024) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Remote sensing products and variables in this study.  
and is correlated with the intensity of this process. GOSIF combines EVI, land cover type, and meteorological data, using the Cubist regression tree model (based on [PERSON]'s M5 model tree) to create rule-based predictive models, where each rule is associated with a multivariate linear regression model, to calculate the GPP of surface vegetation [39]. Both the MODIS GPP and GOSIF GPP products have been subject to scrutiny in previous research efforts [40; 41; 42; 43; 44].