# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Data Collection and Processing
#### 2.2.3 Vegetation Indices Computation  
VIs were also included in the feature dataset, namely the enhanced vegetation index 2 (EVI2), the GRVI, the NDVI, and the SAVI (Table 2). The use of the EVI2 in crop yield prediction models is justified by its many advantages, and has been shown to achieve higher prediction accuracy compared to other VIs, such as the NDVI [32]. It also offers higher sensitivity, especially in areas with high biomass, and provides valuable information on crop conditions and yields [33, 34]. Regarding the GRVI, it is often used as a phenological indicator, detecting changes in canopy vegetation [35]. Moreover, in a study by [PERSON] et al. [36], the GRVI showed a high correlation with sugarcane yields. Concerning the NDVI, it can be implemented to monitor crop growth, detect plant stress, and make decisions regarding irrigation, fertilization, pesticide application, and has also been employed in numerous studies to accurately predict crop yields [37, 38]. The SAVI, in turn, is also a suitable VI for use in yield prediction models, since it attempts to minimize the effects of soil brightness using a correction factor [39]. It is similar to the NDVI, but accounts for variations in soils, making it useful in arid and semi-arid regions, where vegetation cover is low and soil brightness can significantly affect vegetation detection [40]. Furthermore, in a study by [PERSON] et al. [41], the SAVI had the highest correlation with soybean grain yield, possibly due to the use of the soil effect correction, demonstrating its ability to predict crop yields. As previously mentioned, three different platforms, each with varying spatial resolutions, were used to obtain the VIs (Section 2.2.2). The data from each platform were processed, and atmospheric corrections were implemented before it became available. \begin{table}
\begin{tabular}{c c c c c} \hline
**Platform/ Satellite** & **Sensor** & **Product** & \begin{tabular}{c} **Spatial** \\ **Resolution (m)** \\ \end{tabular} & \begin{tabular}{c} **Revisiting** \\ **Time** \\ \end{tabular} \\ \hline \multirow{4}{*}{GSMaP} & Multi-Band Passive & \multirow{4}{*}{\begin{tabular}{c} Hourly \\ Precipitation \\ Rate \\ \end{tabular} } & \multirow{4}{*}{11,000} & \multirow{4}{*}{3 h} \\  & & & & \\ \multirow{4}{*}{Terra} & \multirow{4}{*}{\begin{tabular}{c} Moderate- \\ Resolution Imaging \\ Spectroradiometer \\ (MODIS) \\ \end{tabular} } & \multirow{4}{*}{1000} & \multirow{4}{*}{1 day} \\  & & & & \\ \multirow{4}{*}{Landsat 8} & \multirow{4}{*}{\begin{tabular}{c} Davigline and \\ Nighttime Land \\ Surface \\ Temperature \\ (LST) \\ \end{tabular} } & \multirow{4}{*}{30} & \multirow{4}{*}{16 days} \\  & & & & \\ \multirow{2}{*}{Landsat 8} & \multirow{2}{*}{\begin{tabular}{c} Operational Land \\ Imager (OLI) \\ Multispectral \\ Instrument (MSI) \\ \end{tabular} } & \multirow{2}{*}{30} & \multirow{2}{*}{16 days} \\  & & & & \\ \multirow{2}{*}{\
\begin{tabular}{c} Davigline and NIR \\ bands \\ \end{tabular} } & \multirow{2}{*}{10} & \multirow{2}{*}{5 days} \\  & & & \\ \multirow{2}{*}{
\begin{tabular}{c} Davigline and NIR \\ bands \\ \end{tabular} } & \multirow{2}{*}{3} & \multirow{2}{*}{1 day} \\  & & & \\ \end{tabular}
\end{table} 
Table 1: Remote sensing platform overview: sensors, bands, spatial resolutions, and revisiting time. The data for each orchard were obtained using the geospatial data abstraction library (GDAL) in Python to calculate the mean value for each grower. Figure 3 illustrates the above-described procedure, by displaying an example of the NDVI computed for the three VI platforms, in March 2019. Figure 3a-c depicts the NDVI images, with the spatial resolution associated with each platform. They were then subjected to a mean calculation, yielding a singular value that was then used in the creation of the final dataset. These three VI datasets were produced to compare the performance of the Landsat 8, Sentinel-2, and PlanetScope data.