# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
## 2 Materials and Methods
### Statistical Analysis and Random Forest Model Building  
All four study sites were combined to explore the correlation ([PERSON]'s correlation coefficient, r) of seasonal (or time period November, March, April_A, April_B) VI and reflectance bands with cover crop biomass and seeding rate. Based on the strength of correlation observed in our study and importance shown by other literature, red, NIR, NDVI, EVI and OSAVI [22; 27; 28; 31; 32] were further analyzed using analysis of variance (ANOVA) to explore the CC treatment effect on band/indices value and biomass yield. Each experiment was analyzed separately for ANOVA, and the least significant difference (LSD) for CC treatment was identified at \(p\leq 0.05\). R software was used to run the ANOVA  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**VIs** & **Expression \#** & **Source** \\ \hline Normalized difference vegetation index (NDVI) & \(\frac{\text{NIR}-\text{R}}{\text{NIR}+\text{R}}\) & [25] \\ Enhanced vegetation index (EVI) & \(2.5*\left(\frac{\text{NIR}-\text{R}}{\text{NIR}+\text{68}-\text{75}+\text{ B}+\text{T}}\right)\) & [26] \\ Two-band enhanced vegetation index (EVI2) & \(2.4*\left(\frac{\text{NIR}-\text{R}}{\text{NIR}+\text{24}+\text{R}+1}\right)\) & [27; 28] \\ Optimized soil adjusted vegetation index (OSAVI) & \((1+0.16)*\frac{\text{NIR}-\text{K}}{\text{NIR}+\text{IR}+\text{T}+0.16}\) & [29; 30] \\ Simple ratio (SR) & \(\frac{\text{NIR}}{\text{NIR}+\text{1}}\) & [31] \\ Modified simple ratio (MSR) & \(\frac{\text{NIR}}{\text{NIR}+\text{1}}\) & [32] \\ Excess green index (ExG) & \(2*\text{G}-\text{R}-\text{B}\) & [33; 34] \\ Excess red index (ExR) & \(1.4*\text{R}-\text{G}\) & [33; 35] \\ \hline \hline \end{tabular} \# Note: Coefficients of EVI: gain factor G is 2.5, soil and canopy background adjustment factor L is 1, aerosol scattering factor CI is 6 and C2 is 7.5. Coefficients of EVI2: gain factor G is 2.4, soil and canopy background adjustment factor L is 1, aerosol scattering factor CI is 1.4. Coefficients of OSAVI: canopy background adjustment factor L is 0.16.  
\end{table}
Table 2: Vegetation indices (VIs) used for cover crop biomass estimation. The indices were derived using the blue (B), green (G), red (R), and near-infrared (NIR) bands of Planet imagery.  
model, where CC, year, and their interaction were included to extract appropriate mean square error (MSE) to test LSD among CC treatments.  
The random forest models were developed using the R package \"randomForest' [39] to predict biomass with planet imagery and hyperspectral data. For Planet imagery, 12 predictors (four reflectance bands and eight VIs) were used to develop a random forest model for each time period (November, March, first half of April and second half of April). In the next step, CC species information was added to the 12 variables to predict biomass. For hyperspectral data, data were only available for one time point (during biomass sampling at the end of April), and models were developed using all bands (164) first, then using all derived VIs (115) to predict biomass. The cover crop species information was added to the bands and VIs to predict biomass in the second step. Data were split into 70% training and 30% testing during each model run. Five-fold cross-validation and grid search criteria were used to fine-tune the model parameter mtry (randomly selected predictor during each split) using the R package \"caret\" [40]. The model parameter ntree (i.e., the number of trees to grow) was set to 150. The final model was used to test on the remaining 30% dataset and test statistics R\({}^{2}\) and mean square error (MSE) were calculated.