# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
## 3 Results
### Biomass Prediction Using Random Forest Model  
Biomass prediction using Planet imagery was evaluated for each time period (November, March, first half of April, and second half of April). The coefficient of determination (R\({}^{2}\)) was low when bands and VIs were used as predictors (Table 5). Across the time period evaluated in this study, R\({}^{2}\) was highest during March and lowest during the second half of April. The model predicted better when CC species/mix information was also added as a predictor. During March, including CC as the predictor increased R\({}^{2}\) from 0.25 to 0.61, and it decreased MSE from 2.72 to 1.51.  
The timing of hyperspectral data collection corresponded to the April_B time period for the Planet imagery data. Biomass predictability from Planet imagery during the April_B time period was very weak, and R2 improved from 0 to 0.36 when using CC species information in the model. FieldSpec showed better biomass predictability (R\({}^{2}\) 0.35 for bands and 0.46 for VIs). Therefore, the comparison between Planet imagery model output during the April_B time period and the hyperspectral model showed that biomass prediction was better with hyperspectral data compared to Planet imagery (Table 5), probably due to the higher number of bands included in the model and the greater amount of information in these bands. In addition, biomass prediction was better with VIs than bands (R\({}^{2}\) 0.35 vs. 0.46). Unlike Planet imagery, the addition of CC information had no impact on biomass prediction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Source** & **Time Period** & **Predictors** & **\# of Predictors** & **R\({}^{2}\)** & **MSE** \\ \hline Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Planet & & & & & \\ Planet & & & & & \\ Planetary & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ Planet & & & & & \\ Imagery & & & & & \\ \hline FieldSpec & April & & & & & \\ FieldSpec & April & & & & & \\ FieldSpec & April & & & & \\ \hline FieldSpec & April & & & & & \\ FieldSpec & April & & & & \\ FieldSpec & April & & & & \\ \hline \hline \end{tabular} Note: For Planet imagery, November, March, April_A (1–15 April) and April_B (1–15 April) represent average value for that time period, while FieldSpec data were a one-time measurement.  
\end{table}
Table 5: Cover crop biomass prediction using random forest model.  
The ten most important wave bands for predicting cover crop biomass ranged from 555 nm to 1695 nm (Figure 4A). Among the tested VIs (Figure 4B), the ten most important variables were photochemical reflectance index (PRI) \(\times\) carotenoid index (CI) [41], leaf water vegetation index (LWVI1) [42], disease-water stress index (DWSI-2) [43], derivative index (D1) [44], Vogelmann indices 2 and 4 [45], edge green first derivative ratio (EGFR) [46], shortwave infrared (SWIR SI) [47], plant water index (PWI) [48], and shortwave infrared (SWIR LI) [47]. Since the hyperspectral data were collected once, the authors would like to list only the important variables picked by the random forest model at this stage, rather than to draw further conclusions based on this dataset.