# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
## 4 Discussion
Periodic cover crop biomass sampling and its relationship with the VIs will be explored in future work to make a more conclusive argument about the timing of imagery that best correlates with bands and VIs. Our third hypothesis investigated by testing whether mixed cover crop species affect the remote sensing-based biomass estimation using machine learning approach. By applying the random forest machine learning approach, cover crop biomass was estimated. Cover crop biomass estimation using Planet imagery bands and VIs was not satisfactory, as the highest R\({}^{2}\) value observed was 0.25 using March imagery (Table 5). This was probably due to the mixed species used in our study. Several authors have reported higher cover crop biomass predictability when using remote sensing indices in single-species studies [18; 51; 52]. Higher R\({}^{2}\) values ranging from 0.53 to 0.76 for mixed cover crop species was reported by [53] using Planet imagery. They used several large fields to predict cover crop biomass compared to small plots in the current study, where few pixels were inside each plot. One obvious pattern we observed was the improvement of biomass predictability with the random forest model when CC species information was provided to the model (Table 5). This implies that biomass prediction can be improved if cover crop species are classified beforehand and used as model predictors. Different classification algorithms are developing over time with the availability of higher-resolution imagery and computing power [54; 55]. These algorithms can be utilized in automated classification of cover crop species before using a machine learning approach for biomass estimation. Additionally, model predictability improves if more ancillary information is provided. Information such as estimated souring date from Sentinel imagery helped to explain 55% of the variability in winter cover crop NDVI values [19]. Based on the Planet dataset, our third hypothesis was rejected, and biomass estimation was affected by mixed cover crop species. A previous study using Landsat-8, Sentinel-2 and PlanetScope sensors reported that VIs from PlanetScope sensors were most accurate (lower root mean square error and higher R\({}^{2}\)) for mixed cover crop biomass estimation [53] when individual sampling sites within the fields were considered. We extended our work with the hyperspectral dataset to test the third hypothesis further. Biomass estimation using hyperspectral bands and indices was better compared to Planet imagery during April (Table 5), but prediction did not improve with the addition of CC species information to the set of predictors. This is likely due to the range of wavebands,and VIs used on the model being enough to capture the total variability of the dataset. This result shows that our third hypothesis was accepted based on the hyperspectral data. However, more hyperspectral studies with measurements throughout the growing season are needed to confirm this result. The hyperspectral model could improve if reflectance data throughout the growing season are captured and utilized in the model-building process, which is a future work target aimed for in this project. The most important bands and VIs from the hyperspectral models were further explored (Figure 4). The bands selected by random forest models from hyperspectral data showed important bands ranging from 555 nm to 1695 nm. Vegetation indices selected by the hyperspectral model utilized bands ranging from 500 nm to 2210 nm. For example, vegetation index EGFR utilizes the derivative of different wavebands from 500 to 750 nm, while SWIR LI and SWIR SI utilize a differential ratio of wavebands 2210 nm and 2090 nm. Wavebands and VIs may be different if the scanning is conducted during the month of March, which is the most highly correlated time period based on Planet imagery.