## 4 Results
### Sounding Model Validation  
Classification and capacity factor regression results for the period 2015-2022 are displayed in Tables 3 and 4, respectively. A naive baseline mAP is 0.5 for a model that predicts the most prevalent class, or always \"on\". This serves as a simple benchmark against which to compare our models' performance. For the regression model, a naive baseline that predicts a constant value of the training set's mean capacity factor would produce an RMSE of 0.34 and a mean bias error (MBE) of 0 across all imagery sources for both NDT and FGD. Models were trained and cross-validated on 99 plants with FGD (25 in Europe's ENTSO-E and 74 in the US) and 74 plants with NDT (45 in Europe's ENTSO-E, 22 in the US, and 7 in Australia). All of our models outperformed their respective baselines--and by an especially wide margin on the simpler task of classification. NDT models generally did better than FGD because NDT tends to produce larger plumes. Comparing the different model types drew out a few more insights. The BigEarthNet models require 10 bands and so can only be used for Sentinel-2 and Landsat 8. In general, RESISC models performed slightly better than BigEarthNet. Overall, RESISC and XGBoost fixed similarly, with RESISC sometimes outperforming XGBoost and sometimes vice versa, emphasizing the utility of an ensemble approach. Both PlanetScope and Sentinel-2 models performed similarly; the on/off classification mAP for both varied from 0.93 to 0.96 for NDT and from 0.84 to 0.90 for FGD across all models, and the capacity factor regression RMSE for both ranged from 0.20 to 0.22 for NDT and 0.26 to 0.30 for FGD. The Landsat 8 models generally were less accurate, with on/off classification mAP values of 0.81 to 0.90, and capacity factor regression RMSE values between 0.24 and 0.30. The Landsat 8 models' lower performance is most likely due to the coarser 30 m spatial resolution and revisit rate of 16 days, which results in fewer training images. Additionally, the capacity factor regression RMSE values for Sentinel-2 and PlanetScope overlap, which suggests that Sentinel-2's 10 m resolution is sufficient to identify on/off classification and the capacity factor. Even though PlanetScope's 3 m resolution did not substantially improve the model's performance, the daily revisits improve the temporal resolution, allowing for the more \begin{table}
\begin{tabular}{l l c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**NDT**} & \multicolumn{2}{c}{**FGD**} \\  & & **RMSE [CI]** & **MBE [CI]** & **RMSE [CI]** \\ \hline PlanetScope & XGBoost & 0.209 [0.194, 0.221] & \(-0.021\)\([-0.034,\,-0.006]\) & 0.297 [0.285, 0.308] & \(-0.056\)\([-0.076,\,-0.037]\) \\ PlanetScope & CNN-RESISC & 0.196 [0.186, 0.206] & \(-0.024\)\([-0.037,\,-0.010]\) & 0.263 [0.244, 0.277] & \(-0.065\)\([-0.084,\,-0.045]\) \\ Sentinel-2 & XGBoost & 0.210 [0.198, 0.220] & \(-0.020\)\([-0.034,\,-0.006]\) & 0.270 [0.258, 0.281] & \(-0.029\)\([-0.049,\,-0.011]\) \\ Sentinel-2 & CNN-RESISC & 0.203 [0.192, 0.213] & \(-0.019\)\([-0.034,\,-0.004]\) & 0.267 [0.248, 0.282] & \(-0.048\)\([-0.066,\,-0.029]\) \\ Sentinel-2 & CNN-BigEarthNet & 0.220 [0.206, 0.233] & \(-0.025\)\([-0.041,\,-0.008]\) & 0.259 [0.246, 0.269] & \(0.005\)\([-0.014,\,0.025]\) \\ Landsat 8 & XGBoost & 0.243 [0.220, 0.260] & \(-0.011\)\([-0.032,\,\,0.013]\) & 0.285 [0.271, 0.298] & \(-0.063\)\([-0.085,\,-0.042]\) \\ Landsat 8 & CNN-RESISC & 0.266 [0.249, 0.280] & \(-0.089\)\([-0.110,\,-0.068]\) & 0.299 [0.289, 0.309] & \(-0.036\)\([-0.054,\,-0.015]\) \\ Landsat 8 & CNN-BigEarthNet & 0.264 [0.247, 0.278] & \(-0.031\)\([-0.054,\,-0.006]\) & 0.301 [0.288, 0.313] & 0.025 [0.002, 0.050] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Capacity factor regression cross-validated root mean squared error (RMSE) and mean bias error (MBE), alongside respective block-bootstrapped 90% confidence intervals, for 2015–2022.