## 4 Discussion
### ML Algorithm in Yield Assessment  
The S2 bands 2, 3, 4, and 8A were used for the Scenario a RF algorithm; Band 2 and Band 8A performed well compared to Bands 4 and 8A. As depicted in Figure 7, however, the performance of the RF model executed using the vegetation indices, NDVI, alone as a parameter was not significant, whereas other VIs performed well at 45, 60, and 75 DAP. The combination of SPAD and RDVI (Model e) had a different impact on both the 60 and 75 DAP in RF algorithms. In most of the models, Band 3 was found to be more predictive for the crop age 60-90 DAP, and the GNDVI and SIPI for the crop age 75 and 90 DAP, respectively. The RDVI performed well for the crop age 60-75 DAP and was found to be more predictable for carrot yield (Figure 8). The generated RF models were appropriate in the yearly prediction of carrot yield with an optimal window of 60 to 75 DAP. Moreover, with the inclusion of biophysical parameter SPAD values, similar spatial patterns are observed in both the d and f models, coinciding in terms of spatial variability in the experimental fields. The GLI index covers both blue and red bands to derive the overall photosynthetic activity of the plant regardless of the pigment; the green band may also be a good proxy to measure the chlorophyll a concentration [59], and the SIPI index to assess water stress in carrots [60] can be achieved at R\({}^{2}\) of 0.79 along with SPAD values [61, 62]. The model performance statistics are discussed as shown in Table 6. Of the studied five scenarios (Models a-e), an algorithm with the maximum predictors (i.e., Scenario e), including individual S2 bands + VIs + SPAD values, performed well (R\({}^{2}\) = 0.82; RMSE = 7.8 t h\({}^{-1}\)) compared to other scenarios (a-d). The second-best model with an  
Figure 8: Standards coefficients for the generation of carrot yield maps generated using a random forest (RF) algorithm. input of VIs and SPAD values (Scenario d) has resulted in a moderate correlation (R\({}^{2}\) = 0.67; RMSE = 10.2 t ha\({}^{-1}\)). A poor performance (R\({}^{2}\) = 0.46; RMSE = 26.2 t ha\({}^{-1}\)) was noted with Scenario a, i.e., only individual S2 bands as inputs for the RF model. It may be due to the impact of the feature selection process to reduce collinearity among predictors; it was also noticed that the RF model (d) was slightly improved compared to the other RF models (a-c) after the removal of non-correlated variables. Furthermore, Model e confirmed that the incorporation of LCC in ML modeling improves the model performance and reduces the bias from 26.2 t ha\({}^{-1}\) to 7.8 t ha\({}^{-1}\) (Table 6). It is attributed with the correlation between the temporal profile of the in situ measured chlorophyll (SPAD values), and the harvested carrot yield was reported to be moderate at 60 DAP (R\({}^{2}\) = 0.46; \(p\) > 0.0001) and 90 DAP (R\({}^{2}\) = 0.52; \(p\) > 0.0001). As depicted in Figure 9, a crop age of 75 DAP (R\({}^{2}\) = 0.78; \(p\) < 0.0001) was found to be suitable for the early prediction of carrot yield using the SPAD values. The correlation between the temporal profile of vegetation indices and the harvested carrot yield (Y\({}_{\text{A}}\)) varied across the VIs. As depicted in Figure 9, carrot yield was positively correlated and showed a moderate (R\({}^{2}\) = 0.59; \(p\) < 0.001) to high significance (R\({}^{2}\) = 0.74; \(p\) < 0.001) for GNDVI and RDVI, at the DAP of 60 to 90, respectively. On the other hand, SIPI also performed satisfactorily (R\({}^{2}\) = 0.51; \(p\) > 0.001), while the correlation was moderately low with NDVI and GLI. When the simulated LCC layers were incorporated to the ML model, the performance of the model was superior compared to the models those did not include LCC layers for the early prediction of carrot yield.