# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 4 Discussion  
This study presents a first approach to explore the multi-temporal satellite-based method to determine sugarcane yield at the field level. It was possible to use 54 orbital images that were merged according to their phenological stage, as shown in Table 3 generating nine new variables. These variables had values of five spectral bands and four VIs with a spatial resolution of 10 m \(\times\) 10 m assigned to compose the data used to infer the potential of MLR and RF regression methods to estimate sugarcane yield. In addition, the type of input predictors for both models were evaluated to fit the predictive models using high-resolution data. The study demonstrates the potential of using spectral bands as an alternative approach to the common crop yield forecast based on VIs [23; 54; 55; 56]. The spatial variability of the fields was mapped for the range of yield data values across all fields over the sugarcane growing seasons. The association of high-resolution imagery data and ML technique showed satisfactory results for estimating and mapping the sugarcane  
Figure 5: Observed sugarcane yield of 2019/2020 season (**A**). Predictive yield models based on Random Forest (RF) using NDRE (Normalized Difference Red Edge) (**B**), spectral bands (**C**), based on Multiple Linear Regression (MLR) using: NDRE (Normalized Difference Red Edge) (**D**), and spectral bands (**E**). yield, which can be applied for supporting PA practices and, also, to provide a better understanding of the spatial variability within-field over the season. The time-series analysis of orbital images enabled the development of a methodology based on the ML technique to advance on understanding the spatio-temporal variability of sugarcane fields. It helps to improve the decision-making process, as imagery data can provide relevant information regarding yield potential over the season. Previously, [PERSON] et al. [56] developed a time-series analysis for VIs to forecast sugarcane yield based on empirical models with RMSE values ranging from 7.0 to 17.0 Mg ha\({}^{-1}\), but such an analysis was conducted at the district level. In this study, the fact that individual correlations between predicting and target variables were low, combined with the clear autocorrelation in predicting variables, supports the use of a multivariate input (time-series) through a robust ML algorithm (the RF). It was expected that linear approaches (MLR) that predict yield based on VIs would present worse accuracy performance than non-linear models (e.g., RF regression). [PERSON] et al. [57] applied MLR with spectral bands and simple linear regression with NDVI to predict sugarcane yield. They found that MLR did not improve accuracy when compared to the simple linear regression. Thus, they proceeded using the simple linear regression and found R\({}^{2}\) values ranging from 0.22 to 0.41. Comparing their result with this study, the MLR presented lower R\({}^{2}\) than those found in [PERSON] et al. [57], even using a greater number of predictor variables (22 instead of 8). Despite the higher R\({}^{2}\) found in the MLR model, the RF regression is the model that outperformed the linear approach, highlighting its suitability to be used to support yield prediction models based on orbital images. Comparing NDVI, GNDVI, NDRE, and WDRVI, they all presented similar accuracy results for estimating the sugarcane yield. These results, except for NDRE, corroborate the results in [PERSON] et al. [57] that reported no significant difference among the use of GNDVI, NDVI, and WDRVI for predicting sugarcane crop growth and yield. However, their approach based on linear regression models was different from the one in this study that tested both linear and non-linear models, as it was noted that the non-linear (RF regression) model performed better (higher R\({}^{2}\), lower RMSE, and MAE), independent of the type of predictor variables.