## 5 Discussion  
Over the last decade, there has been a considerable increase in the introduction of ML algorithms to remote sensing for a wide range of fields. Crop biophysical parameters (e.g., LAI) are key variables for a wide range of applications. With the progress of remote sensing techniques, we are able to acquire high dimensional (spatial, temporal, and spectral) remote sensing data, which demands more efficient, accurate and robust algorithms in a wide variety of applications with remote sensing. In this context, there is great potential for ML algorithms to be used in a wide range of remote sensing applications. The diversity of available ML algorithms poses great challenges for the selection of MLRAs, as well as the decision of optimal number of training sample size and spectral bands to different MLRAs. Besides, another significant problem that may arise involves hyperparameter tuning in the application of ML algorithms. In general, experience is required to obtain satisfactory results for ML algorithms, and experiments with a large number of datasets may be needed, which are generally not available. Accordingly, it is necessary to perform a comparison of different fashionable ML models to support better remote sensing applications. In this study, we focus on the comparison of five well-known ML algorithms for cotton LAI retrievals with Sentinel-2 imagery, because these algorithms have a wide range of applications in remote sensing. Additionally, the hyperparameter tuning results of five prominent ML models are provided. Our study could provide support for associated remote sensing studies based on ML algorithms. Furthermore, a comparatively great fluctuation is observed at 7 among all the models except for GPR in Figure 9, the previous removing spectral band is the red-edge band (B7), which indicates that having low dCor values do not necessarily correspond to being less important for LAI retrieval. Notably, ensemble methods (RF and GBRT) and GPR models have a great benefit of delivering feature importance information (not shown in our study), which provides insight into the greatest contributing features of the model. Such information could be used for better model interpretation, and this information is also useful for feature selection processes when applied to models that contain a great number of features. In related studies, [PERSON], et al. [55] compared four ML algorithms (NN, SVR, KRR and GPR) using simulated Sentinel-2 and 3 data to assess three biophysical parameters (Chlorophyll content, LAI and FVC). GPR outperformed the other regression methods for the majority of Sentinel configurations, whereas in our study, GPR performed worse than the other regression methods. Results that differ from our study may be for the following reasons. First, we used real Sentinel-2 data rather than simulation data, and it may be difficult to represent the performance of real Sentinel-2 data with simulation data. Second, we focus on cotton crops over the entire growth period rather than on various crop types. In addition, there may be different hyperparameter settings between models. Finally, there are differences between the Sentinel-2 bands, and we used 10 spectral bands (SWIR included). [PERSON], et al. [83] compared the RF, ANN and SVM regression models for soybean LAI retrieval using unmanned aerial vehicle (UAV) hyperspectral data with different sampling methods. The results showed that RF is suitable for the whole growth period of soybean LAI estimation, while ANN is appropriate for a single growth period. [PERSON] and [PERSON] [84] compared SVR and RFR for wheat LAI estimation using hyperspectral data, and the results showed that SVR provided the best performance of the entire dataset. Different from previous studies, we considered the GBRT model due to its highly robust performance over a wide range of applications. We further explored the sensitivity of different ML algorithms to training sample size and Sentinel-2 spectral bands.