## 3 Methods  
ML algorithms can automatically learn the relationships in any given data between input (reflectances) and output (LAI). To identify the performance of five popular ML algorithms for cotton LAI retrieval, regression models were established based on artificial neural network (ANN), SVR, Gaussian process regression (GPR), random forest (RF) and gradient boosting regression tree (GBRT), at S2-20 m for the whole growth period of cotton (using all 117 quadrats data over the three observation dates).  
All the ML models were implemented using the Scikit-learn package [49], which is an open-source Python [50] module project that integrates a wide range of prevalent ML algorithms [51]. All hyperparameter tuning of the models is based on GridSearchCV in the Scikit-learn package, which can evaluate all possible combinations of hyperparameter values using five-fold cross-validation to determine the best combination of hyperparameter values (the hyperparameter combination that has the best accuracy of the model in terms of root-mean-square error (RMSE)). Cross-validation are model validation techniques to obtain reliable and stable models. This study implemented a five-fold cross-validation, basically, the training datasets are split into five smaller sets, and a model is trained using four of the folds as training data, then the resulting model is validated on the remaining part of the data, the processes continues to circulate five times, and finally, the performance measure estimated by five-fold cross-validation is the average of the values computed in the loop. Training and testing sampling distribution has a great impact on machine learning regression algorithms (MLRAs), and some previous studies demonstrated that 70%/30% split option is appropriate for model training and validation [52; 53; 54], nonetheless, other studies argue that the 80%/20% split option is preferable [27; 55]. In our study, to validate the performance of the ML models, all the datasets were randomly split into  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Products** & **Version** & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} & **Algorithms** & \begin{tabular}{c} **Temporal** \\ **Coverage** \\ \end{tabular} & **References** \\ \hline MODIS & MCD15 C6 & 500 m & 4-day & RTM 3D \({}^{1}\) (LUT) & 2002-present & \begin{tabular}{c} [PERSON], et al. \\ 2015 [46] \\ \end{tabular} \\ \hline GEOV3 & V1.0.1 & \(1/3\) km & 10-day & \
\begin{tabular}{c} Neural network \\ (red, NIR) \\ \end{tabular} & 2014-present &
\begin{tabular}{c} [PERSON], et al. 2013, \\ 2016 [47; 48] \\ \end{tabular} \\ \hline \hline \end{tabular} \({}^{1}\) RTM and LUT stands for “Radiative Transfer Model” and “Look Up Table”, respectively.  
\end{table}
Table 3: Main characteristics of two global LAI products under study.  
75% (n = 87) for model training, and 25% (n = 30) for model validation. Regression models that achieve satisfactory performance in training datasets may fail to predict unseen datasets, and therefore, a model that performs well at both training and unseen testing datasets is referred to as having excellent generalization ability. This type of model could be used for crop LAI retrieval applications.