## 6 Conclusions  
With the challenges of selecting ML algorithms for crop LAI estimation, the results of this study have great implications for the selection of appropriate ML models from the diversity of available ML algorithms, and at the same time, these same results provide the optimal number of training sample size and spectral bands of Sentinel-2 for each model required for cotton LAI retrieval. Regarding the comparison of different ML models for cotton LAI retrieval employed in our study, the GBRT model outperforms the other ML models according to our results. Our findings increase the potential for cotton LAI retrieval with Sentinel-2 imagery, and may be transferrable to other associated problems related to agricultural remote sensing applications. On the other hand, the GBRT model is computationally demanding, which may be a significant problem with a large scale of data. However, GBRT can challenge the model accuracy and computational efficiency selection problems. Considering the computational efficiency, SVR exhibits considerable computational superiority over the other ML models.  
Regarding the sample size sensitivity of ML models, model accuracy increases with the growth of the training sample size, and the GBRT produces the most robust performance for the training samples with respect to the standard error and the best model accuracy on average.  
In terms of spectral band sensitivity analysis, the distance correlation results showed that the SWIR and visible bands have great potential to improve the accuracy of cotton LAI retrievals. By using dCor combined with the backward elimination method, the model accuracy decreases with the reduction in spectral bands. SVR, GPR and RF perform robustly with the spectral bands, and ANN provides the best accuracy on average. The minimum number of bands required for cotton LAI retrieval are recognized as 6 (ANN), 6 (SVR), 5 (RF), 5 (GBRT) and 8 for GPR.  
A comprehensive evaluation has been employed to identify the performance of five ML models, considering a combination of model accuracy, computational efficiency, sensitivity to training sample size and sensitivity to spectral bands. The comprehensive performance of the models is identified as GBRT \(>\) ANN \(>\) SVR \(>\) RF \(>\) GPR.  
Despite the different performances of the five ML regression models, MLRAs are promising ways to retrieve cotton LAI with Sentinel-2 imagery because the MLRAs all achieved encouraging accuracies. With profound applications for a diversity of ML algorithms in remote sensing, MLRAs may provide positive effects for remote sensing applications in terms of classification, regression, and other associated problems.  
Conceptualization, J.M. and H.M.; Methodology, H.M.; Formal Analysis, H.M.; Investigation, H.M., F.J., Q.Z. and H.F.; Data curation, H.M.; Writing--Original Draft Preparation, H.M.; Writing--Review and Editing, J.M.; Visualization, H.M.  
This research was funded by GF6 Project under grant No. 30-Y20A03-9003-17/18 and 09-Y20A05-9001-17/18; the National Natural Science Foundation of China (41871261); and the open fund of the Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group (201701).  
We are grateful to Agricultural College of Shiezi University for providing help in field campaigns. We would like to acknowledge the authors who contributed to the development of Scikit-learn project. We acknowledge the Copernicus Global Land Service, and the Land Processes Distributed Active Archive Center (LP DAAC) within the NASA Earth Observing System Data and Information System (EOSDIS) for providing the global LAI products. We are thankful to the three anonymous reviewers for their insightful comments and suggestions helped to clarify and improve the manuscript.  
The authors declare no conflict of interest.