Based on global-scale data, this study considered the use of integrated machine learning models to investigate the estimation of daily soil moisture (SSM). The results of the study demonstrate that the integrated machine learning algorithm outperforms both the optimization and base machine learning algorithms in predicting SSM. The topic of SSM estimation is of significant importance. I have the following suggestions and questions to further enhance the current manuscript:

1. I would suggest including a literature review on data selection in the introduction, while reserving the Data section solely for describing the data used and the data preprocessing.
2. This study investigates the utilization of integrated machine learning models. However, it raises the question of whether three base models in the integrated model are optimal. Have the authors considered the possibility of adjusting the number of models, such as exploring whether a model integrated with two machine learning models may yield better performance?
3. Page 3, Line 80: “(ii)justify … model”. I would suggest adding experimental results or analyses in this area, such as manipulating the effect of a predictor on the results by increasing or decreasing its influence.
4. Page 12, Line 255-271. The authors chose eight machine learning models based on their popularity and performance, but it appears that there is a significant amount of research applying artificial neural networks (ANN) or using them as a baseline (Uthayakumar et al., 2022; Senyurek et al., 2020; Liu et al., 2020...). Surprisingly, the authors did not include ANN in their selection.
5. The manuscript is too long and needs to be reduced, and the authors of the figures and tables need to be revised and optimized.