**4. Conclusions**

The world is quickly changing with land surface conditions changing dramatically due to anthropogenic impacts over the last two decades. Correspondingly, geophysical datasets, particularly, remote sensing datasets, are created at fast increasing rates. It is challenging to efficiently and innovatively use these datasets for understanding hydrological processes in various climatic and vegetation regimes under anthropogenic impacts, which also offer a wide range of research opportunities. To address these challenges, efforts need to be undertaken to use various remote sensing techniques to improve hydrological simulations and predictions in a changing world. Ten peer-reviewed papers were published in this Special Issue, and can be summarized into the following four categories:


The ten papers presented in this Special Issue reflect the efforts for improving hydrological simulations and predictions using various remote sensing techniques. The papers published in this issue advance the remote sensing of hydrology by applying a new measurement approach, such as UAV or model-data fusions. Though the published ten papers in this Special Issue only cover parts of the summarized categories, we believe that continuous efforts in using remote sensing techniques in hydrology definitely promote hydrology. Furthermore, the authors more broadly discuss how to smartly use the state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions to tackle a quickly changing world.

**Author Contributions:** Y.Z. conceived and led the development of the Special Issue and this paper; D.R. and D.Z. each contributed to the writing of this paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study is supported by the CAS Pioneer Talent Program and the National Natural Science Foundation of China (grant no. 41971032).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** As gues<sup>t</sup> editors of this Special Issue, we thank the journal editors and all the authors submitting manuscripts to this Special Issue. Our thanks are extended to the referees who put grea<sup>t</sup> efforts in reviewing the submissions, which is the cornerstone for the high-quality publications of Remote Sensing.

**Conflicts of Interest:** The authors declare no conflict of interest.
