**1. Introduction**

We are living in a world where geophysical datasets, particularly, remote sensing datasets, are created at fast increasing rates [1]. The efficient and innovative use of these datasets for understanding hydrological processes in various climatic and vegetation regimes under anthropogenic influence has become an important challenge, which offers a wide range of research opportunities at the same time [2,3]. This is particularly urgen<sup>t</sup> for the hydrological research community at large who has relied on both spatially distributed and lumped hydrological models for hydrological simulations/predictions over the last several decades [4]. The demand for increasingly accurate water information spatially distributed at high resolution requires a deeper understanding of the underlying processes and more skillful predictions, at resolutions that do not commensurate with the traditional hydrological data. This is an important challenge to the conventional hydrological modelling.

To address these challenges, efforts need to be made to innovatively integrate various remote sensing techniques into hydrological simulations and predictions, which is more critical in a changing world. This Special Issue of Remote Sensing contributes towards this aim through investigations on how a better and smarter use of high-to-moderate-resolution remote sensing datasets can improve hydrological simulations and predictions. Ten peerreviewed papers are published in this Issue, which are grouped into four categories using remote sensing techniques:

**Citation:** Zhang, Y.; Ryu, D.; Zheng, D. Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World. *Remote Sens.* **2021**, *13*, 3865. https://doi.org/ 10.3390/rs13193865

Received: 22 September 2021 Accepted: 26 September 2021 Published: 27 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).


## **2. Contributed Papers**
