**3. Concluding Remarks**

Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities for the next decades. As a matter of fact, Space agencies, on a worldwide basis, have ongoing programs to develop hyperspectral satellite missions to assure global coverage at high spatial resolution that will have a noteworthy impact on agricultural and natural vegetation monitoring studies. The eleven manuscripts collected in this special issue and, therefore, represent some meaningful progress in the application of hyperspectral EO data for agricultural and vegetation research themes. Further work in this area is required in view of the recent advances and funding opportunities in this field. We expect that the studies published herein will help the agriculture and vegetation research and management communities to better characterize and assess biophysical variables and processes, as well as more effectively predict plant nutrient using upcoming hyperspectral remote sensing technologies.

**Author Contributions:** All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The guest editors would like to thank the authors who contributed to this special issue and the reviewers who helped to improve the quality of the special issue by providing constructive recommendations to the authors. We would like to especially thank all 77 contributing authors who dedicated their research and time to this special issue. Likewise, we want to explicitly thank the fifty reviewers for their great work and effort put in this often-underestimated task. The careful, suitable, and original comments provided by the reviewers improved each of the papers published in this special issue. A special thanks goes to Quenby Qu.

**Conflicts of Interest:** The guest editors declare no conflict of interest.
