Advances in soil moisture retrieval from multispectral  remote sensing using unoccupied aircraft systems  and machine learning techniques

Araya, Samuel N.; Fryjoff-Hung, Anna; Anderson, Andreas; Viers, Joshua H.; Ghezzehei, Teamrat A.

doi:https://doi.org/10.5194/hess-25-2739-2021

Articles | Volume 25, issue 5

https://doi.org/10.5194/hess-25-2739-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-2739-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 5

Research article

|

25 May 2021

Research article |

| 25 May 2021

Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques

Samuel N. Araya, Anna Fryjoff-Hung, Andreas Anderson, Joshua H. Viers, and Teamrat A. Ghezzehei

Supplement

https://doi.org/10.5194/hess-25-2739-2021-supplement

Data sets

saraya209/uas-soil-moisture: First release of uas-soil-moisture code and data S. N. Araya https://doi.org/10.5281/zenodo.4743238

Short summary

We took aerial photos of a grassland area using an unoccupied aerial vehicle and used the images to estimate soil moisture via machine learning. We were able to estimate soil moisture with high accuracy. Furthermore, by analyzing the machine learning models we developed, we learned how different factors drive the distribution of moisture across the landscape. Among the factors, rainfall, evapotranspiration, and topography were most important in controlling surface soil moisture distribution.