Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2739-2021
https://doi.org/10.5194/hess-25-2739-2021
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

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Cited articles

Ahmad, S., Kalra, A., and Stephen, H.: Estimating soil moisture using remote sensing data: A machine learning approach, Adv. Water Resour., 33, 69–80, https://doi.org/10.1016/j.advwatres.2009.10.008, 2010. 
Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M., and Notarnicola, C.: Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data, Remote Sens.-Basel, 7, 16398–16421, https://doi.org/10.3390/rs71215841, 2015. 
Ambroise, C. and McLachlan, G. J.: Selection bias in gene extraction on the basis of microarray gene-expression data, P. Natl. Acad. Sci. USA, 99, 6562–6566, https://doi.org/10.1073/pnas.102102699, 2002. 
Anderson, K. and Gaston, K. J.: Lightweight unmanned aerial vehicles will revolutionize spatial ecology, Front. Ecol. Environ., 11, 138–146, https://doi.org/10.1890/120150, 2013. 
Apley, D. W. and Zhu, J.: Visualizing the effects of predictor variables in black box supervised learning models, Roy. Stat. Soc. B, 82, 1059–1086, https://doi.org/10.1111/rssb.12377, 2020. 
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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.