18 Aug 2020
18 Aug 2020
Advances in Soil Moisture Retrieval from Multispectral Remote Sensing Using Unmanned Aircraft Systems and Machine Learning Techniques
- 1Earth System Science, Stanford University, Stanford, CA, USA
- 2Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, CA, USA
- 3Department of Civil and Environmental Engineering, University of California, Merced, CA, USA
- 4Life and Environmental Science, University of California, Merced, CA, USA
- 1Earth System Science, Stanford University, Stanford, CA, USA
- 2Center for Information Technology in the Interest of Society and the Banatao Institute, University of California, Merced, CA, USA
- 3Department of Civil and Environmental Engineering, University of California, Merced, CA, USA
- 4Life and Environmental Science, University of California, Merced, CA, USA
Abstract. We developed machine learning models to retrieve surface soil moisture (0–4 cm) from high resolution multispectral imagery using terrain attributes and local climate covariates. Using a small unmanned aircraft system (UAS) equipped with a multispectral sensor we captured high resolution imagery in part to create a high-resolution digital elevation model (DEM) as well as quantify relative vegetation photosynthetic status. We tested four different machine learning algorithms. The boosted regression tree algorithm gave the best prediction with mean absolute error of 3.8 % volumetric water content. The most important variables for the prediction of soil moisture were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing data and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.
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Samuel N. Araya et al.


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RC1: 'Referee report: Advances in Soil Moisture Retrieval from Multispectral Remote Sensing Using Unmanned Aircraft Systems and Machine Learning Techniques', Anonymous Referee #1, 22 Sep 2020
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AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020
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AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020
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RC2: 'Review', Salvatore Manfreda, 13 Oct 2020
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AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020
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AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020


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RC1: 'Referee report: Advances in Soil Moisture Retrieval from Multispectral Remote Sensing Using Unmanned Aircraft Systems and Machine Learning Techniques', Anonymous Referee #1, 22 Sep 2020
-
AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020
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AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020
-
RC2: 'Review', Salvatore Manfreda, 13 Oct 2020
-
AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020
-
AC1: 'Author Response to Referee Comments', Samuel Negusse Araya, 13 Dec 2020
Samuel N. Araya et al.
Samuel N. Araya et al.
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