Gaussian process based spatial modeling of soil moisture for dense soil moisture sensing network
| Autor: | Surya S. Durbha, Saurabh Suradhaniwar, Avinash Lokhande, Prakash Andugula |
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| Rok vydání: | 2017 |
| Předmět: |
0208 environmental biotechnology
Soil science 02 engineering and technology 020801 environmental engineering Water resources symbols.namesake Matérn covariance function Pedotransfer function Kriging Ground-penetrating radar symbols Environmental science Water content Wireless sensor network Gaussian process |
| Zdroj: | Agro-Geoinformatics |
| Popis: | Agricultural practices by Wireless sensor networks (WSN) together with precision irrigation systems facilitate efficient use of water resources to maintain soil water balance and crop water requirement. In situ soil moisture measurements are expensive, point-based and cannot be scaled spatially over a field. In this work, to provide reasonable soil moisture maps across the site, Gaussian process regression (GPR) is used. Furthermore, soil moisture semivariograms are modeled by GPR using Matern covariance function to generate interpolated surfaces of soil moisture. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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