Interpolation and 3D visualization of soil moisture

András Hervai,Szabolcs Ákos Fábián,Szabolcs Czigány,Ervin Pirkhoffer,Ákos Halmai,Dénes Lóczy,Gábor Nagy

doi:10.21120/le/11/1/3

Abstract

Adaptation to climate change demands the optimal and sustainable water management in agriculture, with an inevitable focus on soil moisture conditions. In the current study we developed an ArcGIS 10.4. platform-based application (software) to model spatial and temporal changes in soil moisture in a soy field. Six SENTEK Drill & Drop soil moisture sensors were deployed in an experimental field of 4.3 hectares by the contribution of Elcom Ltd. Soil moisture measurement at each location were taken at six depths (5, 15, 25, 35, 45 and 55 cm) in 60-minute intervals. The model is capable to spatially interpolate monitored soil moisture using the technique. The time sequence change of soil moistures can be tracked by a Time Slider for both the 2D and 3D visualization. Soil moisture temporal changes can be visualized in either daily or hourly time intervals, and can be shown as a motion figure. Horizon average, maximum and minimum values of soil moisture data can be identified with the builtin tool of ArcGIS. Soil moisture spatial distribution can be obtained and plotted at any cross sections, whereas an alarm function has also been developed for tension values of 250, 1,000 and 1,500 kPa.

Highlights

Preparation for climate change demands optimal and sustainable water supplies for cost-efficient and sustainable agricultural productivity (Makó et al 2010), which requires the thorough understanding and sound knowledge on vadose zone soil moisture conditions
Soil moisture spatial distribution can be obtained and plotted at any cross sections, whereas an alarm function has been developed for tension values of 250, 1,000 and 1,500 kPa
In the current study we have developed a measurement-based simple numeric soil moisture calculation and 2D and 3D visualization model

Summary

Introduction

Preparation for climate change demands optimal and sustainable water supplies for cost-efficient and sustainable agricultural productivity (Makó et al 2010), which requires the thorough understanding and sound knowledge on vadose zone soil moisture conditions. Soil moisture is a highly variable environmental parameter, both spatially and temporally (Loew – Schlenz 2011), and knowledge on its spatial heterogeneity may provide useful information for precision farming and costefficient crop production (Paul – Speckmann 2004). Soil moisture spatial distribution varies both vertically and horizontally in a small scale largely due to topography (Anderson – Kneale 1980; Zhu – Lin 2011), soil texture, soil organic matter content and vegetation (Novák 2005; Novák et al 2013). Principal options to obtain sufficient knowledge on spatial distribution of soil moisture include high resolution ground monitoring (Zhu – Lin 2011; Bárdossy – Lehmann 1998) or satellite remote sensing applications (Jia et al 2013; Penna et al 2009; Mohanty – Skaggs 2001). A relatively few papers are available on in situ measurement with sensors, whereas a large number of studies tested remote sensing data on large-scale soil moisture products. Precision can be further reduced by background signal disturbances (spreading, attenuation, reflection and scattering) and flight density (Zhou et al 2016)

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