The Vertosols of the lower Namoi valley in New South Wales, Australia, have been extensively developed for irrigated cotton (Gossypium hirsutum L.) production. However, there are competing demands for water from mining, environmental needs, and agriculture, with climate change forecasts suggesting reduced rainfall and hotter climates. This means irrigators need to improve water efficiency, which requires methods to monitor the soil volumetric water content (θ, m³ m^–3). The aim of this study is to assess if a multicoil electromagnetic instrument (DUALEM-421), which measures soil apparent electrical conductivity (EC_a, mS m^–1), can be used to value add to limited measurements of θ and allow extrapolation along a uniformly heavy clay irrigation furrow. Specifically, we seek to invert EC_a using EM4Soil software to generate electromagnetic conductivity images, whereby the estimates of soil true electrical conductivity (σ, mS m^–1) in the topsoil, subsurface, and subsoil are correlated with θ measured by Decagon GS3 sensors installed at the same depths. We also compared the usefulness of inversion of different combinations of DUALEM EC_a: DUALEM-1, DUALEM-2, and DUALEM-21. We found estimated σ from inversion of DUALEM-421 EC_a had good agreement (i.e., Lin's concordance = .84) with measured soil bulk electrical conductivity (σ_b, mS m^–1). We explored the relationship between DUALEM-421 estimated σ and θ using an artificial neural network. The predictions showed the model had good coefficient of determination (R² = .74) with good accuracy (RMSE = 0.04 m³ m^–3) and demonstrated good agreement between measured and predicted θ (Lin's = .84).

中文翻译：

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使用电磁电导率成像的土壤水分延时成像：润湿阶段

澳大利亚新南威尔士州 Namoi 山谷下游的 Vertosols 已被广泛开发用于灌溉棉花 ( Gossypium hirsutum  L.) 的生产。然而，采矿、环境需求和农业对水的需求相互竞争，气候变化预测表明降雨量减少和气候变暖。这意味着灌溉者需要提高用水效率，这需要监测土壤体积含水量 (θ, m ³ m ^–3 ) 的方法。本研究的目的是评估多线圈电磁仪器 (DUALEM-421) 是否可以测量土壤表观电导率 (EC _a , mS m ^–1)，可用于对 θ 的有限测量进行增值，并允许沿均匀重的粘土灌溉沟进行外推。具体来说，我们寻求使用 EM4Soil 软件来反转 EC _a以生成电磁电导率图像，由此表土、地下和底土中土壤真实电导率 (σ, mS m ^–1 )的估计值与 Decagon GS3 传感器测量的 θ 相关安装在相同的深度。我们还比较了 DUALEM EC _a的不同组合反演的有用性：DUALEM-1、DUALEM-2 和 DUALEM-21。我们发现来自 DUALEM-421 EC _a反演的估计 σ与测量的土壤体积电导率 (σ _b , mS m^–1 )。我们使用人工神经网络探索了 DUALEM-421 估计的 σ 和 θ 之间的关系。预测表明该模型具有良好的决定系数 ( R ²  = .74) 和良好的准确度 (RMSE = 0.04 m ³ m ^–3 )，并证明测量的和预测的 θ 之间具有良好的一致性 (Lin's = .84)。