Nonlinear models for soil moisture sensor calibration in tropical mountainous soils

ABSTRACT Electromagnetic sensors are widely used to monitor soil water content (θ); however, site-specific calibrations are necessary for accurate measurements. This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations. Undisturbed soil samples were collected in the A and B horizons of two Ultisols and two Inceptisols from the Mantiqueira Range in Southeastern Brazil. After saturation, the Theta Probe ML2X was used to obtain the soil dielectric constant (ε). Several readings were made, ranging from saturation to oven-dry. After each reading, the samples were weighted to calculate θ (m3 m–3). Fourteen regression models (linear, linearized, and nonlinear) were adjusted to the calibration data and checked for their residue distribution. Only the exponential model with three parameters met the regression assumptions regarding residue distribution. The stepwise regression was used to obtain multiple linear equations to estimate the adjusted parameters of the calibration model from soil attributes, with silt and clay contents providing the best relations. Both the specific and the general calibrations performed well, with RMSE values of 0.02 and 0.03 m3 m–3, respectively. Manufacturer calibration and equations from the literature were much less accurate, reinforcing the need to develop specific calibrations.

摘要
电磁传感器（electromagnetic sensors）被广泛用于监测土壤体积含水量（soil water content, θ），但为获取精准测量结果，需开展点位特异性校准。本研究对比了用于土壤湿度传感器校准的各类回归模型，并探究了土壤属性与专属校准方程校正参数之间的关联。研究从巴西东南部曼蒂凯拉山脉的2个老成土（Ultisols）剖面与2个始成土（Inceptisols）剖面的A层和B层采集原状土样。经饱和处理后，采用Theta Probe ML2X测定土壤介电常数（soil dielectric constant, ε）；测定梯度覆盖饱和状态至烘干状态，共完成多组读数测定。每组读数完成后，对土样进行称重以计算体积含水量θ（单位：立方米每立方米）。针对校准数据拟合了14种回归模型（包括线性模型、线性化模型与非线性模型），并对其残差分布开展检验。结果显示，仅三参数指数模型符合残差分布相关的回归假设条件。本研究采用逐步回归法构建多元线性方程，以基于土壤属性估算校准模型的校正参数，其中粉粒与黏粒含量可实现最优关联效果。专属校准与通用校准均表现优异，对应的均方根误差（RMSE，Root Mean Square Error）分别为0.02与0.03立方米每立方米。厂商提供的校准方案与已有文献中的校准方程精度均较低，这进一步凸显了开展点位专属校准的必要性。