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Estimating soil water and salt contents from field measurements with time domain reflectometry using machine learning algorithms

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  • Wan, Heyang
  • Qi, Hongwei
  • Shang, Songhao

Abstract

Soil water and salt contents are key soil physical parameters that play a crucial role in soil-related hydrological, ecological, environmental, and agricultural processes. Time domain reflectometry (TDR) is commonly used to measure in-situ soil water and salt contents, and provide possible solutions to quickly obtain soil bulk density (BD). However, the measurement accuracy is greatly influenced by the interaction of soil water and salt contents on the measured soil dielectric constant and electrical conductivity, especially for salinized soils. To accurately estimate the soil gravimetric (GWC) and volumetric (VWC) water contents, soil salt content (TS), and BD based on the TDR measurements, we designed different model input schemes to quantify the effect of different soil factors, and applied eight machine learning algorithms to map the non-linear relationship between model inputs and each target soil property. Results of a case study in Hetao Irrigation District in Northwest China indicated that soil particle-size fractions (psfs) are important inputs to predict all the above soil properties. Furthermore, BD mainly contributes to the prediction of soil GWC, and soil surface temperature (T) is effective in improving the GWC and TS estimations. Among eight machine learning algorithms used, extreme gradient boosting (XGB) and gradient boosting regression tree (GBRT) showed good robustness and strong learning capacity. It is recommended to apply XGB to precisely estimate GWC and BD, which resulted in the coefficients of determination (R2) of 0.80 and 0.69, respectively. On the other hand, GBRT precisely estimated the VWC and TS with R2 of 0.71 and 0.84, respectively. The evaluation of spatial distribution characteristic indicated that it is reliable to obtain the spatial distributions of the above soil properties from the TDR measurements based on the recommended model input schemes and machine learning algorithms.

Suggested Citation

  • Wan, Heyang & Qi, Hongwei & Shang, Songhao, 2023. "Estimating soil water and salt contents from field measurements with time domain reflectometry using machine learning algorithms," Agricultural Water Management, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:agiwat:v:285:y:2023:i:c:s0378377423002299
    DOI: 10.1016/j.agwat.2023.108364
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    References listed on IDEAS

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    1. Feng, Zhao-Zhong & Wang, Xiao-Ke & Feng, Zong-Wei, 2005. "Soil N and salinity leaching after the autumn irrigation and its impact on groundwater in Hetao Irrigation District, China," Agricultural Water Management, Elsevier, vol. 71(2), pages 131-143, February.
    2. Oates, M.J. & Fernández-López, A. & Ferrández-Villena, M. & Ruiz-Canales, A., 2017. "Temperature compensation in a low cost frequency domain (capacitance based) soil moisture sensor," Agricultural Water Management, Elsevier, vol. 183(C), pages 86-93.
    3. Taweh Beysolow II, 2017. "Introduction to Deep Learning Using R," Springer Books, Springer, number 978-1-4842-2734-3, December.
    4. Chen Jun & Yifang Ban & Songnian Li, 2014. "Open access to Earth land-cover map," Nature, Nature, vol. 514(7523), pages 434-434, October.
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