A comparative study of multiple approaches for predicting the soil-water retention curve: Hyperspectral information vs. basic soil properties

Abstract

Information about the soil-water retention curve is necessary for modeling water flow and solute transport processes in soils. Soil spectroscopy in the visible, nearinfrared, and shortwave infrared (Vis-NIR-SWIR) range has been widely used as a rapid, cost-effective and nondestructive technique to predict soil properties. However, less attention has been paid to predict soil hydraulic properties using soil spectral data. In this paper, spectral reflectances of soil samples from the Zanjanrood watershed, Iran, were measured in the Vis-NIR-SWIR ranges (350-2500 nm). Stepwise multiple linear regression coupled with the bootstrap method was used to construct predictive models and to estimate the soil-water retention curve. We developed point and parametric transfer functions based on the van Genuchten (VG) and Brooks-Corey (BC) soil hydraulic models. Three different types of transfer functions were developed: (i) spectral transfer functions (STFs) that relate VG/BC hydraulic parameters to spectral reflectance values, (ii) pedotransfer function (PTFs) that use basic soil data as input, and (iii) PTFs that consider spectral data and basic soil properties, further referred to as spectral pedotransfer functions (SPTFs). We also derived and evaluated point transfer functions which estimate soil-water contents at specific matric potentials. The point STFs and SPTFs were found to be accurate at low and intermediate water contents (R2 > 0.50 and root mean squared error [RMSE] <0.018 cm3 cm-3), while the point PTFs performed better close to saturation. The parametric STFs and SPTFs of both the VG and BC models performed similarly to parametric PTFs in estimating the retention curve. The best predictions of soil-water contents were obtained for all the three transfer functions when the VG and BC retention models were fitted to the retention points estimated by the point transfer functions. Overall, our findings indicate that spectral data can provide useful information to predict soil-water contents and the soil-water retention curve. However, there is a need to extend and validate the derived transfer functions to other soils and regions.