Assessing spectrotransfer functions and pedotransfer functions in predicting soil water retentions

Soil hydraulic properties have important effects on describing water flow، solute and gass transports and also are important in hydrological studies. Although spectral information over visible near-infrared and shortwave infrared range، as a rapid، cost-effective and non-destructive method، has been recently applied to predict a number of soil properties، only few attempts have been conducted to predict soil hydraulic properties. The objective of this study was to assess whether inclusion of soil spectral data as a uniqe set of the predictors and alternative to basic soil properties would improve water retention predictions. Consequently، a number of 174 soil samples were taken and the spectral reflectances of the soils over 350 to 2500 nm range were measured، using a handheld spectroradiometer apparatus. The water retention at six matric potentials of -330، -1000، -3000، -5000، -10000 and -15000 cm were also measured by using preassure plate apparatus. Four scenarios including spectrotransfer functions (STFs)، pedotransfer functions (PTFs)، spectropedotransfer functions (SPTFs) and Rosetta PTFs were investigated. The transfer functions were first derived and compared with each other as well as with Rosetta PTFs afterwards. Based on the obtained results، basic soil properties and water retention parameters indicated high and significant (1% significancancy level) correlations with spectral reflectance values particularly in near and shortwave infrared ranges. The STFs indicated higher accuracy (R2>0. 60; RMSR<0. 011 cm3 cm-3) than the others especially at mid and dry end of retention curve. Although SPTFs and PTFs provided similar predictions، but PTFs were estimated narrowly better predictions at wet-end part of retention curve (-330 and -1000 cm). Weak predictions were obtained by Rosetta PTFs for all water contents particularly at the wet part of retention curve. These results suggest the efficacy of the spectral data، which can be used as an indirect method to predict soil water retention status.