Rapid estimation of a soil-water retention curve using visible-near infrared spectroscopy

Estimation of a soil-water retention curve (SWRC) is essential for modeling the water flow and solute transport. A simple method to fit the measured retention data is the Campbell function. In this study, the Campbell function with an anchored gravimetric water content at -1500 kPa (W-1500) was used that included two parameters: the Campbell b and W-1500. An inexpensive methodology was proposed for predicting these Campbell parameters using visible-near infrared spectroscopy (vis-NIRS). Four calibration partial least squares regression models were developed. The first model built on 230 soil samples predicted Campbell b and W-1500 (vis-NIRS1). The second model used the same dataset for predicting Campbell b but included 1570 samples for predicting W-1500 (vis-NIRS2). The third model combined predicted Campbell b with measured W-1500 (vis-NIRS + measured W-1500), similar to approaches used in ROSETTA-1 software. The fourth model used the entire datasets without splitting them into calibration and validation (vis-NIRS3). The R-2 of the Campbell b and W-1500 ranged from 0 to 0.89 and 0.02-0.91, respectively. Results showed that predicted SWRCs were comparable with estimates from the ROSETTA-1 for two scenarios: using soil texture and bulk density as inputs (ROSETTA-1(sc2)) and using in addition water content at -33 and-1500 kPa (ROSETTA-1(sc4)). It was concluded that the vis-NIRS based models captured the shapes of the SWRC, and vis-NIRS2 and vis-NIRS + measured W-1500 could be used as an alternative to ROSETTA-1(sc2). However, vis-NIRS based models failed to predict the SWRC for the sandy soils, most likely due to a small sample size.