Predicting water content using Gaussian model on soil spectra

This paper presents an approach to estimating soil moisture content through fitting an inverted Gaussian function to the continuum in soil spectra. The soil moisture Gaussian model (SMGM) estimates the water content by the declining reflectance in the near infrared (NIR) and shortwave infrared (SWIR) regions, 1.2–2.5 Am, due to the spreading of the fundamental water absorption at 2.8 Am. Convex hull boundary points were used to isolate the spectral continuum and to fit the inverted Gaussian function. The function extrapolates the continuum to the fundamental water absorption beyond the wavelength limits of common laboratory, field, and airborne instruments. Of the derived functional parameters, both amplitude and area on the shortwave side of the inverted Gaussian curve were highly correlated with soil water content. In this study, laboratory spectra, from 0.4 to 2.5 Am, were measured at sequential moisture levels in soil samples collected in Castilla-La Mancha, Spain and in California, USA. The Gaussian area was determined to be the best indicator of gravimetric water content with the initial modeling of 2592 spectra. The SMGM was validated with a separate set of 849 spectra. The model performance significantly improved for water contents below a critical level of 0.32 g water/g soil. Within this restricted range, the SMGM predicted water contents for all soils with a maximum of 0.027 RMSE for 1901 modeled spectra and 0.026 for 602 validation spectra. The water content estimates were improved slightly by stratifying the model and validation sets by the two locations, reducing the RMSE to 0.023 in Spain and 0.025 in California. Further stratifying the model spectra by landform and soil sodicity improved some predictions substantially, but less consistently. Stratifying the samples locally demonstrated that a priori knowledge of soil surfaces by landforms should be part of an image calibration strategy. The SMGM provides practical water content estimates and has a potential use in correcting the effects of soil moisture in hyperspectral images. D 2003 Elsevier Inc. All rights reserved.