Estimation of Soil Water Content Using Short Wave Infrared Remote Sensing.

Technological advances in satellite remote sensing have offered a variety of techniques for estimating soil water content as a key variable in numerous environmental studies. Optical methods are particularly valuable for remote sensing of soil moisture since reflected solar radiation is the strongest passive signal available to satellites and thus observations at optical wavelengths are capable of providing high spatial resolution data. Since remote sensors do not measure soil water content directly, mathematical algorithms that describe the connection between the measured signal and water content must be derived. In this paper, we present a physically-based soil moisture retrieval algorithm in the solar domain (400 – 2500 nm) that is based on the Kubelka-Munk two-flux radiative transfer model. The model is designed to describe diffuse reflectance from a uniform, optically thick, absorbing and scattering medium. The theory suggests a linear relationship between a transformed reflectance and soil water content in the short wave infrared bands (e.g. bands 5 and 7 of Landsat TM and ETM+ satellites) providing an easy-to-use algorithm in these bands. Accuracy of this algorithm was tested and preliminarily verified using laboratory-measured spectral reflectance data of several different soils. Further studies on potentials and challenges of this algorithm for large-scale application using optical satellites (e.g. Landsat, MODIS) data remain a topic of ongoing research.