A Novel Method for Estimation of Root Zone Moisture Content from EO-1 Hyperion Hyperspectral Imagery.

Remote sensing plays a crucial role in predicting soil moisture distributions over large regions, which is important for agricultural water resources management. However, remotely sensed data only is applicable for near-surface moisture content estimation. This presentation illustrates a novel method for estimation of root zone moisture content from EO-1 Hyperion hyperspectral imagery based on numerical modeling of soil moisture dynamics. The textures of more than three hundred soil samples extracted from a 900×900m field site located within the Hetao Irrigation District in China were used to parameterize the HYDRUS-1D numerical model. The study area was spatially discretized into 18000 compartments (30×30×0.02m) and Monte Carlo simulations were applied to generate 2000 different soil particle size distributions for each compartment. Soil hydraulic properties for each realization were determined with ROSETTA and used to parameterize HYDRUS-1D to simulate averaged soil moisture contents within the root zone (0-40 cm) and topsoil (0-5 cm). The average regional-scale soil moisture content within the root zone and associated uncertainty were determined to guide irrigation management.