Abstract
For estimation of root-zone moisture content from EO-1/Hyperion imagery, surface soil moisture was first predicted by hyperspectral reflectance data using partial least square regression (PLSR) analysis. The textures of more than 300 soil samples extracted from a 900 m × 900 m 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 18,000 compartments (30 m × 30 m × 0.02 m), 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 by application of artificial neural network analysis and used to parameterize HYDRUS-1D to simulate averaged soil-moisture contents within the root zone (0-40 cm) and surface (approximately 0-4 cm). Then the link between surface moisture and root zone was established by use of linear regression analysis, resulting in R and RMSE of 0.38 and 0.03, respectively. Kriging and co-kriging with observed surface moisture, and co-kriging with surface moisture obtained from Hyperion imagery were also used to estimate root-zone moisture. Results indicated that PLSR is a powerful tool for soil moisture estimation from hyperspectral data. Furthermore, co-kriging with observed surface moisture had the highest R (0.41) and linear regression model, and HYDRUS Monte Carlo simulations had a lowest RMSE (0.03) among the four methods. In regions that have similar climatic and soil conditions to our study area, a linear regression model with HYDRUS Monte Carlo simulations is a practical method for root-zone moisture estimation before sowing and it can be easily coupled with remote sensing technology.
Estimating Root Zone Moisture from Surface Soil Using Limited Data