The short-term memory model is a nonlinear stochastic transfer function that estimates the soil moisture response in hourly basis for every grid. Estimation of soil moisture in an hourly response requires using the cumulative rainfall during the last week (NEXRAD), the hourly rainfall (NEXRAD), the hourly temperature estimated from MODIS and the initial level of soil moisture estimated from the neural network. The instantaneous soil moisture response is modulated by exponential factor that is applied to the impulse response function.
The empirical model was designed to estimate soil moisture at 2, 4, 8, 20 and 40 inches depth and was applied to Puerto Rico climate conditions. The proposed method may be implemented to a similar tropical region. The proposed method can be used to create the initial conditions of soil moisture for running atmospheric and hydrological models such as: the regional atmospheric modeling system (RAMS), the mesoscale model (MM5), the MIKE SHE and VFLOW hydrological models, etc. Cross-validation techniques show that the proposed algorithm is a potential tool to estimate soil moisture over densely vegetated areas.