Abstract
An artificial neural network (ANN) based algorithm is implemented and tested for soil moisture estimation. The ANN model is calibrated (trained) and validated (tested) with data including National Centers for Environmental Protection (NCEP) daily precipitation; normalized difference vegetation index (NDVI) data processed by the US Geological Survey Earth Resources Observations Systems (USGS EROS) data center; Geostationary Operational Environmental Satellite (GOES) based, cloud-masked infrared (IR) skin temperature produced by the University of Maryland; and soil moisture profiles measured over the Oklahoma (OK) Mesonet. The performance of the ANN model is evaluated by direct comparison between the soil moisture estimated by the ANN model and the Mesonet measurements and by examining the correlation between them. Strong correlation is demonstrated between the ANN estimates and Mesonet measurements for spatially averaged data. This work suggests that the ANN model is a promising alternative to soil moisture estimation. The advantage of the ANN approach to soil moisture estimation is that it can provide estimates having resolution commenmmensurate with remotely sensed IR data and has the potential for worldwide coverage.
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