3.7 Disaggregation of Microwave Remote Sensing Data for Estimating Near-surface Soil Moisture Using a Neural Network

1.1 Statement of problem Estimation of soil moisture using microwave remote sensors holds great promise for many applications, including numerical weather prediction and agriculture. However, a scale disparity exists between the resolutions of future satellite-borne microwave remote sensor data (30-60 km) and the much finer scales at which soil moisture estimates are desired (~ 1 km). Hydrology models may be useful for bridging this gap, as the factors controlling soil moisture variability (precipitation, soil and vegetation properties, topography) are known with reasonable accuracy at fine spatial scales and can be used in models to estimate the spatial distribution of soil moisture at high resolutions. Therefore, in order to facilitate the assimilation of remote sensing data, it is important to explore ways to disaggregate low-resolution passive microwave remote sensing data to the higher resolution of a hydrologic model.