Soil moisture information is of critical importance to real-world applications such as agriculture, water resource management, flood, fire and landslide prediction, mobility, soil hydraulic parameter estimation etc. Many of these applications require soil moisture information at high resolution. While this may be estimated from land surface models, the predictions are often poor due to inadequate model physics, poor parameter estimates and erroneous atmospheric forcing data. An alternative is remote sensing but most techniques only give a soil moisture estimate for the top few centimetres. Moreover, the sensors that give the most reliable soil moisture estimates (passive microwave) have relatively low spatial resolution from space, being on the order of 50 km. Such sensors include the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission launched in Nov 2009, and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission scheduled for launch in Oct 2014. Other high spatial resolution satellite observations such as active microwave, visible and thermal have been shown to contain information on soil moisture, but their data is noisy and/or difficult to interpret. However, it is expected that the low resolution passive microwave data may be downscaled using the noisy high resolution data and/or modeling. For example, SMAP will provide a better than 10 km resolution soil moisture product by merging 3 km active microwave data with 40 km passive microwave data. This paper presents some examples of high resolution soil moisture mapping from ground and airborne techniques, combined active-passive satellite soil moisture retrieval, optical downscaling, and assimilation into a high resolution land surface model.