Soil moisture estimation using remote sensing

Knowledge of soil moisture content in the root zone is important throughout a wide range of environmental applications, yet adequate monitoring or modelling of this parameter, particularly at larger spatial scales, is difficult due to its high spatial and temporal variability. To overcome the land surface model limits on soil moisture estimation accuracy, point measurement spatial coverage limits, and microwave remote sensing spatial-temporal sampling limits, we reduce uncertainties through a combination of these approaches. The land surface model, forced with observation constrained European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data, is used to estimate the spatial and temporal variation of soil moisture content. Near-surface soil moisture measurements from the 6.6 GHz (C-band) channels of the Scanning Multi-channel Microwave Radiometer (SMMR) are then assimilated using a Kalman filter to correct for soil moisture estimation errors. Comparison with the limited ground-based point measurements of soil moisture content found a net improvement when near-surface soil moisture observations were assimilated. Key Words: soil moisture, remote sensing, land surface modelling, data assimilatio