Assessment of Potential AMSR-E Soil Moisture Disaggregation Using Scatterometer Observations
V. Lakshmi and I. Mladenova
Accurate representation of the land–atmospheric processes influencing global climate is a difficult and challenging problem. Overcoming this problem would most likely lead to gaining valuable knowledge allowing improved understating of the water cycle and the complex interaction between land surface and atmosphere. Soil moisture is an essential factor in the partitioning of the surface fluxes. It controls the rates of important key variables in the water cycle such as potential evapotranspiration and precipitation. However, the lack of long-term data sets with adequate spatial and temporal resolution to certain extend limits long term forecasting by means of hydrologic and climatologic modeling.
The variety of available satellites and instruments on the NASA EOS platforms and the launch of the Advanced Microwave Scanning Radiometer (AMSR-E) on the NASA’s AQUA platform in 2002 have made it possible to derive land surface variables such as soil moisture in near real-time. Thus far AMSR-E is the only satellite sensor that supplies a soil moisture product. Past field campaigns combining in situ and aircraft measurements proved the high accuracy of the AMSR-E soil moisture. A fundamental ongoing issue with the satellite estimates is coarse spatial scale and how to downscale them to a spatial resolution compatible with applications.
The available radar systems have limited temporal and spatial coverage. Since the disaggregation algorithm (developed by Narayan, Lakshmi and Jackson, 2006) proposed for this study is based on temporal change detection in soil moisture based on change in radiometer observed brightness temperature and radar measured backscatter temporal resolution is an important issue along with the strong dependence of backscatter observations to surface roughness and terrain topography.
QuikSCAT scatterometer, which offers daily observations with 2.225km ground pixel size for the enhanced sigma-0 product developed by the Brigham Young University Centre for Remote Sensing (http://www.scp.byu.edu/data/Quikscat/SIR/Quikscat_sir.html) seams to be a desirable alternative offering a long-term data set availability with high temporal resolution for developing downscaling technique for disaggregation of radiometer derived soil moisture estimates (i.e. AMSR-E).
The research so far mainly focuses on QuikSCAT backscatter sensitivity to soil moisture and its potential use for AMSR-E disaggregation over the National Airborne Field Experiment 2006 area.