Enhancement of Passive Microwave Soil Moisture Retrievals using Visible/Infrared Imager

Abstract:

Passive microwave (PM) observations of soil moisture (SM), like those produced from data observed by the AMSR-E, WindSat, AMSR2, and SMOS instruments, provide global soil moisture data sets with moderate resolution (~25km), reasonable accuracy (±10%), and short revisit times (2-3 days). A principal source of the current error in these SM data sets is due to heterogeneous topography below the native resolution of the PM instrument. A single PM antenna footprint may encompass surface water, dense and/or sparse vegetation, and bare soil. We show that by using high resolution (~250m) visible/infrared (VIS/IR) observations to estimate the fractions of water, vegetation, and bare soil in each PM footprint, we can deconvolve the brightness temperatures from each individual component. This allows for greatly increased accuracy in the remotely sensed soil moisture content. We will present our results in applying this technique to the WindSat soil moisture algorithm using WindSat PM data and vegetation and water fraction estimates derived from MODIS VIS/IR data.