Land Surface Modeling Studies in Support of AQUA AMSR-E Validation

PI: Eric F. Wood
Institution: Princeton University
Department of Civil and Environmental Engineering
Princeton, NJ 08544
Phone: (609) 258-4675
FAX: (609) 258-4675
Email: efwood@princeton.edu


EOS Team: AMSR-E

NASA EOS-PSO funding through FY02: $92,326

ABSTRACT

A central focus of ESE's Global Water and Energy Cycle (GWEC) program, requires measuring spatially distributed soil moisture and snow properties from space using the AMSR-E instrument on the EOS Aqua spacecraft. As currently planned, the AMSR-E validation plans focus on long-term, point observations and short-term field campaigns. The AMSR-E science data validation plans is developed around in-situ validation data, primarily through long term point measurements and limited field programs using airborne radiometers. The overall goal of this project is to strengthen the validation efforts by providing modeling support to the AMSR-E validation activities through a combination of process-based hydrological modeling and the simulation of the AMSR-E measurements, including the AMSR-E antenna pattern, orbital characteristics, and the gridded products. The modeling effort of this project will help bridge the gap between the small-scale in-situ data and the AMSR-E footprint, and between the short-term field experiments and continuous AMSR-E measurements.

In the project we will combine real-time modeling of the terrestrial water and energy balance with a microwave emission model to provide AMSR-E specific data at a modeling resolution of 10 km or better for the contiguous United States. The resulting modeling products (soil moisture and snow properties) will be used in support of the AMSR ground validation activities to address the following validation questions:
1. What is the accuracy of the AMSR-E level-2 and -3 gridded data products, given the surface heterogeneity within the sampling footprint?
2. What relationships can be established between the small-scale ground validation and the AMSR-gridded product, and how will these relationships vary with geographic region and season?
3. Can the modeling and AMSR-E antenna simulations help in the effective design of validation studies, and in error estimates for the AMSR-E data products?