Calibration and Validation in Active and Passive Microwave Remote Sensing of SMAP Based on Physical Models for Improved Algorithm Performance
National Aeronautics and Space Administration (NASA)
03/19/2014 - 03/18/2019
The NASA Soil Moisture Active/Passive (SMAP) Mission will enable the mapping of soil moisture with unprecedented resolution. Soil moisture is an important parameter of the earth's water, energy and carbon cycle. The mapping of soil moisture is important for hydrologic modeling, climate prediction, and flood and drought monitoring. Soil moisture plays an important forcing function in the interaction between land and atmosphere. It also acts as storage of water between rainfall and evaporation, and influences the infiltration and runoff prediction in hydrologic processes. The mission of SMAP is a combined passive and active polarimetric sensor at the frequencies of 1.41 GHz and 1.26 GHz, respectively. The use of passive L-band radiometer gives a 40km soil-moisture product. The use of active L-band radar provides a 3 km-product. Since the radar has a higher spatial resolution, a combined active/passive retrieval algorithm results in a 9 km soil moisture product.
We have been a member of the Science Definition Team (SDT) in 2008-2013. We propose to be a member of the science team (ST). Our proposed work is in the area of calibration and validation in active and passive microwave remote sensing of SMAP based on physical models for improved algorithm performance. Specifically, we propose the following tasks.
1. Pre-launch Calibration and Validation of 3km active soil moisture product for improvement of algorithm performance that includes error characterization , mixed pixels , global validation using GloSim and Aquarius
2. Post-launch Calibration and Validation of the active 3km soil moisture product and for improvement of algorithm performance
3. Improvement of physical model of cross polarization for calibration and validation and for inclusion of cross polarization in the active algorithm.
4. Calibration and validation of both active and passive data taken over the same scene based on the same physical active/passive forward models and physical parameters of roughness, soil moisture and vegetation