Evaluating the application of microwave-based vegetation observations in an operational soil moisture data assimilation system
- Resource Type
- Conference
- Authors
- Mladenova, Iliana E.; Bolten, John. D.; Crow, Wade; de Jeu, Richard
- Source
- 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International. :5190-5193 Jul, 2015
- Subject
- Geoscience
Soil moisture
Vegetation mapping
Adaptive optics
Optical sensors
Microwave filters
Optical filters
Agriculture
data assimilation
soil moisture
observation error
vegetation density
optical depth
- Language
- ISSN
- 2153-6996
2153-7003
A primary operational goal of the United States Department of Agriculture (USDA) is to improve foreign market access for U.S. agricultural products. A large fraction of this crop condition assessment is based on satellite imagery and ground data analysis. The baseline soil moisture estimates that are currently used for this analysis are based on output from the modified Palmer two-layer soil moisture model, updated to assimilate near-real time observations derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The current data assimilation system is based on a 1-D Ensemble Kalman Filter approach, where the observation error is modeled as a function of vegetation density. This allows for offsetting errors in the soil moisture retrievals. The observation error is currently adjusted using Normalized Difference Vegetation Index (NDVI) climatology. In this paper we explore the possibility of utilizing microwave-based vegetation optical depth instead.