Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/29/2008
Publication Date: 12/30/2008
Publication URL: http://hdl.handle.net/10113/27794
Citation: Shi, J., Jackson, T.J., Tao, J., Du, J., Bindlish, R., Lu, L., Chen, K.S. 2008. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sensing of Environment. 112:4285-4300. Interpretive Summary: Conventional vegetation indices are developed using visible-infrared sensors. These indices are limited by the presence of clouds and can be observed only during the day. Microwave observations allow all weather monitoring. A new set of microwave vegetation indices was developed that uses operational (AMSR-E) satellite observations provided by the Advanced Microwave Scanning Radiometer. These vegetation indices use observations at multiple frequencies to cancel the effect of bare surface emission signal. The results were compared with conventional vegetation indices derived using MODIS observations. Qualitative and quantitative analyses results indicate that the MVIs can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the microwave sensor can “see” through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring agricultural crops at global scales and seasonal phenology from space. This information would be very useful for improving reliable monitoring of crops by USDA-Foreign Agricultural Service and other agencies for the U.S. and world-wide.
Technical Abstract: Vegetation indices are valuable in many fields of geosciences. Conventional, visible-near infrared, indices are often limited by the effects of atmosphere, background soil conditions, and saturation at high levels of vegetation. In this study, the theoretical basis for a new passive microwave vegetation indices (MVIs) based on data from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite was established. Through the analysis of numerical simulations by surface emission model, the Advanced Integral Equation Model (AIEM), it was found that bare soil surface emissivities at different frequencies can be characterized by a linear function with parameters that are dependent on the pair of frequencies used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-frequency radiometer measurements. Using a radiative transfer model, a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. The intercept and slope of this linear function depend only on vegetation properties and can be used as vegetation indices. These can be derived from the dual-frequency and dual-polarization satellite measurements under assumption that there is no significant impact of the polarization dependence on the vegetation signals. To demonstrate the potential of the new microwave vegetation indices, these were compared with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS the optical sensor at continental and global scales. The major purpose of this paper was to describe the concept and techniques involved in these newly developed MVIs and explore the general relationships between these MVIs and NDVI. In this first investigation, the information content of NDVI and MVIs, both are qualitative indices, was compared by examining its response in global pattern and to seasonal vegetation phenology. The results indicate that the MVIs can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the microwave sensor can “see” through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring global short vegetation and seasonal phenology from space.