|Bindlish, Rajat -|
|Zhao, Tianjie -|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: April 1, 2011
Publication Date: N/A
Technical Abstract: Our motivation here is to provide information for the NASA Soil Moisture Active Passive (SMAP) satellite soil moisture retrieval algorithms (launch in 2014). Vegetation attenuates the signal and the algorithms must correct for this effect. One approach is to use data that describes the canopy water content or biomass, which can be estimated using vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Vegetation parameters need to be included from ancillary sources since SMAP does not include any sensor that can provide them. This presents challenges to data processing and integration and concerns about data availability. As an alternative or back-up to routine updating of the NDVI, we are suggesting the development of a global NDVI and EVI annual cycle. This is based on the most recent long term set of observations from the MODIS instrument (10 years). A technique was developed to process the NASA MODLAND NDVI and EVI data base to produce a 10-day annual cycle for each 1 km pixel covering the Earth’s land surface. Since our focus was on soil moisture, the classification rules and flags took this into consideration.