89th American Meteorological Society Annual Meeting

Tuesday, 13 January 2009
GOES-R/ABI for vegetation health monitoring
Hall 5 (Phoenix Convention Center)
Felix Kogan, NOAA/NESDIS, Camp Springs, MD; and W. Guo and Y. Tian
This paper discusses development of improved quality of Vegetation Health (VH) products from the future generation of GOES-R ABI sensor. This new products will address the symposium's topics of detecting “severe weather events and other hazards” that impact our society. The VH is a new technology developed from AVHRR radiance. VH has become very useful and popular in land surface and atmosphere applications especially for such tasks important for environmental security and sustainable development as early drought detection, monitoring land degradation, agricultural production, deforestation, malaria, carbon source/sink, water resources, climate change/variation etc. Unfortunately, AVHRR observations are produced once a day. New generations of GOES-R satellite and sensors in addition to having similar to AVHRR observation in the visible, near infrared and infrared solar spectrum, will be able to provide observations more than 90 observations per day (every 5-15 minutes). The high temporal frequency allows a new approach to temporal compositing inducing an improved removal of cloud-contaminated pixels and other errors in the data. The improved data will help in obtaining much better quality of VH estimates of such weather hazards as drought and develop new applications and products. This presentation provides the results of improved products developed from the SEVIRI instrument onboard Meteosat Second Generation (MSG) satellite, which is used currently as a prototype of GOES-R ABI instrument. It will be demonstrated that the temporal frequency will allow us in addition to less cloud views, to select observations within an optimal sun-sensor viewing geometry, surface structure, angular anisotropy, temporal sampling and other. AVI-prototype VH indices were compared with AVHRR-based and also with in situ data.

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