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Title: Remote Sensing of Leaf Equivalent Water Thickness and Vegetation Water Content using Shortwave Infrared Reflectances

Author
item Hunt Jr, Earle
item YILMAZ, M - GEORGE MASON UNIVERSITY
item Jackson, Thomas

Submitted to: American Society for Photogrammetry and Remote Sensing Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 12/19/2007
Publication Date: 4/28/2008
Citation: Hunt, E.R., Yilmaz, M.T., Jackson, T.J. 2008. Remote sensing of leaf equivalent water thickness and vegetation water content using shortwave infrared reflectances [abstract]. American Society for Photogrammetry and Remote Sensing Proceedings. 2008 CDROM.

Interpretive Summary:

Technical Abstract: Vegetation water content is an important biophysical parameter for estimation of soil moisture from microwave radiometers. The Soil Moisture Experiments in 2004 (SMEX04) and 2005 (SMEX05) had an objective of developing and testing algorithms for a vegetation water content data product using shortwave infrared reflectances. SMEX04 studied native vegetation in Arizona, USA, and Sonora, Mexico, while SMEX05 studied corn and soybean in Iowa, USA. The normalized difference infrared index (NDII) is defined as (R850 - R1650)/(R800 + R1650), where R850 is the reflectance in the near infrared and R1650 is the reflectance in the shortwave infrared. Simulations using the Scattering by Arbitarily Inclined Leaves (SAIL) model indicated that NDII is sensitive to surface moisture content. From Landsat Thematic Mapper and other imagery, NDII is linear with respect to foliar water content with R2 = 0.81. The regression standard error of the y estimate is 0.094 mm, which is equivalent to about a leaf area index of 0.5 m2 m-2. Based on modeling the dynamic water flow through plants, the requirement for detection of water stress is about 0.01 mm, so detection of water stress may not be possible. However, this standard error is accurate for input into the tau-omega model for soil moisture. Therefore, NDII may be a robust backup algorithm for MODIS as a standard data product.