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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Remote Sensing of Vegetation Water Content using Shortwave Infrared Reflectance

Authors
item Hunt, Earle
item Yilmaz, Tugrul - PHD STUDENT, GMU

Submitted to: International Society for Optical Engineering
Publication Type: Abstract Only
Publication Acceptance Date: May 8, 2007
Publication Date: August 27, 2007
Citation: Hunt, E.R., Yilmaz, T. 2007. Remote sensing of vegetation water content using shortwave infrared reflectance [abstract]. Summaries of International Society for Optical Engineering. 2007 CDROM.

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 (VWC) 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 soil moisture content. From Landsat Thematic Mapper and other imagery, NDII is linear with respect to foliar water content from 0 to 1 kg/m2. The standard error of the regression is 0.07 kg/m2, which is equivalent to about a leaf area index of 0.5 m2/m2. Based on modeling the dynamic water flow through plants, the requirement for detection of water stress is about 0.01 kg/m2, 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.

Last Modified: 10/21/2014
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