Share Email Print
cover

Proceedings Paper

Remote sensing of vegetation water content using shortwave infrared reflectances
Author(s): E. Raymond Hunt; M. Tugrul Yilmaz
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Vegetation water content is an important biophysical parameter for estimation of soil moisture from microwave radiometers. One of the objectives of the Soil Moisture Experiments in 2004 (SMEX04) and 2005 (SMEX05) were to develop and test 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 Arbitrarily Inclined Leaves (SAIL) model indicated that NDII is sensitive to surface moisture content. From Landsat 5 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.

Paper Details

Date Published: 6 October 2007
PDF: 8 pages
Proc. SPIE 6679, Remote Sensing and Modeling of Ecosystems for Sustainability IV, 667902 (6 October 2007); doi: 10.1117/12.734730
Show Author Affiliations
E. Raymond Hunt, USDA-ARS Hydrology and Remote Sensing Lab. (United States)
M. Tugrul Yilmaz, USDA-ARS Hydrology and Remote Sensing Lab. (United States)
George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 6679:
Remote Sensing and Modeling of Ecosystems for Sustainability IV
Wei Gao; Susan L. Ustin, Editor(s)

© SPIE. Terms of Use
Back to Top