Remote sensing of vegetation water content using shortwave infrared reflectances

E. Raymond Hunt Jr.; M. Tugrul Yilmaz

doi:10.1117/12.734730

6 October 2007 Remote sensing of vegetation water content using shortwave infrared reflectances

E. Raymond Hunt Jr., M. Tugrul Yilmaz

Proceedings Volume 6679, Remote Sensing and Modeling of Ecosystems for Sustainability IV; 667902 (2007) https://doi.org/10.1117/12.734730
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

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.

Citation Download Citation

E. Raymond Hunt Jr. and M. Tugrul Yilmaz "Remote sensing of vegetation water content using shortwave infrared reflectances", Proc. SPIE 6679, Remote Sensing and Modeling of Ecosystems for Sustainability IV, 667902 (6 October 2007); https://doi.org/10.1117/12.734730

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