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Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery
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Date
2008-02-15
Author
Yılmaz, Mustafa Tuğrul
Hunt Jr., E. Raymond
Goins, Lyssa D.
Ustin, Susan L.
Vanderbilt, Vern C.
Jackson, Thomas J.
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 mu m wavelength) and band 5 (1.55-1.65 mu m wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Axizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R-2 of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation. Published by Elsevier Inc.
Subject Keywords
Computers in Earth Sciences
,
Soil Science
,
Geology
URI
https://hdl.handle.net/11511/47750
Journal
Remote Sensing of Environment
DOI
https://doi.org/10.1016/j.rse.2007.03.029
Collections
Department of Civil Engineering, Article