|Hunt, Earle - Ray|
Submitted to: Israel Journal of Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/3/2012
Publication Date: 1/11/2013
Publication URL: http://handle.nal.usda.gov/10113/59822
Citation: Ustin, S.L., Riano, D., Hunt, E.R. 2013. Estimating canopy water content from spectroscopy. Israel Journal of Plant Science. 60(1):9-23. Interpretive Summary: The manuscript provides an overview of remote sensing for determining the amount of liquid water in vegetation canopies. Physiologically, two variables are used to quantify the amount of drought experienced by vegetation: plant water potential and relative water content. Furthermore, the amount of water per biomass (fuel moisture content) is used to estimate the potential for wildfires. Remotely-sensed indices from two or more spectral bands can be used to estimate changes in water content but not water potential or relative water content. These indices can be calculated from many different satellite sensors; however, they are sensitive to canopy structure and the soil surface reflectance. Physically-based models and computer methods are better able to estimate water content of vegetation canopies, particularly for estimation of fuel moisture content.
Technical Abstract: Foliar water content is a dynamic quantity depending on water losses from transpiration and water uptake from the soil. Absorption of shortwave radiation by water is determined by various frequency overtones of fundamental bending and stretching molecular transitions. Leaf water potential and relative water content are important variables for determining water deficit and drought effects; however, these variables may only be indirectly estimated from leaf and canopy spectral reflectances. Indices using different combinations of spectral bands may be used to estimate leaf and canopy water contents but have large errors caused by variations of canopy structure and soil surface reflectance. Inversion of radiative transfer models such as PROSPECT and SAIL with artificial neural networks is a promising method for creating global datasets of canopy water content.