Linking Seasonal Foliar Chemistry to VSWIR-TIR Spectroscopy Across California Ecosystems
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Presented at the 2014 HyspIRI Workshop (http://hyspiri.jpl.nasa.gov/documents/2014-workshop)
Synergies between the Visible Near Infrared/ Short Wave Infrared (VSWIR) and Thermal Infrared (TIR) spectra for identifying plant species’ foliar chemistry have been largely unexplored. Here we evaluate the capability of VSWIR and/or TIR spectra to predict leaf levels of lignin, cellulose, nitrogen, water content, and leaf mass per area. We specifically examined how these predictive relationships might change seasonally and among plant functional types. Lastly we determined whether these relationships between spectra and foliar chemistry could be extended to the reduced spectral resolution available in airborne and proposed spaceborne sensors, including the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), the Hyperspectral Thermal Emission Spectrometer (HyTES), and the Hyperspectral Thermal Imager (HyspIRI).
In the 2013 spring, summer, and fall seasons, fresh leaves from sixteen common shrub and tree species in California representing three broad plant functional types were sampled from the Sierra Nevada Mountains, the Central Valley, and coastal Santa Barbara. Partial Least Squares (PLS) regression analysis was used to relate spectral response at wavelengths from 0.3 – 15.4 µm to laboratory-measured biochemical and biophysical properties. For each component, three PLS regression models were fit using different portions of the spectrum: VSWIR (0.3 – 2.5µm), TIR (2.5 – 15.4µm), and the entire spectrum (0.3 – 15.4µm). Three additional models were fitted using spectra resampled to AVIRIS (0.4 – 2.5µm), HyTES (7.5 – 12µm), and HyspIRI (0.38 – 12µm).
The majority of the highest performing models used either the TIR or full spectrum. When using simulated sensor spectra, HyspIRI produced the highest performing models, followed by HyTES. From model results the combination of VSWIR and TIR increased the R2 value of regression models compared to VSWIR alone, signifying that the inclusion of TIR data would improve predictions of foliar chemistry. We also found that model precision varied by seasons and across plant functional types. Models developed for all seasons resulted in a decreased R2 value or required twice the number of factors to explain the variance of a single season. These results indicate that the TIR could augment the VSWIR in advancing identification of leaf properties of the world’s ecosystems by helping to set the foundation for future use of full spectrum satellite imagery through HyspIRI.