|Published in||Remote Sensing of Environment, v. 115(2):659-670|
Cheng, T., Rivard, B. and Sánchez-Azofeifa, G.A.
Earth Observation Systems Laboratory, Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2E3
The gravimetric water content (GWC, %), a commonly used measure of leaf water content, describes the ratio of water to dry matter for each individual leaf. To date, the relationship between spectral reflectance and GWC in leaves is poorly understood due to the confounding effects of unpredictably varying water and dry matter ratios on spectral response. Few studies have attempted to estimate GWC from leaf reflectance spectra, particularly for a variety of species. This paper investigates the spectroscopic estimation of leaf GWC using continuous wavelet analysis applied to the reflectance spectra (350&ndash2500 nm) of 265 leaf samples from 47 species observed in tropical forests of Panama. A continuous wavelet transform was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength and scale. Linear relationships were built between wavelet power and GWC expressed as a function of dry mass (LWCD) and fresh mass (LWCF) in order to identify wavelet features (coefficients) that are most sensitive to changes in GWC. The derived wavelet features were then compared to three established spectral indices used to estimate GWC across a wide range of species.
Eight wavelet features observed between 1300 and 2500 nm provided strong correlations with LWCD, though correlations between spectral indices and leaf GWC were poor. In particular, two features captured amplitude variations in the broad shape of the reflectance spectra and three features captured variations in the shape and depth of dry matter (e.g., protein, lignin, cellulose) absorptions centered near 1730 and 2100 nm. The eight wavelet features used to predict LWCD and LWCF were not significantly different however, predictive models used to determine LWCD and LWCF differed. The most accurate estimates of LWCD and LWCF obtained from a single wavelet feature showed root mean square errors (RMSEs) of 28.34% (R2 = 0.62) and 4.86% (R2 = 0.69), respectively. Models using a combination of features resulted in a noticeable improvement predicting LWCD and LWCF with RMSEs of 26.04% (R2 = 0.71) and 4.34% (R2 = 0.75), respectively. These results provide new insights into the role of dry matter absorption features in the shortwave infrared (SWIR) spectral region for the accurate spectral estimation of LWCD and LWCF. This emerging spectral analytical approach can be applied to other complex datasets including a broad range of species, and may be adapted to estimate basic leaf biochemical elements such as nitrogen, chlorophyll, cellulose, and lignin.