Climate change is altering the water-table (WT) height and near-surface moisture conditions in northern peatlands, which in turn both increases the susceptibility to fire and reduces the carbon sink capacity of these ecosystems. To further develop remote sensing-based measurements of peatland moisture characteristics, we employed coincident surface reflectance and moisture measurements in two Sphagnum moss-dominated peatland sites. We applied the Mexican hat continuous wavelet transform to the measured spectra to generate wavelet features and coefficients across a range of scales. Overall, wavelet analysis was an improvement over the previously tested spectral indices at both the study sites. Linear mixed effect models for WT height using wavelet features accounted for more of the variance with both an improved marginal    $R^{\mathrm { {2}}}$    (29% greater) and a larger conditional    $R^{\mathrm { {2}}}$    (21% greater) compared to the best performing spectral index. While spectral indices performed similarly with wavelet coefficients for moisture content measured at 3 cm depth, they performed poorly for volumetric moisture content measured at 7 cm depth. The current study also revealed the advantage of selecting the best subsets of wavelet features based upon genetic algorithm over a more widely used technique that selects features based on correlation scalograms. It also provided new insights into the significance of various spectral regions to detect WT alteration-induced vegetation change.
['Asim Banskota', 'Michael J. Falkowski', 'Alistair M. S. Smith', 'Evan S. Kane', 'Karl M. Meingast', 'Laura L. Bourgeau-Chavez', 'Mary Ellen Miller', 'Nancy H. F. French']
Continuous Wavelet Analysis for Spectroscopic Determination of Subsurface Moisture and Water-Table Height in Northern Peatland Ecosystems