Detecting diurnal and seasonal variation in canopy water content of nut tree orchards fromairborne imaging spectroscopy data using continuous wavelet analysis

摘要

Continuous wavelet analysis (CWA) has recently been applied to leaf-level spectroscopic data for quantifying foliar chemistry, but it is unclear how well or whether CWA can be applied to imaging spectroscopy data under the conditions of higher noise level and more complicating factors. This study evaluates the application ofCWA to airborne imaging spectroscopy data for predicting diurnal and seasonal variation in canopy water content (CWC) for nut tree orchards. We collected CWC measurements and concurrent imagery from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) instrument twice a day (morning and afternoon) in spring and fall of 2011 in California, USA. Several robust wavelet features were determined and compared to four watersensitive spectral indices, three existing in the literature and one optimized in this study, for the assessment of predictive performance. Results showed that the best prediction using CWA (R~2 = 0.84 and root mean square error (RMSE) = 0.027 kg/m~2)was produced by a combination of threewavelet features and itwas considerably better than those by the existing water indices.While the best wavelet feature (1100 nm, scale 6) characterized the water absorption in the near-infrared region, the optimized index ND_(850,720) used a red edge band at 720 nm instead of a directwater absorption band. A bootstrap sampling of the validation data set indicated that ND_(850,720) predicted CWC significantly worse (p < 0.0001) and exhibited greater sensitivity to seasonality. Both CWA and ND_(850,720) revealed statistically significant diurnal declines of CWC in two different seasons in the context of a substantial seasonal decline, but the former detected greater declines in diurnal CWC. Our results demonstrated the feasibility of applying CWA to airborne imaging spectroscopy data for CWC mapping and its superiority to spectral indices for improved prediction of CWC and understanding of spectral–chemical relations.