Lopatin, J. (2023). Estimation of Foliar Carotenoid Content Using Spectroscopy Wavelet-Based Vegetation Indices. IEEE Geosci. Remote. Sens. Lett., 20, 2500405.
Abstract: The plant carotenoid (Car) content plays a crucial role in the xanthophyll cycle and provides essential information on the physiological adaptations of plants to environmental stress. Spectroscopy data are essential for the nondestructive prediction of Car and other traits. However, Car content estimation is still behind in terms of accuracy compared to other pigments, such as chlorophyll (Chl). Here, I examined the potential of using the continuous wavelet transform (CWT) on leaf reflectance data to create vegetation indices (VIs). I compared six CWT mother families and six scales and selected the best overall dataset using random forest (RF) regressions. Using a brute-force approach, I created wavelet-based VIs on the best mother family and compared them against established Car reflectance-based VIs. I found that wavelet-based indices have high linear sensitivity to the Car content, contrary to typical nonlinear relationships depicted by the reflectance-based VIs. These relations were theoretically contrasted with the synthetic data created using the PROSPECT-D radiative transfer model. However, the best selection of wavelength bands in wavelet-based VIs varies greatly depending on the spectral characteristics of the input data before the transformation.
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Villacres, J., Fuentes, A., Reszka, P., & Cheein, F. A. (2021). Retrieval of Vegetation Indices Related to Leaf Water Content from a Single Index: A Case Study of Eucalyptus globulus (Labill.) and Pinus radiata (D. Don.). Plants-Basel, 10(4), 697.
Abstract: The vegetation indices derived from spectral reflectance have served as an indicator of vegetation's biophysical and biochemical parameters. Some of these indices are capable of characterizing more than one parameter at a time. This study examines the feasibility of retrieving several spectral vegetation indices from a single index under the assumption that all these indices are correlated with water content. The models used are based on a linear regression adjusted with least squares. The spectral signatures of Eucalyptus globulus and Pinus radiata, which constitute 97.5% of the forest plantation in Valparaiso region in Chile, have been used to test and validate the proposed approach. The linear models were fitted with an independent data set from which their performance was assessed. The results suggest that from the Leaf Water Index, other spectral indices can be recovered with a root mean square error up to 0.02, a bias of 1.12%, and a coefficient of determination of 0.77. The latter encourages using a sensor with discrete wavelengths instead of a continuum spectrum to estimate the forestry's essential parameters.
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