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Lopatin. (2023). Interannual Variability of Remotely Sensed Phenology Relates to Plant Communities. IEEE Geosci. Remote. Sens. Lett., 20, 2502405.
Abstract: Vegetation phenology is considered an essential biological indicator in understanding the behavior of ecosystems and how they respond to environmental cues. However, the potential of interannual variations of remotely sensed phenology signals to differentiate plant types remains poorly understood, especially in understudied systems with highly heterogeneous landscapes such as wetlands. This study presents a case study in a San Francisco Bay area marsh that investigates the usefulness of interannual variation, defined as the root-mean-square error of enhanced vegetation index (EVI) measurements against a fitted phenology curve, at the beginning, middle, and end of the growing season as indicators of plant types. The study found that altitude above sea level and certain land surface phenology metrics, such as the day-of-the-year of the end of the season, the mid-autumn day, and the greening rate before the summer peak, were significantly related to these interannual variation trends. These results indicate that a detailed time-series analysis at the beginning and end of growing seasons may enhance large-scale wetland characterization. Overall, the findings of this study contribute to our understanding of vegetation phenology and provide a framework for more accurate wetland classification in future studies.
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.