Abstract
Equivalent water thickness (EWT) and leaf mass per area (LMA) are important indicators of plant processes, such as photosynthetic and potential growth rates and health status, and are also important variables for fire risk assessment. Retrieving these traits through remote sensing is challenging and often requires calibration with in situ measurements to provide acceptable results. However, calibration data cannot be expected to be available at the operational level when estimating EWT and LMA over large regions. In this study, we assessed the ability of a hybrid retrieval method, consisting of training a random forest regressor (RFR) over the outputs of the discrete anisotropic radiative transfer (DART) model, to yield accurate EWT and LMA estimates depending on the scene modeling within DART and the spectral interval considered. We show that canopy abstractions mostly affect crown reflectance over the 0.75–1.3 μm range. It was observed that excluding these wavelengths when training the RFR resulted in the abstraction level having no effect on the subsequent LMA estimates (RMSE of 0.0019 g/cm2 for both the detailed and abstract models), and EWT estimates were not affected by the level of abstraction. Over AVIRIS-Next Generation images, we showed that the hybrid method trained with a simplified scene obtained accuracies (RMSE of 0.0029 and 0.0028 g/cm2 for LMA and EWT) consistent with what had been obtained from the test dataset of the calibration phase (RMSE of 0.0031 and 0.0032 g/cm2 for LMA and EWT), and the result yielded spatially coherent maps. The results demonstrate that, provided an appropriate spectral domain is used, the uncertainties inherent to the abstract modeling of tree crowns within an RTM do not significantly affect EWT and LMA accuracy estimates when tree crowns can be identified in the images.
Highlights
The five Mediterranean climate regions possess a unique biodiversity richness [1,2], including both terrestrial and aquatic ecosystems, despite their limited extent
We assessed how the level of abstraction used to represent a scene within an radiative transfer models (RTM) affects crown reflectance over the 0.75–2.4 μm spectral range and what it means concerning Equivalent water thickness (EWT) and leaf mass per area (LMA) estimation accuracies in the context of hybrid retrieval methods
The hybrid method consisted of training an random forest regressor (RFR) over databases generated with the discrete anisotropic radiative transfer (DART) model, considering two different spectral intervals
Summary
The five Mediterranean climate regions (located in California, Chile, South Africa, Australia, and around the Mediterranean Basin) possess a unique biodiversity richness [1,2], including both terrestrial and aquatic ecosystems, despite their limited extent. Mediterranean vegetation is well adapted to these conditions and can rapidly recover after summer droughts and wildfires. Increasing anthropic pressure through both urban and agricultural development and changing climatic conditions is threatening the biodiversity of these ecoregions, so much so that the Mediterranean biome is expected to experience the greatest biodiversity change by 2100 [5]. As the recent wildfires in California and Australia illustrate, fire risk is likely to considerably increase in the future, with strong impacts on wildlife [6]
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