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The article discusses the use of hyperspectral reflectance data to predict leaf metabolism, specifically focusing on nonphotorespiratory mitochondrial CO2 release (Rdark). Traditional trait-based models have limitations in predicting Rdark, leading researchers to explore alternative approaches. By incorporating hyperspectral reflectance data, researchers can capture a wider array of leaf traits and move beyond traditional unidimensional approaches, offering new insights into plant physiological dynamics and ecosystem monitoring. [Extracted from the article]