Ms. Julia K. Marrs Profile

Julia K. Marrs

at Boston Univ

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Proceedings Article | 14 May 2019 Paper

Solar-induced fluorescence retrievals in the context of physiological, environmental, and hardware-based sources of uncertainty

Julia Marrs, Lucy Hutyra, David Allen

Proceedings Volume 10986, 109860F (2019) https://doi.org/10.1117/12.2520457

KEYWORDS: Spectroscopy, Signal to noise ratio, Absorption, Luminescence, Calibration, Vegetation, Sensors, Carbon, Signal detection, Atmospheric corrections

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The terrestrial biosphere is a crucial sink for anthropogenic emissions of carbon to the atmosphere, but is also the source of the largest uncertainties in estimated global carbon budgets. Numerous tower- and satellite-based platforms have recently been established to measure solar-induced fluorescence (SIF), which, as a proxy for photosynthesis, shows great promise for constraining global estimates of gross primary productivity. Nonetheless, published SIF retrievals span two orders of magnitude, illustrating an opportunity for improved characterization of the SIF signal in the context of instrument noise, detector calibrations and limitations, viewing geometry, and typical signal magnitude. In 2017, the Forested Optical Reference for Evaluating Sensor Technology (FOREST) site was established at the National Institute of Standards and Technology (NIST) as a test-bed for SIF instrument intercomparison and calibration methods development. Further, we empirically characterize the physiological and ecological meaning of SIF by directly linking to carbon exchange with an extensive suite of ground measurements. Following optimizations to our SIF spectrometer deployment, we find that deviations from ideal measurement conditions, including low light or intermittent cloud cover, introduce significant noise outside even dramatic physiological manipulations. It is critical that common standards are developed for SIF measurement systems to ensure validation of data quality and clear linkages to physiological and biophysical parameters. SIF is a promising technique to improve measurement and understanding of local to global trends in primary productivity, but data quality control is a key challenge to tackle with the rapid deployment of new sensors across the globe. This work is an initial evaluation of sensitivities of SIF signals to hardware and methodologies.

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