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Abstract:
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The amount of carbon assimilated by plants through photosynthesis, called Gross Primary Productivity (GPP), is the largest carbon flux between the terrestrial biosphere and the atmosphere and, if quantified accurately, can grant insights into understanding several ecosystem functions as well as the impact of climate change to crop yields. Recently, satellite-based measurements of solar-induced chlorophyll fluorescence (SIF) have been used as a strong proxy to measure GPP. SIF values will depend on the type of land cover; thus, the observed values can be decomposed into the specific land type components to obtain its SIF yield information. We propose to implement a spatially varying coefficient regression model where the coefficients represent the specific SIF yields. For each land type coefficient we induce spatial dependence by penalizing the square deviations among adjacent sites according to some data-driven threshold value. The adjacent sites are chosen according to a minimum spanning tree (MST) in order to reduce redundancy in site pairing. An additional constraint of non-negativity is implemented to the coefficients so that interpretability of the results is maintained.
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