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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 depend on the type of land cover; thus, we can estimate SIF yield information based on the observed values for specific land types. 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. Pseudo-replicates using K-nearest neighbors are used to select the best penalizing value. The adjacent sites are chosen according to a minimum spanning tree (MST) in order to reduce redundancy in site pairing.
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