The Mixed Pixel Effect in Land Surface Phenology: A Simulation Study

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Because of the limited spatiotemporal resolutions in vegetation index(VI) products, land surface phenology (LSP) results may not well capture ground-based phenological changes. This is likely the result of the mixed pixel effect: (1) a pixel in VI products may contain an unknown composition of vegetation species or land cover types; and (2) these species differ in their sensitivity to climatic variations. The mixed pixel effect has induced inconsistent findings in LSP with in situ observations of spring phenology. To this end, this study has designed a series of simulation experiments to initiate the methodological exploration of how the green-up date (GUD) of a mixed pixel could be altered by the endmember GUDs and different non-GUD variables, including the endmember composition, minimum and maximum normalized difference vegetation index (NDVI), and the length of the growth period. The study has also compared the sensitivity of two generally adopted GUD identification methods, the relative threshold method and the curvature method (also known as the inflection-point method). The simulations with two endmembers show that even if there is no change in the endmember GUDs, the GUD of the mixed pixel could be substantially altered by the changes in non-GUD variables. In addition, the study has also developed a simulation toolkit for the GUD identification with cases of three or more endmembers. The results of the study provide insights into effective strategies for analyzing spring phenology using VI products: the mixed pixel effect can be alleviated by selecting pixels that are relatively stable in the land cover or species composition. This simulation study calls for in situ phenological observations to validate the LSP, such as conducting climate-controlled experiments on few mixed species at a small spatial scale. The paper also argues for the necessity of isolating GUD trends caused by non-phenological changes in the study of spring phenology.



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Remote Sensing of Environment



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