Remote Sensing · 2020 · 39 citations · 57 references
Natural SciencesEarth SciencesEcologyClimatologyPlant EcologyRemote SensingLand Cover Change DetectionRemote Sensing Change DetectionSeasonal Growth PatternsVegetation Change DetectionVegetation IndicesVegetation MonitoringPhenological CharacterizationPhenological Vegetation IndicesPhenologyRemote Sensing Of PhenologySatellite PhenologySatellite-based PhenotypingVegetation Cover Change DetectionVegetation Phenology Monitoring
Growing seasons of vegetation generally start earlier and last longer due to anthropogenic warming. To facilitate the detection and monitoring of these phenological changes, we developed a discrete, hierarchical set of global “phenoregions” using self-organizing maps and three satellite-based vegetation indices representing multiple aspects of vegetation structure and function, including the normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and vegetation optical depth (VOD). Here, we describe the distribution and phenological characteristics of these phenoregions, including their mean temperature and precipitation, differences among the three satellite indices, the number of annual growth cycles within each phenoregion and index, and recent changes in the land area of each phenoregion. We found that the phenoregions “self-organized” along two primary dimensions: degree of seasonality and peak productivity. The three satellite-based indices each appeared to provide unique information on land surface phenology, with SIF and VOD improving the ability to detect distinct annual and subannual growth cycles in some regions. Over the nine-year study period (limited in length by the short satellite SIF record), there was generally a decrease in the spatial extent of the highest productivity phenoregions, though whether due to climate or land use change remains unclear.
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