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RSS FeedsRemote Sensing, Vol. 12, Pages 671: Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing (Remote Sensing)

 
 

18 february 2020 15:03:09

 
Remote Sensing, Vol. 12, Pages 671: Phenological Characteristics of Global Ecosystems Based on Optical, Fluorescence, and Microwave Remote Sensing (Remote Sensing)
 


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.


 
178 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 12, Pages 665: Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Geostatistical Techniques and Multiple Regression (Remote Sensing)
Remote Sensing, Vol. 12, Pages 670: Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager--A Case Study (Remote Sensing)
 
 
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