Plant phenology is a key parameter for accurately modeling ecosystem dynamics.Limited by scarce ground observations and benefiting from the rapid growth of satellite-based Earth observations,satellite data have been w...Plant phenology is a key parameter for accurately modeling ecosystem dynamics.Limited by scarce ground observations and benefiting from the rapid growth of satellite-based Earth observations,satellite data have been widely used for broad-scale phenology studies.Commonly used reflectance vegetation indices represent the emergence and senescence of photosynthetic structures(leaves),but not necessarily that of photosynthetic activities.Leveraging data of the recently emerging solar-induced chlorophyll fluorescence(SIF)that is directly related to photosynthesis,and the traditional MODIS Normalized Difference Vegetation Index(NDVI),we investigated the similarities and differences on the start and end of the growing season(SOS and EOS,respectively)of the Tibetan Plateau.We found similar spatiotemporal patterns in SIF-based SOS(SOS_(SIF))and NDVI-based SOS(SOS_(NDV)I).These spatial patterns were mainly driven by temperature in the east and by precipitation in the west.Yet the two satellite products produced different spatial patterns in EOS,likely due to their different climate dependencies.Our work demonstrates the value of big Earth data for discovering broad-scale spatiotemporal patterns,especially on regions with scarce field data.This study provides insights into extending the definition of phenology and fosters a deeper understanding of ecosystem dynamics from big data.展开更多
基金This study was supported by the National Natural Science Foundation of China(41861134036,41988101)by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0405)。
文摘Plant phenology is a key parameter for accurately modeling ecosystem dynamics.Limited by scarce ground observations and benefiting from the rapid growth of satellite-based Earth observations,satellite data have been widely used for broad-scale phenology studies.Commonly used reflectance vegetation indices represent the emergence and senescence of photosynthetic structures(leaves),but not necessarily that of photosynthetic activities.Leveraging data of the recently emerging solar-induced chlorophyll fluorescence(SIF)that is directly related to photosynthesis,and the traditional MODIS Normalized Difference Vegetation Index(NDVI),we investigated the similarities and differences on the start and end of the growing season(SOS and EOS,respectively)of the Tibetan Plateau.We found similar spatiotemporal patterns in SIF-based SOS(SOS_(SIF))and NDVI-based SOS(SOS_(NDV)I).These spatial patterns were mainly driven by temperature in the east and by precipitation in the west.Yet the two satellite products produced different spatial patterns in EOS,likely due to their different climate dependencies.Our work demonstrates the value of big Earth data for discovering broad-scale spatiotemporal patterns,especially on regions with scarce field data.This study provides insights into extending the definition of phenology and fosters a deeper understanding of ecosystem dynamics from big data.
