Phenology is a valuable attribute of vegetation to assess the biological impacts from climate change.A challenge of phenological research is to obtain information on both high temporal resolution and fine spatial scal...Phenology is a valuable attribute of vegetation to assess the biological impacts from climate change.A challenge of phenological research is to obtain information on both high temporal resolution and fine spatial scale observations.Here,we constructed an air temperature map based on temporal merging and spatial interpolation algorithms to overcome the cloud-related problem from the MODIS LST product.Then,we derived the accumulated growing degree days(AGDD)from the constructed mean air temperature map to use as a meteorological indicator.Further,we verified the indicator with the seasonal mean air temperature and the green-up date of a Quercus mongolica forest determined from the field-based measurements.The AGDD threshold for each Q.mongolica forest when the first leaf has unfolded was detected from the EXG trajectory extracted from digital camera images.A comparison between meteorological and MODIS-derived AGDD showed good agreement between them.There was also high consistency between DoYs extracted from AGDD and EVI based on curvature K for Q.mongolica forests of 30 sampling sites throughout South Korea.The results prove that microclimatic factors such as elevation,waterbody,and land-use intensity were faithfully reflected in the reconstructed images.Therefore,the results of this study could be applied effectively in areas where microclimatic variation is very severe and for monitoring phenology of undergrowth,which is difficult to detect from reflectance imaging.展开更多
文摘Phenology is a valuable attribute of vegetation to assess the biological impacts from climate change.A challenge of phenological research is to obtain information on both high temporal resolution and fine spatial scale observations.Here,we constructed an air temperature map based on temporal merging and spatial interpolation algorithms to overcome the cloud-related problem from the MODIS LST product.Then,we derived the accumulated growing degree days(AGDD)from the constructed mean air temperature map to use as a meteorological indicator.Further,we verified the indicator with the seasonal mean air temperature and the green-up date of a Quercus mongolica forest determined from the field-based measurements.The AGDD threshold for each Q.mongolica forest when the first leaf has unfolded was detected from the EXG trajectory extracted from digital camera images.A comparison between meteorological and MODIS-derived AGDD showed good agreement between them.There was also high consistency between DoYs extracted from AGDD and EVI based on curvature K for Q.mongolica forests of 30 sampling sites throughout South Korea.The results prove that microclimatic factors such as elevation,waterbody,and land-use intensity were faithfully reflected in the reconstructed images.Therefore,the results of this study could be applied effectively in areas where microclimatic variation is very severe and for monitoring phenology of undergrowth,which is difficult to detect from reflectance imaging.
