Phenological characteristics of temperate coniferous and broad-leaved mixed forests based on multiple remote sensing vegetation indices, chlorophyll fluorescence and CO_2 flux data
Vegetation phenology research has gained increasing attention because it is the best indicator of terrestrial ecosystem responses to climate change,and their consequences for ecosystem functioning. Forests play an important role in the terrestrial carbon cycle and maintain global climate stability. With the development of remote sensing technology, multiple remote sensing indices are applied to the study of forest vegetation phenology,among which Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from Moderate-Resolution Imaging Spectroradiometer(MODIS) are the most widely used. With the launch of Greenhouse Gases Observing Satellite(GOSAT) , Global Ozone Monitoring Experiment 2 (GOME-2) and Orbiting Carbon Observatory 2 (OCO-2) satellite,chlorophyll fluorescence(SIF) as a probe for photosynthesis of vegetation has been widely applied for studying the vegetation phenology in the global scope. In this study,we have analyzed the phenological characteristics of Pinus koraiensis and broad-leaved mixed forests in Changbaishan flux station from 2007~ 2013,using a double logistic function fitting and dynamic threshold method. Thereafter,the phenological characteristics,parameters,and time series curves of the three types of data were analyzed and compared. In addition,the validity of the results was confirmed by daily gross primary production(GPP) from 2007 to 2010. We found that time series of NDVI exhibits an earlier start of growth season(SOS) date and a late end of growth season(EOS) date than that of EVI and SIF,and that the shape of the curve of growing season is too flat and broad to reflect the seasonal variations accurately owing to the saturation effect. The time series of EVI had more pronounced seasonal characteristics,which was more consistent with GPP than that of NDVI,although the former showed a slightly later decline. SIF had the closest correlation with GPP and the best ability to track the seasonal cycle of photosynthesis and reflect the seasonal changes in forest growth. The close relationship of SIF data with photosynthesis indicated that SIF is more likely to play a better role in vegetation phenology monitoring than vegetation index. Moreover,the phenomenon of rapid decline in SIF around summer solstice,mentioned by many phenological studies,is consistent with the findings of the present study. Therefore,we have discussed the causes of this phenomenon from several aspects and provided a more reasonable explanation. Comparison of the three remote sensing indices in the present study suggested that SIF reflects seasonal variation in forest vegetation phenology better. With the increase in the number of remote sensing platforms and the improvement of inversion methods,SIF will play a more important role in multi-scale and multi-type vegetation phenology research.