Analysis Of Vegetation Phenological Differences In The Mongolian Plateau Based On SIF,NDVI And NIRv

Vegetation phenology is an important index to characterize plant growth processes,which can respond sensitively to changes in the surrounding environment and can reveal potential links between vegetation growth and development and influencing factors.Regional-scale vegetation phenology is generally extracted using remote sensing data,and with the development of remote sensing data,newer vegetation indices have been developed,which provide new ideas for the study of vegetation phenology.However,there are differences in vegetation phenology parameters estimated based on data from different products,especially in the response characteristics to climatic factors.Therefore,in order to understand the differences in vegetation phenological parameters extracted from different data and their responses to influencing factors,this study estimated vegetation phenological parameters the start of growing season（SOS）,end of growing season（EOS）and length of growing season（LOS）based on sunlight-induced chlorophyll fluorescence（SIF）,normalized difference vegetation index（NDVI）and near-infrared reflectance index（NIRv）of vegetation.Trends and change points of the phenological parameters were analyzed based on these data.Further,the correlations between the three vegetation phenology parameters and snow-related factors,precipitation,temperature,soil moisture and population density were analyzed and their differences were compared.The main results of this study are as follows:（1）Differences in vegetation phenological parameters:SOS_NDVI is the earliest,SOS_NIRv is the second,and SOS_SIF is the latest among the three phenology parameters,with mean values of 97.4th day,103.6th day,and132.3th day,respectively;EOS_SIF is the earliest,EOS_NIRv is the second,and EOS_NDVI is the latest,with mean values of 274th day,297th day,and303.9th day,respectively;LOS_SIF was the shortest,followed by LOS_NIRv,and LOS_NDVI was the longest,with mean values of 112.3 days,195.6 days,and 198.6 days,respectively.（2）Differences in spatiotemporal variation characteristics of vegetation phenological parameters:the trend of SOSSIF had higher significance,with 10.29%of pixels passing the significance test（p<0.05）;the trend of EOS_NDVI had more significant pixels,of which 13.71%passed the significance test（p<0.05）.In addition:the effect of altitude:SOSSIF significantly increased,SOS_NDVI and SOS_NIRv significantly decreased;EOS_SIF significantly decreased,EOS_NDVI slowly decreased and EOS_NIRvsignificantly increased;LOS_SIF significantly shortened,LOS_NDVI slowly increased and LOS_NIRv significantly lengthened.The change points of SOS_SIF were mainly concentrated in the change points of SOS_SIF were mainly concentrated in 2001 to 2003,while the change points of SOS_NDVIand SOS_NIRv appeared after the above years.the change points of EOS_SIFand EOS_NIRv were mainly concentrated in 2001 to 2007,while the change points of EOS_SIF appeared later.Unlike the weak correlation of SOS_SIF,SOS_NDVI and SOS_NIRv were significantly and negatively correlated with winter snow cover and snowmelt period.（3）Differences in response of vegetation phenological parameters to various influencing factors:among the meteorological factors,recharge of soil moisture in spring made the highest contribution to the advance of SOS.The correlations between meteorological factors and EOS_SIF were most significant in summer and autumn.From the perspective of spatial differentiation,each influence factor explained the highest SOS_NDVI,with snowmelt period and winter snow cover being the highest explained influence factors.Population density explained the highest degree of SOS_NIRv and the lowest degree of SOS_SIF.Each influence factor explained the highest degree of EOS_SIF,with spring and autumn temperatures being the highest explained influences.Human activities explained the highest degree of EOS_NIRv and the lowest degree of EOS_NDVI.The results of this study reveal the differences and potential of different remote sensing data in estimating vegetation phenological parameters,which will help better understand the dynamic changes of vegetation phenology and the response to changes in various influencing factors,and provide some data and empirical support for the high-precision extraction of high vegetation phenological index in the Mongolian Plateau in the future.