Plant Phenology From Remote Sensing And Mechanism Of Ecosystem Carbon Cycling

Global climate change has become the focus of the international society in recent years,it not only for the ecosystem that basis of human survival caused irreversible change but also has a far-reaching impact on all aspects of society,such as melting glaciers,extreme climate,species reduced and food production.In order to improve the impact of climate change on human society,governments have taken a series of related measures.Compared with anthropogenic emission reduction,forest ecosystem,as the main body of terrestrial ecosystem,plays an significant role in the process of vegetation carbon balance.Vegetation phenology is the study of periodic changes of plant growth(e.g.,bud break,flowering and leaf-off)and how these changes are related to international variability of climate,as well as it plays a significant role in regulating carbon sequestration period of terrestrial ecosystems.Remote sensing offers the best way and data sampling from a regional perspective,and thus supports the modeling of phenology from local to global scales.it is positive significance to further understand the surface carbon cycle on vegetation phenology that inversion of phenology using remote sensing data.In addition,to grasp the vegetation carbon exchange is sensitive to various meteorological factors is an important step in understanding the impact of climate change on the ecosystem carbon cycle.Using CO2 flux data at 15 forest sites in North America,the Moderate Resolution Imaging Spectroradiometer(MODIS)data,PhenoCam observation,and meteorological data,based on analyzing the progress of remote sensing phenology in recent years,we proposed a new model to estimate phenology based entirely on the MODIS data,and discusses the carbon exchange component in response to the meteorological factors on different season.The main innovations are as follows:Firstly,we here present a review of remote sensing phenology since this new century and we discuss the data source,modeling algorithm,various validation and its complexity when predicting phenology using remote sensing.Secondly,remote sensing of land surface phenology(LSP),i.e.,the start and the end of the growing season(SOS and EOS,respectively)in evergreen needleleaf forests is particularly challenging due to their limited seasonal variability in canopy greenness.Using 107 site-years of CO2 flux data at 14 evergreen needleleaf forest sites in North America,we developed a new model to estimate SOS and EOS based entirely on the Moderate Resolution Imaging Spectroradiometer(MODIS)data.We found that the commonly used vegetation indices(VI),including the normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI),were not able to detect SOS and EOS in these forests.The MODIS land surface temperature(LST)showed better performance in the estimation of SOS than did a single VI.Interestingly,the variability of LST(i.e.,the coefficient of variation,CV_LST)was more useful than LST itself in detecting changes in forest LSP.Therefore,a new model using the product of VI and CV_LST was developed and it significantly improved the representation of LSP withmean errors of 11.7 and 5.6 days for SOS and EOS,respectively.Further validation at five sites in the Long Term Ecological Research network(LTER)using camera data also indicated the applicability of the new approach.These results suggest that temperature variability plays a previously overlooked role in phenological modeling,and a combination of canopy greenness and temperature could be a useful way to enhance the estimation of evergreen needleleaf forest phenology of future ecosystem models.Finally,using carbon flux observation data and meteorological data of three flux site at the same latitude.We do analysis of how components of the carbon exchange(gross primary productivity: GPP,net ecosystem productivity: NEP ecosystem respiration: Re)in different seasons be impacted by meteorological data(photosynthetically active radiation: PAR,air temperature: TA,soil temperature: TS,relative humidity: RH,precipitation: P).The results showed that PAR,TA and TS were the main controlling factor of carbon exchange in each season,but RH and P had no significant effect on it.In addition,compare with the middle of the growing season,the vegetation in the changing period of growing season(spring and autumn)is more sensitive to the meteorological factors.For the carbon exchange component,the difference of NEP response to meteorological factors is most significant in different vegetation types,and higher summer temperature will make evergreen forest respiration rate faster than photosynthesis,so that the quarter NEP decline and increased with the temperature decreasing,which shows that in the background of global climate warming,not only the temperature will control the NEP by affecting the length of growth season,summer temperatures will also affect the annual process of evergreen forest carbon accumulation.