Vegetation Parameter Inversion And Application Based On Ensemble Machine Learning

Vegetation is not only an important part of terrestrial ecosystem,but also plays an important role in regulating the dynamic balance of global carbon,maintaining global climate stability and studying global climate change.Vegetation parameters can not only represent the characteristics of vegetation from many aspects,but also can be classified to study and discuss vegetation from various angles.In the photosynthesis and respiration of vegetation,water is also a vital subject and participant.The change of vegetation water content not only directly reflects the basic biophysical processes of vegetation itself,but also has an inseparable relationship with the global water cycle and carbon cycle.NPP of vegetation regulates the global gas cycle The development and change of vegetation phenology will not only affect the size of plant biomass,but also affect the concentration of CO₂in the world atmosphere,which may have an important impact on global climate change and global carbon cycle.At present,the inversion models based on remote sensing technology can be divided into two categories:traditional empirical inversion model and physical inversion model:Although the traditional empirical inversion model is simple in structure,the result accuracy is limited;Although the physical inversion model has a solid theoretical foundation of science and technology,the input parameters of the model are complex and difficult to obtain and are not universal.In this paper,Sichuan Province is chosen as the research area,MODIS satellite remote sensing image data,some measured data and meteorological data as the main data sources.In the process of vegetation parameter inversion,the machine learning method model is introduced.Not only the model of vegetation parameter inversion based on machine learning is built,but also the response relationship between vegetation parameters to climate change is studied.The main contents and conclusions of this paper are as follows:(1)The inversion of FMC of vegetation fuel water contentIn this paper,the machine learning model is introduced in the study of FMC of vegetation fuel water content.Two methods are selected:the inversion method based on vegetation moisture index and four machine learning method models.The FMC of vegetation fuel water content is retrieved by using two kinds of methods,and the inversion accuracy of four machine learning methods is compared,and the best xbboost model is used to study the vegetation fuel content in Sichuan Province The inversion of FMC results of water volume is carried out and studied in time and space.(2)Inversion of NPP of vegetationThis paper studies the NPP selection method model of net primary productivity of vegetation:CASA model of light energy utilization rate.The NPP inversion is carried out in Sichuan Province by CASA model,and the data of remote sensing products are compared and analyzed.Meanwhile,the results of CASA model are analyzed in time and space.(3)Inversion of vegetation phenologyIn this paper,we introduce machine learning model to vegetation phenology,and select two methods:traditional threshold method and gbdt machine learning method.We use two methods to retrieve vegetation phenology of Sichuan Province in the same research area and compare the results.At the same time,the paper also makes a spatiotemporal analysis of the inversion results of gbdt machine learning method model.(4)Response of vegetation parameters to climate changeThis paper studies and analyzes the response relationship between the three vegetation parameters and climate change,and studies the response of FMC,NPP and phenology to the change of temperature and precipitation in Sichuan Province during 2011-2015.