Hyperspectral Inversion Of Vegetation Biochemical Components Based On Machine Learning And Radiative Transfer Model

Plant leaves are an important part of the plant body,which contains many biochemical components such as chlorophyll,carotenoids,water,lignin and cellulose,which contain a variety of information.Reasonable estimation of the content of vegetation biochemical parameters is of great significance to the development of rural areas,to the balance of local ecosystems,and to local ecological security.In addition,machine learning algorithms can well explain the implicit and potentially nonlinear functional relationship between plant biochemical parameters and spectral reflectance,which makes machine learning algorithms more suitable for inversion of vegetation leaves in combination with radiative transfer models.and canopy biochemical components.The research contents and conclusions of this paper are as follows:(1)At the leaf scale,in order to improve the inversion accuracy of leaf biochemical parameters of vegetation,the sensitivity analysis of broad-leaf radiative transfer model(PROSPECT)was carried out by using the extended Fourier amplitude sensitivity test,and the corresponding sensitive bands of chlorophyll,carotenoid,water and dry matter were extracted.Among them,the extraction bands of chlorophyll are 531nm-737nm,carotenoid is 400nm530nm,water is 1312-2500nm,and dry matter is 400nm-2500nm.Secondly,on the basis of the standard Whale Optimization Algorithm(WOA),the improved chaotic sequence is introduced to initialize the population and produce uniformly distributed individuals.Then,nonlinear convergence factor and weight are introduced to improve the parameters of whale optimization algorithm,and the improved parameters are used to update individual positions,making the algorithm easier to traverse the whole world to find the optimal solution.Then,the fitness values are sorted at the completion of an iteration,and elite individuals with high fitness are selected.Gaussian perturbation is performed on elite individuals through perturbation probability to increase the probability of the algorithm jumping out of the local optimal.Improved Whale Optimization Algorithm(IWOA)is formed.Finally,the inversion accuracy of the improved whale algorithm is compared with that of the common particle swarm optimization algorithm.The results show that the accuracy of chlorophyll,carotenoid and equivalent water thickness inversion by improved whale algorithm is higher than that by particle swarm optimization algorithm.Specifically,the R2 of chlorophyll,carotenoid and equivalent water thickness retrieved by the improved whale algorithm(particle swarm optimization)were 0.969(0.175),0.917(0.013)and 0.925(0.738),respectively.The RMSE of chlorophyll,carotenoid and equivalent water thickness were 6.899(18.022),0.384(27.065)and 0.013(0.004),respectively.In general,compared with PSO standard particle swarm optimization algorithm,the improved whale optimization algorithm and band inversion model have higher prediction accuracy for biochemical parameters.(2)At canopy scale,chlorophyll content in the canopy was taken as the research object.In order to improve the accuracy of crop canopy chlorophyll content inversion,the canopy solar radiation brightness and the corresponding leaf chlorophyll content and leaf area index(LAI)of winter wheat in the study area were measured based on the 2017 winter wheat experimental plot in Langfang city.By processing the measured radiation brightness data,the canopy reflectance data of the study area was obtained.In addition,the 3FLD fluorescence inversion algorithm was used to obtain the canopy scale sun-induced chlorophyll fluorescence data as the data source of the model.2-0 is calculated by using the fractional order differentiation order fractional order spectrum of step length is 0.1,through correlation analysis and projection algorithm combined with continuous hyperspectral feature extraction method,first using the method of correlation analysis of the characteristics of canopy chlorophyll content and high spectral analysis between parameters,select canopy chlorophyll content significantly related top 50 characteristic bands.Secondly,in order to reduce the mutual correlation between the bands,and considering the impact of the structural complexity of the neural network model on the inversion accuracy,the 50 selected characteristic bands were processed by continuous projection algorithm to eliminate the redundant information in the spectral matrix,and finally the 1.1-order 739nm was extracted.1.6 order 740-nm;Four bands of 791nm and 766nm in order 1.9,which are closely related to canopy chlorophyll content,were used as input features of the model.In addition,the Back Propagation Network(BP)has some problems,such as slow convergence speed and easy to fall into local minimum.Therefore,Differential evolution of Grey Wolf optimization algorithm(DE-GWO)is used to optimize the weights and thresholds of BP neural network iteratively.The optimized neural network model was used to predict canopy chlorophyll content.The influence of introduced fluorescence data on inversion results is compared.The results show that the BP neural network model optimized by the differential evolution gray Wolf algorithm improves the inversion accuracy of the data set with fluorescence parameters compared with the data set without fluorescence parameters.The R2 and RMSE of the two datasets were 0.925 and 10.134 and 16.833,respectively.