Estimation Of Species Abundance And Leaf Area Index Retrieval Of Grassland Canopy Based On Hyperspectral Data

Leaf area index(LAI),the most basic parameter describing the vegetation canopy geometry,is also an important input parameter for models such as climate model and ground-air interaction.Steppe,one of the most widely distributed types of vegetation in the world,plays an important role in the global carbon cycle.Therefore,accurately carrying out large-scale steppe LAI remote sensing inversion is of great significance.However,the steppe canopy has high species richness and complex structure,and individual species show strong parameter differences at a leaf scale.Meanwhile,there are large differences in growth between species themselves and different species on an individual scale,which has great effects on the complexity of spectral composition at canopy scale,and then leads to strong uncertainty for remote sensing inversion of LAI.Hyperspectral data,with its rich spectral information,can obtain a large amount of parameter information of vegetation canopy,which provides a possibility for canopy species identification and abundance estimation.Utilizing the hyperspectral to construct a species end-spectrum library,based on Hyperion satellite hyperspectral data,the canopy species abundance estimation is carried out to realize the LAI inversion of the canopy species abundance,which has important application value.Based on the hyperspectral data of Hyperion satellite,hyperspectral data of drone and ground-synchronized steppe observation data in Xilin Gol Prairie of Inner Mongolia,a method based on the hyperspectral data of UAV was proposed to extract the endmember spectrum of grassland species based on vegetation index classification via analyzing the spectral response characteristics of steppe canopy species composition and its abundance.Furthermore,for the abundance of steppe species,different mixed pixel decomposition models were estimated,and the results of steppe species abundance estimation in the study area were obtained.Therefore,the LAI inversion of steppe canopy species abundance was realized,and the LAI distribution of steppe in the study area was obtained.The main conclusions of this study were as follows:(1)The spectral response characteristics of steppe canopy species composition and its abundance were analyzed.Based on the PROSPECT and PROSAIL models,the spectral response characteristics of composition and abundance by steppe canopy species were analyzed from leaf to canopy scale.On the leaf scale,the effects of leaf structure,chlorophyll,dry matter,carotenoids,moisture content and other parameters on the spectral characteristics of the leaves and the sensitive wavelength range were analyzed by using the PROSPECT model.Furthermore,the simulated spectra of different species were obtained by substituting the measured parameters of each species into the PROSPECT mode.Using the two parameters of Euclidean distance and spectral angular distance,the separability of the blade scale was evaluated from the two aspects of reflectance value and curve geometry,which indicated that the spectral characteristics of leaves by different species were significantly different though the difference degree was different.On the canopy scale,the canopy spectra of different species under different LAIs were modeled using the PROSAIL model.The results showed that the difference in the spectral characteristics of different spectral features increased with increasing of LAI,however,the degree of difference was different under different LAIs.The separability of LAI was poor in 0.5,the spectral characteristic variation amplitude was significantly enhanced in 0.5-4,and the variation range of spectral characteristics was obviously weakened in>4.The analysis of spectral characteristics by different species under the same LAI showed that with the increase of LAI,the spectral characteristics of each species generally increased.The discrimination degree was low in<0.5,the discrimination degree was significantly enhanced in 0.5-4,and the discrimination degree of different species had obvious weakening trend in>4.(2)An endmember extraction method for steppe species was proposed and an endmember spectral library was constructed.Based on the analysis of spectral response characteristics of steppe canopy species composition and its abundance,a method based on UAV hyperspectral data was proposed to extract the endmember spectral of grassland species based on vegetation index classification.Meanwhile,by using different endmember extraction algorithms,a set of candidate endmembers was constructed,and the endmember spectral library was optimized by combining the reference endmember spectrum of the terrestrial marker in the hyperspectral imaging process,and the integrated Euclidean distance and spectral angular distance.(3)The abundance of canopy species in the study area was estimated.Based on Hyperion satellite hyperspectral data,the full-constrained least squares(FCLS)and multi-terminal linear mixed spectral decomposition(MESMA)abundance estimation methods were used to estimate the canopy species abundance.In addition,the optimal estimation results of steppe canopy species abundance in the study area were determined via the accuracy evaluation.(4)LAI inversion taking into account the abundance of steppe canopy species.Based on the estimation of the abundance of steppe canopy species in the study area and the PROSAIL model,the range and step size were determined via the measured parameter values.Using the combination of spectral angle and Euclidean distance as the spectral matching algorithm,the LAI inversion study considering the abundance of grass canopy species and the accuracy evaluation and error analysis were carried out.There were some differences in the inversion precision of different species composition.The inversion result of Leymus chinensis+Cleistogenes squarrosa had the highest precision,and the average absolute error was 0.31.The error of L.chinensis+Potentilla chinensis was higher than other community types,and the average absolute error was 0.73.However,for the entire study area,the overall average absolute error of the steppe LAI inversion results was 0.43,achieving a high inversion accuracy.The main innovations of this paper include:(1)For the estimation of species abundance of complex turf canopy composed of multiple species,this paper proposes the method to extract the endemic spectrum of steppe species based on UAV hyperspectral data and vegetation index grading.Furthermore,different mixed pixel decomposition models were proposed for the estimation of steppe species abundance,and the estimation results of steppe species abundance were obtained,which had certain technical methods and innovation.(2)Based on the estimation of steppe canopy species abundance,the LAI remote sensing inversion of steppe canopy was carried out to optimize and constrain the model parameters,so as to improve the research idea of steppe LAI inversion precision composed of complex species,which had theoretical innovation.