本文選題：PROSAIL模型 + 草地　； 參考：《青海師范大學(xué)》2014年碩士論文

【摘要】：草地是陸地生態(tài)系統(tǒng)的重要組成部分，是地球表面的天然屏障，提供了畜牧業(yè)的原料，，對(duì)人類的生存和發(fā)展有著不可估量的作用。葉面積指數(shù)作為指示作物生長(zhǎng)狀況的生物物理參數(shù),與植被各種生物物理過(guò)程有著密切的聯(lián)系。因此準(zhǔn)確地獲取草地的葉面積指數(shù)對(duì)青海湖流域牧草產(chǎn)量估算具有重要意義。 本文選取青海湖流域?yàn)檠芯繀^(qū),選用MODIS影像和Landsat-8影像，結(jié)合實(shí)地采集葉面積指數(shù)數(shù)據(jù)、實(shí)測(cè)光譜數(shù)據(jù),利用基于輻射傳輸模型的PROSAIL模型對(duì)草地葉面積指數(shù)進(jìn)行遙感反演研究。 論文主要研究包括以下幾個(gè)方面： 1、數(shù)據(jù)的獲取與預(yù)處理：包括樣地實(shí)測(cè)光譜數(shù)據(jù)、葉面積指數(shù)數(shù)據(jù)、葉綠素濃度數(shù)據(jù)的采集和整理；遙感影像的大氣校正。 2、 PROSAIL模型模擬分析：結(jié)合實(shí)測(cè)光譜數(shù)據(jù)將葉片反射率轉(zhuǎn)化為冠層反射率，并對(duì)PROSAIL模型在青海湖流域草地的適用性進(jìn)行了分析； 3、PROSAIL模型參數(shù)敏感性分析：根據(jù)實(shí)測(cè)數(shù)據(jù)分析了PROSAIL模型輸入?yún)?shù)的敏感性。并依據(jù)模型敏感度計(jì)算公式定確定模型參數(shù)的敏感度。 4、查找表的建立：將敏感參數(shù)按照一定的步長(zhǎng)進(jìn)行取值，得到葉片不同情況下的冠層反射率，建立LAI與冠層反射率的查找表； 5、葉面積指數(shù)LAI反演：將進(jìn)行過(guò)大氣校正過(guò)的遙感影像像元反射率按照代價(jià)函數(shù)與查找表進(jìn)行匹配查找，得到相應(yīng)的冠層葉面積指數(shù)LAI，然后用青海湖流域樣地實(shí)測(cè)數(shù)據(jù)對(duì)反演結(jié)果進(jìn)行驗(yàn)證。 通過(guò)研究,得到以下結(jié)論： 1、PROSPECT模型在反演草地葉面積指數(shù)方面有著較好的適用性:PROSPECT模型反演的的草地葉片反射率與實(shí)測(cè)葉片反射率絕對(duì)偏差小于0.015。 2、 PROSAIL模型輸入?yún)?shù)敏感度由高到低為L(zhǎng)AICabCmSLNCw，確定LAI和Cab兩個(gè)最敏感的參數(shù)用于建立草地的LAI-冠層反射率查找表，對(duì)應(yīng)選擇Landsat-8影像4、5、6波段參與LAI反演；MODIS影像選擇1、2波段進(jìn)行反演。 3、反演LAI結(jié)果與實(shí)測(cè)的LAI具有很好的一致性，Landsat-8影像兩者的相關(guān)系數(shù)R2=0.855，均方根誤差RMSE=0.63；MODIS兩者的相關(guān)系數(shù)R2=0.809，均方根誤差RMSE=0.86。
[Abstract]:Grassland is an important part of terrestrial ecosystem and a natural barrier on the surface of the earth. It provides raw materials for animal husbandry and plays an inestimable role in the survival and development of human beings. As a biophysical parameter indicating crop growth, leaf area index is closely related to various biophysical processes of vegetation. Therefore, it is of great significance to obtain the leaf area index of grassland accurately for the estimation of forage yield in Qinghai Lake basin. In this paper, the Qinghai Lake Basin is selected as the study area, MODIS image and Landsat-8 image are selected, combined with the field data of leaf area index and the measured spectral data, the remote sensing inversion of grassland leaf area index is carried out by using PROSAIL model based on radiative transfer model. This paper mainly includes the following aspects: 1. The acquisition and pretreatment of data: including the collection and collation of the measured spectral data, leaf area index data and chlorophyll concentration data; Atmospheric correction of remote sensing image. 2. Simulation and analysis of Prosail model: the leaf reflectivity was transformed into canopy reflectance based on measured spectral data, and the applicability of PROSAIL model in Qinghai Lake basin grassland was analyzed. 3 sensitivity analysis of PROSAIL model parameters: the sensitivity of input parameters of PROSAIL model is analyzed based on the measured data. According to the model sensitivity formula, the sensitivity of the model parameters is determined. 4. The establishment of the lookup table: the sensitive parameters are calculated according to a certain step size, and the canopy reflectivity of the leaves under different conditions is obtained. Building the look-up table of Lai and canopy reflectivity. 5. Lai inversion of leaf area index: matching pixel reflectivity of atmospheric corrected remote sensing image according to cost function and lookup table. The corresponding canopy leaf area index (Lai) was obtained, and the inversion results were verified by the measured data of Qinghai Lake basin. Through research, The conclusions are as follows: 1 the project model has good applicability in retrieving the grassland leaf area index. The absolute deviation between the measured leaf reflectance and the grassland leaf reflectance obtained by the 0. 10% project model is less than 0. 015.2, and that of the project is less than 0. 015.2, and the absolute deviation of the measured leaf reflectance is less than 0. 015.2. The model input parameter sensitivity is from high to low to LAICabCmSLNCw. the two most sensitive parameters Lai and Cab are determined to establish the LAI- canopy reflectance lookup table of grassland. Landsat-8 images are selected for inversion in 4 and 5 ~ 6 bands. 3. The inversion results are in good agreement with the measured Lai. The correlation coefficients of Landsat-8 images and Landsat-8 images are 0.855, RMSE 0.63 and RMSE = 0.63 respectively. The correlation coefficient between MODIS and MODIS is 0.809, and the root mean square error (RMSE) is 0.86.
【學(xué)位授予單位】：青海師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：S812;TP79

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7 周篤s

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