基于Landsat-8遥感数据和PROSAIL辐射传输模型反演叶面积指数

干旱区地理 ›› 2016, Vol. 39 ›› Issue (5): 1096-1103.

基于Landsat-8遥感数据和PROSAIL辐射传输模型反演叶面积指数

杜育璋¹, 姜小光¹, 张雨泽¹, 黄成¹, 刘朝霞², 刘亮^1,3

1 中国科学院大学, 北京 100049;
2 中国科学院新疆生态与地理研究所, 新疆乌鲁木齐 830011;
3 民政部国家减灾中心, 北京 100124

收稿日期:2016-04-01 修回日期:2016-07-26 出版日期:2016-09-25
通讯作者: 姜小光,男,博士生导师,教授.Email:xgjiang@ucas.ac.cn
作者简介:杜育璋,男,硕士研究生,主要从事植被参数遥感定量反演研究.Email:duyuzhang13@mails.ucas.ac.cn
基金资助:
国家重点基础研究计划（2013CB733402）；国家自然科学基金项目（41231170，41471297）

Retrieving leaf area index using PROSAIL radiative transfer model based on Landsat 8 image

DU Yu-zhang¹, JIANG Xiao-guang¹, ZHANG Yu-ze¹, HUANG Cheng¹, LIU Zhao-xia², LIU Liang^1,3

1 University of Chinese Academy of Sciences, Beijing 100049, China;
2 Xinjiang Institute of Ecology and Geography, Chines Academy of Sciences, Urumqi 830011, Xinjiang, China;
3 National Disaster Reduction Center of China, MCA, Beijing 100124, China

Received:2016-04-01 Revised:2016-07-26 Online:2016-09-25

摘要/Abstract

摘要： 为了探讨Landsat 8 OIL数据在LAI大范围反演方面的应用潜力，使用Landsat 8 OIL影像，通过PROSAIL辐射传输模型，采用3种波段组合（Band2-7，Band2-5，Band3-5）建立了3个模拟冠层反射率－叶面积指数（LAI）查找表，用2种代价函数（Geman and McClure代价函数，均方根误差代价函数）实现了对玉米、土豆、森林LAI的定量反演，并用LAI-2200测量数据作为相对真值对反演精度进行评价。结果表明：（1）使用Landsat 8数据，通过PROSAIL模型反演叶面积指数的精度是可以接受的，RMSE范围为在［0.892 4，1.205 0］，R²范围为［0.721 3，0.873 3］。（2）Band5 （近红外），Band4（红）Band3（绿）的波段组合反演效果在3种组合中精度最高，平均RMSE=0.993 1，R²=0.787 3。（3）Geman and McClure代价函数比常用的均方根误差代价函数得到了更高的反演精度，平均RMSE=0.940 5，R²=0.817 5。（4）相对最优的反演策略是Band5，Band4，Band3的波段组合结合GM代价函数，RMSE=0.892 4，R²=0.873 3。（5）存在玉米土豆的反演值普遍低于测量值，而森林的反演值普遍高于测量值的问题。

关键词: 叶面积指数, PROSAIL模型, Landsat 8, 查找表

Abstract: Landsat 8 is the latest satellite in the Landsat program launched on February 11,2013. Landsat 8's Operational Land Imager(OLI) improves on past Landsat sensors in radiation resolution and the scan mode. In order to discussing the potential of Landsat 8 OIL data for LAI inversion application,a leaf area index(LAI) and canopy reflectance lookup table(LUT) was established by using the PROSAIL radiative transfer model and Landsat 8 OIL image for the LAI inversion of corn,potatoes and forest. Firstly,the paper set PROSAIL model parameter values according to previous research to generate a lookup table. Secondly,the paper divided Landsat 8 OIL reflectance data into three groups based on wavelength respectively,they are 6-bands group(Blue,Green, Red,NIR,Swir1,Swir2),4-bands group(Blue,Green,Red,NIR) and 3-bands group(Green,Red,NIR). Green, red and NIR are often used in many studies,and the paper took two other bands of Landsat 8 OIL into consideration to assess their applicability. And then,the paper found a series of records with the smallest differences in the lookup table in the corresponding band by using RMSE and Geman and McClure function as a cost function (hereinafter referred to as RMSE cost function and GM cost function). RMSE cost function is widely used.However, GM cost function is more effective when analyzing the reflectance of NIR band and red band in vegetation region,because GM cost function can decline the negative impact when the absolute value of one parameter is significantly higher than other items. Finally,the paper regarded the corresponding LAI value of the record as the inversion result. The result demonstrated as follows:(1) The retrieval accuracy was acceptable,RMSE was in the range of 0.892 4 to 1.205 0,R² was in the range of 0.721 3 to 0.873 3;(2) The band combination of band5(near infrared),band4(red) and band3(green) got the highest accuracy among three band combinations(RMSE=0.993 1,R²=0.787 3);(3) GM cost function had higher accuracy than RMSE cost function(RMSE=0.940 5,R²=0.817 5);(4) Relatively optimal inversion strategy was Band5,Band4,Band3 combined with GM cost function (RMSE=0.892 4,R²=0.873 3);(5) There was a problem that the retrieved LAI of corn and potatoes were generally lower than the measured value,and the retrieved LAI of forest was generally higher than the measured value. During the study,the paper found there was a certain degree of uncertainty in retrieval when expanded the scope of certain parameter was. This phenomenon may be caused by the ill-posed inverse problem,so the researches in next step could take the correlations between the model free parameters into consideration.The paper provided a reference for Landsat 8 OIL LAI inversion in other regions by analyzing the relative optimal inversion strategy of PROSAIL model.

Key words: leaf area index (LAI), PROSAIL model, Landsat 8, look up table

中图分类号:

TP79

杜育璋, 姜小光, 张雨泽, 黄成, 刘朝霞, 刘亮. 基于Landsat-8遥感数据和PROSAIL辐射传输模型反演叶面积指数[J]. 干旱区地理, 2016, 39(5): 1096-1103.

DU Yu-zhang, JIANG Xiao-guang, ZHANG Yu-ze, HUANG Cheng, LIU Zhao-xia, LIU Liang. Retrieving leaf area index using PROSAIL radiative transfer model based on Landsat 8 image[J]. , 2016, 39(5): 1096-1103.

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