Please wait a minute...
img

官方微信

遥感技术与应用  2019, Vol. 34 Issue (5): 983-991    DOI: 10.11873/j.issn.1004-0323.2019.5.0983
模型与反演     
Landsat8卫星影像大气校正及其植被指数与SEVI性能比较
曹小杰1,2,4(),江洪1(),张兆明2,何国金2,赵晶晶3
1. 福州大学数字中国研究院(福建),福建 福州 350108
2. 中国科学院遥感与数字地球研究所,北京 100094
3. 国家信息中心,北京 100045
4. 航天恒星科技有限公司,北京 100091
Atmospheric Correction of Landsat-8 Satellite Image and Its Vegetation Index Comparison with SEVI
Xiaojie Cao1,2,4(),Hong Jiang1(),Zhaoming Zhang2,Guojin He2,Jingjing Zhao3
1. Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100094, China
3. National Information Center, Beijing 100045, China
4. Space Star Technology Co, Ltd, Beijing 100091, China
 全文: PDF(3994 KB)   HTML
摘要:

遥感影像受大气的吸收散射以及地形起伏变化的影响,使得传感器接收到的辐射信号既包含了地物的信息,同时也包含了大气以及地形的信息。为了提高地表反射率的反演精度,需要去除遥感影像中大气和地形的影响。提出了一种基于查找表的Landsat8-OLI遥感影像的大气校正方法,该方法由6S辐射传输模型生成查找表,其中输入的参数包括大气水蒸汽含量、臭氧浓度和气溶胶光学厚度等MODIS大气参数产品。利用传统方法建立的大气参数查找表通常只考虑一部分因素,这对于以MODIS产品为输入参数的大气校正是不适用的。本文建立了一个包括大部分输入参数的高维大气校正查找表,对于Landsat-8 OLI传感器具有很高的通用性,通过进行光谱分析、与USGS地表反射率产品交叉验证等方式来验证模型的精度。验证结果表明该方法能有效地反演精确可靠的地表反射率。最后,采用目视解译、统计分析将校正结果与SEVI做对比分析,比较地形影响消减的效果。结果表明该模型与SEVI在地形消减的效果上作用相当。

关键词: 查找表Landsat8遥感影像6S辐射传输模型地表反射率SEVI    
Abstract:

remote sensing images are affected by the atmospheric absorption, scattering and topographic changes, so that the radiation signals received by the sensors contain both the information of the ground features and the information of the atmosphere and terrain.In order to improve the retrieval accuracy of land surface reflectance, remote sensing images need to be pretreated.In this paper, a method of atmospheric correction for Landsat8 remote sensing images based on Look-Up Table(LUT) is proposed.The method generates LUT by the 6S radiative transfer model, in which the input parameters include water vapor content, ozone concentration, and aerosol optical depth (AOT) retrieved from the MODIS atmospheric two stage product.The atmospheric parameter table established by traditional method usually considers only a few factors, which is not applicable to atmospheric correction using MODIS product as input parameter.Therefore, this paper established a five dimensional LUT most of the input parameters, with high generality for Landsat-8 OLI sensor, and spectral analysis, to verify the the accuracy of the model of USGS surface reflectance products. The correlation (R2) between model-based NDVI and USGS-based NDVI was as high as 0.802 6.The verification results show that the method can effectively accurate inversion of surface reflectance products. It is also found that the calculated NDVI based on 6S radiation transmission model is more in line with the spectral characteristics of typical vegetation than the NDVI based on apparent reflectance. Finally, using visual interpretation, statistical analysis and Shadow-eliminated Vegetation Index(SEVI) correction results will do comparative analysis, compare the terrain subtractive effect. The results show that the 6S radiative transfer model and SEVI have little difference in the effect of terrain attenuation.

Key words: Look-up table    Landsat8 remote sensing images    6S radiative transfer model    Surface albedo product    TAVI
收稿日期: 2018-03-24 出版日期: 2019-12-05
ZTFLH:  TP79  
基金资助: 福建省自然科学基金项目(2017J01658);国家重点研发计划项目“全球多时空尺度遥感动态监测与模拟预测”(2016YFB0501502)
通讯作者: 江洪     E-mail: caoxj@spacestar.com.cn;jh910@fzu.edu.cn
作者简介: 曹小杰(1990-),男,山东淄博人,硕士研究生,主要从事遥感信息处理与研究。E?mail:caoxj@spacestar.com.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
曹小杰
江洪
张兆明
何国金
赵晶晶

引用本文:

曹小杰,江洪,张兆明,何国金,赵晶晶. Landsat8卫星影像大气校正及其植被指数与SEVI性能比较[J]. 遥感技术与应用, 2019, 34(5): 983-991.

Xiaojie Cao,Hong Jiang,Zhaoming Zhang,Guojin He,Jingjing Zhao. Atmospheric Correction of Landsat-8 Satellite Image and Its Vegetation Index Comparison with SEVI. Remote Sensing Technology and Application, 2019, 34(5): 983-991.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.5.0983        http://www.rsta.ac.cn/CN/Y2019/V34/I5/983

图1  八达岭国家森林公园研究区Landsat 8 OLI影像(6、5、4波段组合)
图2  Landsat 8 OLI影像大气校正流程图
图3  影响因素敏感性分析图
因素节点总数
太阳天顶角0、5、10、15、20、25、30、35、40、45、50、55、60、65、7015
水蒸汽0、0.5、1、1.5、2、2.5、3、3.5、4、4.5、511
臭氧0、0.1、0.2、0.25、0.3、0.35、0.4、0.45、0.5、110
气溶胶光学厚度0.01、0.05、0.1、0.15、0.2、0.3、0.4、0.5、0.6、0.7、0.8、0.9、1、1.2、1.4、1.6、1.8、218
目标高度/km0、0.5、1、1.5、2、2.5、3、3.5、4、5、6、7、813
表1  查找表的维度和节点数
图4  原始影像与地表反射率影像(波段组合654)
Fig.5  NDVI profile curve of the samples图5样本的NDVI曲线图
图6  本文模型的各波段地表反射率与USGS各波段地表反射率之间的相关性(注:1~5行自左至右分别对应Band1-Band7,横坐标为USGS-NDVI,纵坐标为本文模型的NDVI,原始NDVI值乘以10 000)
图7  研究区植被指数图像((a)~(c)依次为表观反射率得到的SEVI、NDVI和RVI, (d)~(e)为本文模型得到的NDVI和RVI,(f)~(g)为USGS产品的NDVI和RVI)
数据类型植被指数mbr

表观反射率

数据

RVI1.766 872.243 980.313 471
NDVI0.184 3430.399 7440.256 224
SEVI-0.005 353 706.008 36-0.000 687 643
本文模型校正数据NDVI-0.008 325 880.652 586-0.010 168 0
RVI0.137 0645.217 690.012 850 8

USGS地表

反射率

NDVI-0.092 382 70.767 504-0.125 575
RVI-2.194 538.078 82-0.177 217
表2  cosi 植被指数线性分析结果
图8  研究区植被指数-cosi散点图
1 Zhong Q. A Method for Determing Surface Albedo over the Tibetan Plateau from AVHRR Data[J]. Plateau Meteorology, 1985, 4(3):193-203.
2 Wang J K, Wang K C, Wang P C.A Three-Dimensional Model to Calculate Surface Reflectance over Urban Areas[J]. Chinese Journal of Atmospheric Sciences, 2008, 32(5):1119-1127.
3 Markham B L, Helder D L. Forty-year Calibrated Record of Earth-reflected Radiance from Landsat: A Review[J]. Remote Sensing of Environment, 2012, 122(Complete):30-40.
4 Zagolski F, Gastellu-Etchegorry J P. Atmospheric Corrections of AVIRIS Images with a Procedure based on the Inversion of the 5S Model[J]. International Journal of Remote Sensing, 1995, 16(16):3115-3146.
5 Vermote E F, Tanré Didier, Deuze J L, et al. Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An Overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3):675-686.
6 Liang S, Fang H, Chen M. Atmospheric Correction of Landsat ETM+ Land Surface Imagery. I. Methods[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11):2490-2498.
7 Lu D, Mausel P, Brondizio E, et al. Assessment of Atmospheric Correction Methods for Landsat TM Data Applicable to Amazon Basin LBA Research[J]. International Journal of Remote Sensing, 2002, 23(13): 21.doi: 10.1080101431160 110109642
doi: 10.1080101431160 110109642
8 Soudani K, Fran Ois C, Maire G L, et al. Comparative Analysis of IKONOS, SPOT, and ETM+ Data for Leaf Area Index Estimation in Temperate Coniferous and Deciduous Forest Stands[J]. Remote Sensing of Environment, 2006, 102(1-2):161-175.
9 Zhang Z M, He G J, Wang X Q. A Practical DOS Model-based Atmospheric Correction Algorithm[J]. International Journal of Remote Sensing, 2010, 31(11):2837-2852.
10 Hall F G, Strebel D E, Nickeson J E, et al. Radiometric Rectification: Toward Common Radiometric Response among Multidate, Multisensor Images[J]. Remote Sensing of Environment, 1991, 35(1):11-27.
11 Coppin P, Bauer M E. Processing of Multitemporal Landsat TM Imagery to Optimize Extraction of Forest Cover Change Features[J]. Geoscience & Remote Sensing IEEE Transactions on, 1994, 32(4):918-927.
12 Tanre D, Deuze J, Herman M, et al. Second Simulation of the Satellite Signal in the Solar Spectrum - 6S Code[C]∥Proceedings of IEEE Geoscience and Remote Sensing Symposium, 1990.
13 Wang Y, Liu L, Hu Y, et al. Development and Validation of the Landsat-8 Surface Reflectance Products Using a MODIS-based Per-pixel Atmospheric Correction Method[J]. International Journal of Remote Sensing, 2016, 37(6):1291-1314.
14 Peng Y, He G, Zhang Z, et al. Study on Atmospheric Correction Approach of Landsat 8 Imageries based on 6S Model and Look-up Table[J]. Journal of Applied Remote Sensing, 2016, 10(4):045006.
15 Jiang H, Wang S, Cao X, et al. A Shadow- eliminated Vegetation Index (SEVI) for Removal of Self and Cast Shadow Effects on Vegetation in Rugged Terrains[J]. International Journal of Digital Earth, 2019,12(9):1013-1029. DOI: 10.1080/17538947. 2018.1495770.
doi: 10.1080/17538947. 2018.1495770
16 Jiang Hong, Wang Xiaoqin, Wu Bo, et al. A Topography-adjusted Vegetation Index(TAVI) and Its Application in Vegetation Fraction Monitoring[J]. Journal of Fuzhou University (Natural Science Edition), 2010,38(4):527-532.江洪, 汪小钦, 吴波, 等. 地形调节植被指数构建及在植被覆盖度遥感监测中的应用[J]. 福州大学学报(自然科学版), 2010,38(4):527-532.
17 Jiang H, Wu B, Wang X. Developing a Novel Topography- adjusted Vegetation Index (TAVI) for Rugged Area[C]∥ Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS2010), 2010: 2075–2078.
18 Zhang Zhaoming, He Guojin, Liu Dingsheng, et al. An Improved Physical Model to Correct Topographic Effects in Remotely Sensed Imagery[J]. Spectroscopy and Spectral Analysis, 2010, 30(7):1839-1842.张兆明, 何国金, 刘定生, 等. 一种改进的遥感影像地形校正物理模型[J]. 光谱学与光谱分析, 2010, 30(7):1839-1842.
[1] 张鹏,刘勇. MOD09A1数据产品中缺失条带的插补方法[J]. 遥感技术与应用, 2015, 30(2): 331-336.
[2] 王婧,刘毅,张华,蔡兆男,杨东旭. LM以及CIA效应对TanSat 3个波段吸收光谱的影响[J]. 遥感技术与应用, 2014, 29(5): 771-781.
[3] 曹广真,漆成莉,马 刚,张凤英,吴雪宝. FY-3A气象卫星VIRR云检测产品与IRAS的匹配[J]. 遥感技术与应用, 2008, 23(1): 89-92.
[4] 闵祥军, 朱永豪, 朱振海, 田庆久. MAIS图像大气订正及其在岩矿制图中的应用[J]. 遥感技术与应用, 1999, 14(2): 1-9.