基于MODIS卫星遥感图像数据典型地表发射率光学特性分析

doi:10.7522/j.issn.1000-0240.2018.0084

冰川冻土 ›› 2018, Vol. 40 ›› Issue (4): 784-791.doi: 10.7522/j.issn.1000-0240.2018.0084

基于MODIS卫星遥感图像数据典型地表发射率光学特性分析

顾吉林, 刘淼, 汤宏山

辽宁师范大学物理与电子技术学院, 辽宁大连 116029

收稿日期:2018-04-23 修回日期:2018-07-15 出版日期:2018-08-25 发布日期:2018-10-08
作者简介:顾吉林(1981-),男,辽宁大连人,讲师,2012年在大连海事大学获博士学位,从事大气辐射传输特性研究与光电检测技术研究.E-mail:gujilin2003@163.com.
基金资助:
大连市高层次人才创新支持计划项目（2017RQ141）；国家自然科学基金项目（11547234）；辽宁省创新创业教育改革试点专业项目；辽宁省普通高等教育本科教学改革研究项目"虚拟结合应用型物理实验教学体系的构建与实践"资助

Analysis of the optical properties of typical surface emissivity based on the data of MODIS satellite telemetry

GU Jilin, LIU Miao, TANG Hongshan

School of Physics and Electronic Technology, Liaoning Normal University, Dalian 116029, China

Received:2018-04-23 Revised:2018-07-15 Online:2018-08-25 Published:2018-10-08

摘要/Abstract

摘要： 地表发射率是热红外遥感中的重要参量，也是辐射传输中的重要参数。基于MOD11B1卫星遥感图像数据，利用HDF插件获取典型地表温度参数，具体包括沙地、黄土、草坪、江水、冰面和雪地。在ENVI Classic软件环境下，针对2015年12月至2016年8月不同区域、不同季节的典型地表进行6个热红外波段发射率数据获取，研究不同季节典型地表的发射率随波长以及温度的变化规律。研究结果表明：冬季典型地表发射率参数最高且变化范围小在0.02内。沙地的发射率数值平均在0.870~0.990之间；草坪、黄土和江水的发射率数值平均在0.910~0.990之间，冰面和雪地的发射率数值平均在0.965~0.985之间。草坪、沙地、黄土、江水、雪地和冰面地表发射率在波长3~5 μm范围内随温度成波浪型分布；草坪、江水、雪地和冰面地表发射率在波长8~12 μm范围内随温度不变化。

关键词: 地表发射率, 地表温度, MODIS, 光学特性

Abstract: The emissivity is a physical quantity that represents the physical surface radiation ability and is an important parameter in radiation transmission and infrared remote sensing. The optical characteristics of surface emissivity have been studied by means of the variation of the emissivity rate of typical surface in different seasons with various wavelength and temperature. Here the typical surfaces include grass, sand, loess, river, snow and ice. By summarizing the relevant literatures and analyzing the measurement methods of emissivity, a method to obtain surface emissivity and surface temperature was proposed based on the data of MOD11B1. The emissivity rates of six thermal infrared bands in different regions and seasons were inverted under the ENVI Classic software environment from December 2015 through August 2016. The line charts of 11 340 indicator samples were drawn to analyze the variation trend of surface emissivity with wavelength. Using the HDF plug-in to read the typical surface temperature parameters, 1 890 data points of the surface temperature were obtained according to the missing data of the linear fitting supplement. The relationship between emissivity and temperature was analyzed by using Origin to draw the surface emissivity curves changing with temperature. The results showed that the typical surface emissivity parameter in winter is the maximum, with a changing range less than 0.02. The emissivity rate from grass, loess and river water is in between 0.910 and 0.990, and that of sand is in between 0.87 and 0.99. The emissivity of ice and snow is in between 0.965 and 0.985 in average. The change of surface emissivity of grass, sand, loess, river, surface of snow and ice with temperature was in a wavy form within the wave range of 3~5 μm. But the surface emissivity of grass, water, snow and ice surface do not change with temperature significantly in the wave length range of 8~12 μm.

Key words: surface emissivity, surface temperature, MODIS, optical property

中图分类号:

P407

顾吉林, 刘淼, 汤宏山. 基于MODIS卫星遥感图像数据典型地表发射率光学特性分析[J]. 冰川冻土, 2018, 40(4): 784-791.

GU Jilin, LIU Miao, TANG Hongshan. Analysis of the optical properties of typical surface emissivity based on the data of MODIS satellite telemetry[J]. JOURNAL OF GLACIOLOGY AND GEOCRYOLOGY, 2018, 40(4): 784-791.

参考文献

[1] Wu Ying, Weng Fuzhong. Effects of soil texture on the retrieved microwave emissivity at the different frequencies of a desert area and its modeling[J]. Acta Meteorologica Sinica, 2014, 72(4):749-759.[吴莹, 翁富忠. 沙漠地区土壤质地对不同频点微波地表发射率反演和模拟的影响[J]. 气象学报, 2014, 72(4):749-759.]
[2] Zhai Jun, Liu Jiyuan, Liu Ronggao, et al. Spatial-temporal patterns and important factors driving and surface emissivity in China, 2000-2011[J]. Resources Science, 2013, 35(10):2094-2103.[翟俊, 刘纪远, 刘荣高, 等. 2000-2011年中国地表比辐射率时空格局及影响因素分析[J]. 资源科学,2013, 35(10):2094-2103.]
[3] Gong Qiang, Wang Hongyu, Zhu Ling, et al. Characteristics and variations of the ground temperature field in Liaoning Province[J]. Journal of Glaciology and Geocryology, 2017, 39(3):505-514.[龚强, 汪宏宇, 朱玲, 等. 辽宁省地温场结构及变化特征[J]. 冰川冻土, 2017, 39(3):505-514.]
[4] Xu Hanqiu. Retrieval of the reflectance and land surface temperature of the newly-launched Landsat 8 satellite[J]. Chinese Journal of Geophysics, 2015, 58(3):741-747.[徐涵秋. 新型Landsat8卫星影像的反射率和地表温度反演[J]. 地球物理学报, 2015, 58(3):741-747.]
[5] Zou Defu, Zhao Lin, Wu Tonghua, et al. Assessing the applicability of MODIS land surface temperature products in continuous permafrost regions in the central Tibetan Plateau[J]. Journal of Glaciology and Geocryology, 2015, 37(2):308-317.[邹德富, 赵林, 吴通华, 等. MODIS地表温度产品在青藏高原连续多年冻土区的适用性分析[J]. 冰川冻土, 2015, 37(2):308-317.]
[6] Hu Shenshen. Comparative analysis of GLASS albedos over the Tibetan Plateau based on multi-source data[D]. Nanjing:Nanjing University of Information Science and Technology, 2016.[胡慎慎. 多源数据对比分析青藏高原GLASS地表反照率[D]. 南京:南京信息工程大学, 2016.]
[7] Li Shaoliang, Duan Sibo, Tang Bohui, et al. Review of methods for land surface temperature derived from thermal infrared remotely sensed data[J]. Journal of Remote Sensing, 2016, 20(5):899-920.[李召良, 段四波, 唐伯惠, 等. 热红外地表温度遥感反演方法研究进展[J]. 遥感学报, 2016, 20(5):899-920.]
[8] Yao Tong. A study on parametric feature of the surface roughness and Albedo in North China[D]. Lanzhou:Lanzhou University, 2014.[姚彤. 我国北方地区地表粗糙度和反照率参数化特征研究[D]. 兰州:兰州大学, 2014.]
[9] Zhang Renhua. Quantitative Model of thermal Infrared Remote Sensing and Ground Experiments[M]. Beijing:Science Press, 2009.[张仁华. 定量热红外遥感模型及地面实验基础[M]. 北京:科学出版社, 2009.]
[10] Zhang Feng, Yu Kun, Zhang Kaihua, et al. An emissivity measurement apparatus for near infrared spectrum[J]. Infrared Physics & Technology, 2015, 73:275-280.
[11] Zheng Zhiyuan, Wei Zhigang, Li Zhenchao, et al. A study of variation characteristics of surface broadband emissivity over three typical bare soil underlying surfaces innorthwestern China[J]. Chinese Journal of Atmospheric Sciences, 2016, 40(6):1227-1241.[郑志远, 韦志刚, 李振朝, 等. 中国西北三类典型裸土下垫面地表宽波段发射率变化特征研究[J]. 大气科学, 2016, 40(6):1227-1241.]
[12] Carlo P, Eduardo T, Marilena M, et al. Methodology for spectral emissivity measurement by means of single color pyrometer[J]. Measurement, 2016, 403-409.
[13] Li Huoqing, Wu Xinping, Ali Mamtimin, et al. Estimating the surface broadband emissivity of deserts in Xinjiang base on MODIS and FTIR data[J]. Journal of Desert Research, 2017, 37(3):523-529.[李火青, 吴新萍, 买买提艾力·买买提依明, 等. 基于FTIR和MODIS数据估算新疆沙漠宽波段地表比辐射率[J]. 中国沙漠, 2017, 37(3):523-529.]
[14] Hu Juyang, Tang Shihao, Dong Lixin, et al. Analysis of thermal infrared emissivity for sand dust source regions in northwest China[J]. Journal of Infrared and Millimeter Waves, 2013, 32(6):550-554.[胡菊旸, 唐世浩, 董立新, 等. 我国西北沙源区地表热红外发射率特征分析[J]. 红外与毫米波学报, 2013, 32(6):550-554.]
[15] Shi Yaya, Yang Chengsong, Che Tao. Accuracy verification of the Tibetan Plateau Permafrost Map based on MODIS LST product[J]. Journal of Glaciology and Geocryology, 2017, 39(1):70-78.[石亚亚, 杨成松, 车涛. MODIS LST产品青藏高原冻土图的精度验证[J]. 冰川冻土, 2017, 39(1):70-78.]
[16] Wang Xinsheng, Xu Jing, Liu Fei, et al. Spatial-temporal changes of land surface emissivity in China from 2001 to 2010[J]. Acta Geographica Sinica, 2012, 67(1):93-100.[王新生, 徐静, 柳菲, 等. 近10年我国地表比辐射率的时空变化[J]. 地理学报, 2012, 67(1):93-100.]
[17] Qiu Yubao, Zhang Huan, Chu Duo, et al. Cloud removing algorithm for the daily cloud free MODIS-based snow cover product over the Tibetan Plateau[J]. Journal of Glaciology and Geocryology, 2017, 39(3):515-526.[邱玉宝, 张欢, 除多, 等. 基于MODIS的青藏高原逐日无云积雪产品算法[J]. 冰川冻土, 2017, 39(3):515-526.]

基于MODIS卫星遥感图像数据典型地表发射率光学特性分析

Analysis of the optical properties of typical surface emissivity based on the data of MODIS satellite telemetry

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