Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2003, Vol. 15 Issue (1) : 46-50     DOI: 10.6046/gtzyyg.2003.01.12
Technology and Methodology |
AN ANALYSIS OF THE QUALITY AND APPLICATION OF THE SZ-3 CMODIS DATA
CHEN Jin-song1, TIAN Qin-jiuSHAO Yun2, ZHU Bo-qin1, HUI Fen-ming1, WANG Wei-min2
1. Institute of Remote Sensing Applications, Chinease Academy of Science, Beijing 100101, China;
2. International Institute for Earth System Science, Nanjing Unvirsity, Nanjing 210093, China
Download: PDF(2628 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  Obtained from the first Chinese medium-resolution imaging spectrometer in SZ-3 spacecraft, the data of CMODIS, with wavelength ranging from 0.4 μm to 12.5μm, contain abundant spectral information. In order to extract useful information from the data and develop their wide applications in many fields, it is necessary to evaluate their quality and analyze the prospects of their application. This paper makes a preliminary evaluation of their spectral quality by using corresponding ground data and analyzes their application in monitoring dynamic change of land cover, water quality and water pollution. The results indicate that the quality of CMODISdata is up to the standard of application, and hence CMODISdata have good application prospects in the survey of national land resources, the monitoring of water pollution, the estimation of crop yield, and the monitoring of natural disasters.
Keywords Soil moisture      Retrieve by remote sensing      ATI      TVI      Shiyang river basin     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
WEI Wei
REN Hao-Chen
ZHAO Jun
WANG Xu-Feng
Cite this article:   
WEI Wei,REN Hao-Chen,ZHAO Jun, et al. AN ANALYSIS OF THE QUALITY AND APPLICATION OF THE SZ-3 CMODIS DATA[J]. REMOTE SENSING FOR LAND & RESOURCES, 2003, 15(1): 46-50.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2003.01.12     OR     https://www.gtzyyg.com/EN/Y2003/V15/I1/46


[1] 田庆久,郑兰芬,童庆禧.基于遥感影像的大气辐射校正和反射率反演方法[J].应用气象学报,1998,9(4):456-461.





[2] 田庆久,张良培,郑兰芬,等.成像光谱遥感定标模型的分析与评价[J].遥感技术与应用,1996,11(3):16-21.





[3] 王晋年,郑兰芬,童庆禧.成像光谱图像吸收鉴别模型与矿物填图研究[J].环境遥感,1996,11(1):20-30.





[4] 李小文.地物的二向性反射和方向谱特征[J].环境遥感. 1989,4(1):67-72.





[5] 陈述彭,童庆禧,郭华东.高光谱分辨率遥感信息机理与地物识别

[A] .见:遥感信息机理研究[M]. 北京:科学出版社,1988,139-231.





[6] 陈俊,宫鹏.实用地理信息系统[M]. 北京:科学出版社,1998.





[7] 邵晖,王建军,薛永祺.推帚式超光谱成像仪关键技术[J]. 遥感学报,1998,2(4):251-254.





[8] 浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社,2000.





[9] 张良培,郑兰芬,童庆禧.利用高光谱对生物变量进行估计[J]. 遥感学报,1997,1(2):111-114.





[10] 郑兰芬,王晋年.成像光谱遥感技术及其图像光谱信息提取分析研究[J]. 环境遥感,1992,7(1):49-58.





[11] 宫鹏. 遥感生态测量学进展[J].自然资源学报,1999,14(4):313-317.





[12] 宫鹏,史培军,浦瑞良,等.对地观测技术与地球系统科学[M]. 北京:科学出版社,1996.





[13] 浦瑞良,宫鹏.森林生物化学与CASI高光谱分辨率遥感数据的相关分析[J].遥感学报,1997,1(2):115-123.





[14] 刘玉洁,杨忠东,等.MODIS遥感信息处理原理与算法[M].北京:科学出版社,2001.





[15] Adams JB, Smith M O, Johnson PE. Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander 1 site[J].J Geophys Res, 1986, 91: 8098-8112.





[16] Al-Abbas, et al. Spectra of normal and nutrient-deficient maize leaves[J].Agron J, 1974, 66:16-20.





[17] Analytical Spectral Devices (ASD)Inc[M]. Technical Guide. 3rd Ed, USA, 1999.





[18] ANCAL Inc. C-SPEC Data Acquisition and Manipulation Program: Users Guide & Operating Instructions

[S] .Version 1995, 1.5.





[19] Asner GP.Biophysical and biochemical sources of variability in canopy reflectance[J].Remote Sens. Environ, 1998, 64:234-253.





[20] Astar G, Greenstone R(editors). MTPE EOS Reference Handbook[J].1995, 1-277.
[1] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3] SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
[4] WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[5] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[6] QIN Dahui, YANG Ling, CHEN Lunchao, DUAN Yunfei, JIA Hongliang, LI Zhenpei, MA Jianqin. A study on the characteristics and model of drought in Xinjiang based on multi-source data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 151-157.
[7] BO Yingjie, ZENG Yelong, LI Guoqing, CAO Xingwen, YAO Qingxiu. Impacts of floating solar parks on spatial pattern of land surface temperature[J]. Remote Sensing for Natural Resources, 2022, 34(1): 158-168.
[8] BU Ziqiang, BAI Linbo, ZHANG Jiayu. Spatio-temporal evolution of Ningxia urban agglomeration along the Yellow River based on nighttime light remote sensing[J]. Remote Sensing for Natural Resources, 2022, 34(1): 169-176.
[9] YANG Wang, HE Yi, ZHANG Lifeng, WANG Wenhui, CHEN Youdong, CHEN Yi. InSAR monitoring of 3D surface deformation in Jinchuan mining area, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 177-188.
[10] SUN Yiming, ZHANG Baogang, WU Qizhong, LIU Aobo, GAO Chao, NIU Jing, HE Ping. Application of domestic low-cost micro-satellite images in urban bare land identification[J]. Remote Sensing for Natural Resources, 2022, 34(1): 189-197.
[11] SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
[12] HU Yingying, DAI Shengpei, LUO Hongxia, LI Hailiang, LI Maofen, ZHENG Qian, YU Xuan, LI Ning. Spatio-temporal change characteristics of rubber forest phenology in Hainan Island during 2001—2015[J]. Remote Sensing for Natural Resources, 2022, 34(1): 210-217.
[13] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[14] YAO Jinxi, ZHANG Zhi, ZHANG Kun. An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 249-256.
[15] LI Dong, TANG Cheng, ZOU Tao, HOU Xiyong. Detection and assessment of the physical state of offshore artificial reefs[J]. Remote Sensing for Natural Resources, 2022, 34(1): 27-33.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech