中国水稻科学
     Home | About Journal | Editorial Board | Publication Ethics Statement | Subscriptions | Advertisement | Contacts Us | Chinese
  2010, Vol. 24 Issue (5): 516-522     DOI: 10.3969/j.issn.1001-7216.2010.05.012
研究报告 Current Issue | Next Issue | Archive | Adv Search  |   
County Level Rice Yield Estimation Based on Combination of Terra and Aqua MODIS EVIs
PENG Dai-liang1,2; HUANG Jing-feng1,3,4,*; SUN Hua-sheng5; WANG Fu-min6
1Institute of Agricultural Remote Sensing & Information Application, Zhejiang University, Hangzhou 310029, China; 2Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100190, China; 3Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang Province, Zhejiang University, Hangzhou 310029, China; 4 Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310029,China;5College of Surveying and Mapping, Xuzhou Normal University, Xuzhou 221116, China; 6College of Architectural and Civil Engineering, Zhejiang University, Hangzhou 310058, China; *Corresponding author, Email: hjf@zju.edu.cn
 Download: PDF (2426 KB)   HTML (1 KB)   Export: BibTeX | EndNote (RIS)      Supporting Info
Abstract Based on the comparison of enhanced vegetation indices(EVIs) from moderate resolution imaging spectroradiometer(MODIS), linear, quadratic nonlinear and stepwise regression models were constructed with the 16day and 250m resolution vegetative indices of Terra and Aqua(MOD13Q1, MYD13Q1) combined EVIs multiply by township rice planting area and total rice grain yield in Liling City, Hunan Province, China. The optimal fitting models were selected by error analysis, and then the total rice yield in the next year was forecasted. More than 50% absolute values of errors between Terra and Aqua MODIS EVIs were less than 0.03, and 93.22% and 99.50% of them were less than 0.08 and 0.10, respectively. The relative errors of total rice grain yield for all optimal fitting models were less than 0.10%, although forecasting errors were larger than that of fitting, the relative errors of forecasting total rice grain yield were less than 5%.
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
PENG Dai-liang
HUANG Jing-feng
SUN Hua-sheng
WANG Fu-min
Key wordsmoderate resolution imaging spectroradiometer   enhanced vegetation index   rice   yield estimation   remote sensing      
Received: 1900-01-01;
Cite this article:   
PENG Dai-liang,HUANG Jing-feng,SUN Hua-sheng et al. County Level Rice Yield Estimation Based on Combination of Terra and Aqua MODIS EVIs [J]. , 2010, 24(5): 516-522 .
 
[1] 黄敬峰. 基于GIS的大面积水稻遥感估产方法研究——以浙江省为例[D]. 杭州: 浙江大学, 1999: 1-206.
[2] Doraiswamy P C, Cook P W. Spring wheat yield assessment using NOAA AVHRR data. Can J Remote Sens, 1995, 21: 43-51.
[3] Hayes M J, Decker W L. Using NOAA AVHRR data to estimate maize production in the US combelt. Int J Remote Sens, 1996, 17: 3189-3200.
[4] Hayes M J, Decker W L. Using satellite and real-time weather data to predict maize production. Int J Biometeorol, 1998, 42 (1): 10-15.
[5] 王乃斌. 中国小麦遥感动态监测与估产. 北京: 中国科学技术出版社, 1996.
[6] Idso S B, Jackson R D, Reginato R J. Remote sensing for agricultural water management and crop yield prediction. Agr Water Manage, 1977, 1(4): 299-310.
[7] Liu H Q, Huete A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans Geosci Remote, 1995, 33: 457-465.
[8] Huete A R, Liu H Q, Batchily K. A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sens Environ, 1997, 59: 440-451.
[9] Kaufman Y J, Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans Geosci Remote, 1992, 30: 261-270.
[10] Huete A R. A soil-adjusted vegetation index (SAVI). Remote Sens Environ, 1988, 25: 295-309.
[11] Huete A R, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ, 2002, 83: 195-213.
Copyright © Chinese Journal of Rice Science 浙ICP备05004719号-5
Supported by: Beijing Magtech