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利用MODIS数据识别水稻关键生长发育期 被引量:25

Detecting major growth stages of paddy rice using MODIS data
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摘要 利用遥感方法提取中国范围内的水稻关键生长发育期。首先,对时间序列TerraMODIS-EVI(Enhanced Vegetation Index)进行傅里叶和小波低通滤波平滑处理,然后,根据水稻在移栽期、分蘖初期、抽穗期和成熟期的EVI变化特征,实现对各个生长发育期的识别。通过将利用2005年MODIS数据识别的结果与当年气象台站的地面观测资料进行比较,采用本研究中的识别方法得出的水稻各个生长发育期的绝对误差大部分小于16d,经过F检验表明提取的结果与地面观测资料在0.05水平下具有显著一致性。研究中的信息提取方法可被用于其他年份的水稻生长发育期识别,根据其他作物的生长发育特点,也可能适合于提取其他作物的生长发育期。 Phenological information of paddy rice is important for area extraction and growth monitoring. The purpose of this study is to detect the major phenological stages of paddy rice over China using remote sensing data. Time-series Terra moderate-resolution imaging spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) was smoothed with the low pass Fourier and wavelet filtering methods, then the stages of transplanting, beginning of tillering, heading, and maturation were obtained according to their characteristics. The paddy rice stages in 2005 derived from this study were significantly positive correlated and consistent with the statistical data (P 〈 0.05), and most of the absolute errors were less than 16 d. The methods presented in this study could be applied in other years, as well as the ability to generate the growth stages of other crops.
出处 《遥感学报》 EI CSCD 北大核心 2009年第6期1122-1137,共16页 NATIONAL REMOTE SENSING BULLETIN
基金 国家"863"课题(编号:2006AA120101) 国家自然科学基金项目(编号:40871158/D0106 40875070/D0509)~~
关键词 遥感 MODIS EVI 水稻 生长发育期 物候 remote sensing, MODIS, EVI, paddy rice, growth stage, phenology
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  • 1Chen J, Jonsson P, Tamura M, Gu Z, Matsushita B and Eklundh L. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 91:332--344.
  • 2Chen X, Tan Z J, Schwartz M D and Xu C. 2000. Determining the growing season of land vegetation on the basis of plant phenology and satellite data in Northern China. International Journal of Biometeorology, 44:97--101.
  • 3Chen X, Xu C and Tan Z. 2001. An analysis of relationships among plant community phenology and seasonal metrics of Normalized Difference Vegetation Index in the northern part of the monsoon region of China. International Journal of Biometeorology, 45(4): 170--177.
  • 4Cihlar J. 1996. Identification of contaminated pixels in AVHRR composite images for studies of land biosphere. Remote Sensing of Environment, 56:149--153.
  • 5Cooley J, Lewis P and Welch P. 1969. The finite Fourier transform. IEEE Trans. Audio Electroacoustics, 17(2): 77--85.
  • 6Defila C and Clot B. 2001. PhytophenoIogical trends in Switzerland. International Journal of Biometeorology, 45:203--207.
  • 7Duchemin B, Goubicr J and Courrier G. 1999. Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data. Remote Sensing of Environment, 67:68--82.
  • 8Fischer A. 1994. A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sensing of Environment, 48(2): 220--230.
  • 9Gallo K P and Flesch T K. 1989. Large-area crop monitoring with the NOAA AVHRR: Estimating the silking stage of corn development. Remote Sensing of Environment, 27:73--80.
  • 10Gillian L G, John F M, Jerry M, Aline G, Carlos C C and Carlos E P C. 2008. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil Remote Sensing of Environment, 112:576--587.

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