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作物物候信号能够反映温度和降水等变化对植被生长的影响,是进行农作物动态分析和田间管理的重要依据。基于2008年EOS-MODIS多时相卫星遥感数据,研究了我国东北地区水稻的主要物候期的识别方法。首先提取研究区24个农业气象观测站所在位置的MODIS-EVI(Enhanced Vegetation Index,增强型植被指数)指数的时间序列;同时利用小波滤波消除时间序列上的噪音,小波滤波选用函数包含Daubechies(7—20),Coiflet(3—5)和Symlet(7—15)共26种类型。然后根据水稻移栽期、抽穗期和成熟期在EVI时间序列上的表现特征来识别水稻主要物候期。最后与东北地区24个站点水稻物候观测资料对比并分析误差。结果表明,Symlet11小波滤波的效果最好,其移栽期识别结果的误差绝大部分在±16 d,抽穗期和成熟期识别结果的误差在±8 d。表明通过此方法可以较好地识别东北水稻主要物候期,并可进一步应用到整个东北地区水稻的物候空间分布和时间变化特征研究上。
Crop phenology signals can reflect the effects of temperature and precipitation on the vegetation growth, which is an important basis for the dynamic analysis and field management of crops. Based on the 2008 EOS-MODIS multi-temporal satellite remote sensing data, the major phenological periods of rice in northeastern China were studied. Firstly, the time series of MODIS-EVI (Enhanced Vegetation Index) index of 24 agrometeorological observatories located in the study area was extracted. At the same time, the time series noise was eliminated by wavelet filtering. The selected function of wavelet filtering included Daubechies (7 -20), Coiflet (3-5) and Symlet (7-15) a total of 26 types. Then, the main phenophase of rice was identified according to the characteristics of EVI time series during transplanting, heading and maturity of rice. Finally, we compared the observed data of rice phenology at 24 stations in Northeast China and analyzed the errors. The results show that the Symlet11 wavelet filter has the best effect, the error of the identification results at transplanting stage is mostly ± 16 d, and the error of identification results at heading and maturity is ± 8 d. It is indicated that this method can identify the main phenophase of rice in Northeast China and can be further applied to the study of phenological distribution and temporal variation of rice in the entire Northeast China.