| 基于SPOT/VEGETATION时间序列的冬小麦物候提取方法 |
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| 引用本文： | 鹿琳琳,郭华东.基于SPOT/VEGETATION时间序列的冬小麦物候提取方法[J].农业工程学报,2009,25(6):174-179. |
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| 作者姓名： | 鹿琳琳 郭华东 |
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| 作者单位： | 1. 遥感科学国家重点实验室,中国科学院遥感应用研究所,北京,100101
2. 中国科学院对地观测与数字地球科学中心,北京,100190 |
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| 基金项目： | 国家重点基础研究发展计划项目（2009CB723904） |
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| 摘 要： | 农作物物候信息的获取十分重要。近年来，开展了大量利用时序遥感数据提取植被物候的研究，但这些研究中提出的方法主要应用于森林或草地等植被类型。由于冬小麦的光谱指数时间序列有着独特的特性，利用这些方法不能获取较好的冬小麦物候提取结果。研究提出了一种新的物候提取方法，能够从SPOT/VEGETATION NDVI S10产品时间序列中成功的提取出冬小麦的返青期等详细的物候信息。研究选取一个位于山东省济宁市的典型的冬小麦种植区对该方法进行验证。结果表明，这种方法能够有效的消除遥感数据中的噪音对提取结果造成的影响，可以达到较以往的物候提取方法更满意的拟合效果和更符合实际的物候提取结果。
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| 关 键 词： | 遥感，VEGETATION，时间序列，冬小麦，物候 |
| 收稿时间： | 2008/2/25 0:00:00 |
| 修稿时间： | 2009/4/24 0:00:00 |
Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data |
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| Lu Linlin and Guo Huadong.Extraction method of winter wheat phenology from time series of SPOT/VEGETATION data[J].Transactions of the Chinese Society of Agricultural Engineering,2009,25(6):174-179. |
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| Authors: | Lu Linlin and Guo Huadong |
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| Institution: | 1.State Key Laboratory of Remote Sensing Science;Institute of Remote Sensing Applications;Chinese Academy of Sciences;Beijing 100101;China;2.Center for Earth Observation and Digital Earth;Beijing 100190;China |
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| Abstract: | The extraction of crop phenology is very important. In recent years, there are many researches on phenology derivation from multi-temporal remote sensing data. But most of them focus on vegetation types like forests and grasslands. Since the temporal characteristics of winter wheat spectral index is different from above biomes, satisfactory phenology extraction results cannot be achieved using these methods. A novel curve fitting method is presented in this research. Using this method, detailed winter wheat phenology can be extracted from time series of SPOT/VEGETATION S10 product successfully. It was applied to a typical wheat site in Jining, Shandong Province. The results indicate that this method is robust to the noise in remote sensing dataset. Compared with other methods, time series of remote sensing data can be fitted to this model effectively, and realistic phenology results are detected. |
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| Keywords: | remote sensing VEGETATION time series winter wheat phenology |
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