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一种利用MODIS数据的夏玉米物候期监测方法
引用本文:李颖,陈怀亮,李耀辉,王秀萍,张方敏.一种利用MODIS数据的夏玉米物候期监测方法[J].应用气象学报,2018,29(1):111-119.
作者姓名:李颖  陈怀亮  李耀辉  王秀萍  张方敏
作者单位:1.中国气象局·河南省农业气象保障与应用技术重点开放实验室, 郑州 450003
基金项目:中国气象局·河南省农业气象保障与应用技术重点开放实验室开放基金(AMF201507,AMF201608,AMF201708),NSFC-河南人才培养联合基金(U1304405),风云三号(02)批气象卫星地面应用系统工程应用示范系统项目(FY-3(02)-UDS-1.8.2)
摘    要:采用MODIS数据重构夏玉米归一化植被指数生长曲线,提取并建立特征点位对应日期与作物进入不同物候期的实际日期之间的最佳匹配关系。研究表明:使用改进的SG(Savitzky-Golay)迭代滤波对最大值合成后的植被指数时间序列做平滑处理并进行Logistic曲线拟合,可得到时间分辨率为1 d的作物生长过程曲线,经与2013-2014年物候期实测数据匹配,选择利用动态阈值1提取七叶期,均方根误差为5.4 d;利用曲率最小值提取拔节期,均方根误差为6.4 d;利用动态阈值2提取抽雄期,均方根误差为6.0 d。经2015年物候期实测数据验证,3个关键物候期的遥感监测误差均在6 d以内。利用该方法可提高基于遥感数据开展大面积作物物候期监测识别的效率和准确率。

关 键 词:归一化植被指数    作物生长曲线    SG滤波    Logistic拟合
收稿时间:2017/5/24 0:00:00
修稿时间:2017/9/26 0:00:00

A Method for Summer Maize Phenology Monitoring by MODIS Data
Li Ying,Chen Huailiang,Li Yaohui,Wang Xiuping and Zhang Fangmin.A Method for Summer Maize Phenology Monitoring by MODIS Data[J].Quarterly Journal of Applied Meteorology,2018,29(1):111-119.
Authors:Li Ying  Chen Huailiang  Li Yaohui  Wang Xiuping and Zhang Fangmin
Affiliation:1.Henan Key Laboratory of Agrometeorological Support and Applied Technique, CMA, Zhengzhou 4500032.Henan Institute of Meteorological Sciences, Zhengzhou 4500033.Henan Meteorological Administration, Zhengzhou 4500034.High-tech Industrial Development Zone, Chengdu 6100165.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information & Technology, Nanjing 210044
Abstract:Crop phenology period is an important feature of the agricultural eco-system. It is important to investigate the crop phenology period in large area for precision crop management and yield forecast by remote sensing technical. However, there are still some limitations in this approach, such as the investigation precision is restricted by the investigation area, and different types of crop growth curves don''t match with the crop phenology period very well.Therefore, data from moderate-resolution imaging spectroradiometer (MODIS), and summer maize growth stages by field observations from 23 agricultural meteorological stations in Henan Province are adopted to improve the identification efficiency and accuracy. Normalized difference vegetation index (NDVI) growth curve with the time resolution of 1 d is reconstructed by denoising, smooth processing and logistic curve fitting. Crop growth feature points on the reconstructed growth curve are extracted by using dynamic threshold method and curvature extremum method. The optimum matching relationship between feature points and maize growth stages is constructed by based on the feature points, their occurrence date, and observed dates of growth stages. By matching with the investigated growth stage dates in 2013-2014, values of dynamic threshold 1 is chosen to extract the 7-leaf stage, with the root mean square error (RMSE) of 5.4 d. Values of minimum curvature is chosen to extract the jointing stage, with the RMSE of 6.4 d. Values of dynamic threshold 2 is chosen to extract the tasseling stage, with the RMSE of 6.0 d. By validation with the investigated growth stage dates in 2015, RMSE with the selected 3 key growth stages are all less than 6 d. The accuracy is higher than the earlier proposed methods to extract maize growth stages by using MODIS or other similar lower/medium spatial resolution remote sensing data. Map of summer maize critical phenology in Henan Province demonstrates that most of summer maize in the research areas enter 7-leaf stage, jointing stage and tasseling stage in June 25 to 27 June,10 July to 17 July and 27 July to 2 August, respectively. Numbers of pixels, which enter 7-leaf stage, jointing stage and tasseling stage on the above-mentioned dates, account for 45.6%, 66.8% and 71% of the total, respectively. Maize growth stages obtained by the method proposed by this research can be used in crop management and grain yield forecast.
Keywords:normalized difference vegetation index  crop growth curve  SG filter  Logistic fitting
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