MODIS EVI时间序列数据和光谱角聚类的冬小麦遥感估产分区方法研究

Division of Winter Wheat Yield Estimation by Remote Sensing Based on MODIS EVI Time Series Data and Spectral Angle Clustering

农作物遥感估产区划是农作物遥感估产的基础,它为估产研究和实践提供了重要的科学依据。以冬小麦生育期内的MODIS EVI时间序列作为分区数据,选择江苏省为试验区,探讨了一种改进的光谱角制图和K均值聚类相结合(光谱角聚类)的分区方法,并在冬小麦遥感估产中进行了试验。结果表明:光谱角聚类分区方法充分利用了MODIS时间序列数据所反映的农作物生长进程,可以充分体现气候差异所带来的冬小麦区域差异;与传统分区相比,基于光谱角聚类分区方法所得到的遥感估产结果具有较高的决定系数R2(0.702 6比0.624 8)和较低的均方根误差RMSE(343.34比381.34kg.hm-2),体现了该分区方法在冬小麦遥感估产中的优势。光谱角聚类分区方法仅以获取便利的低分辨率时间序列遥感数据为分区数据,且能很好的将冬小麦划分为特征性质一致的区域,所得遥感估产模型的精度和稳定性也较好,为冬小麦遥感估产分区提供了一种有效方法,有利于进行冬小麦遥感估产研究。

Crop yield estimation division is the basis of crop yield estimation; it provides an important scientific basis for estima- tion research and practice. In the paper, MODIS EVI time-series data during winter wheat growth period is selected as the divi- sion data; JiangSu province is study area; A division method combined of advanced spectral angle mapping（SVM） and K-means clustering is presented, and tested in winter wheat yield estimation by remote sensing. The results show that： division method of spectral angle clustering can take full advantage of crop growth process that is reflected by MODIS time series data, and can fully reflect region differences of winter wheat that is brought by climate difference. Compared with the traditional division method, yield estimation result based on division result of spectral angle clustering has higher R2 （0. 702 6 than 0. 624 8） and lower RMSE （343.34 than 381.34 kg · hm 2 ）, reflecting the advantages of the new division method in the winter wheat yield estimation. The division method in the paper only use convenient obtaining time-series remote sensing data of tow-resolution as division data, can divide winter wheat into similar and well characterized region, accuracy and stability of yield estimation model is also very good, which provides an efficient way for winter wheat estimation by remote sensing, and is conducive to winter wheat yield estima- tion.