一种基于图论重构MODIS EVI时间序列数据集的新方法

A New Method to Reconstruct MODIS EVI Time Series Data Set based on Graph Theory

MODIS的增强型植被指数(EVI)时间序列数据早已广泛应用于植被观测、生态环境和全球气象变化等研究领域,但即使EVI时间序列数据已经经过严格的预处理,其中仍然存在着一些噪声。因此,本文开发了一种简单有效的方法来重构EVI时间序列数据,减少EVI时间序列数据中的噪声,尤其是一些由大气云层和冰雪覆盖产生的噪声。新方法的理论来源于图论,利用拉普拉斯矩阵的关系对EVI中选定的邻域窗口的像元权重进行赋值,得到中心像元的拟合。新方法已应用于2016—2018年的MODIS MOD13A1产品,并与S-G滤波法、谐波函数法、双逻辑斯蒂拟合法和非对称高斯函数法进行了比较。结果表明,在荒漠、草原和林地中,新方法留一验证测试的绝对差值最小,相较于其他方法效果较优;在拟合不同植被类型的EVI时间序列数据时,图论邻点方法呈现出更好的细节拟合曲线;其在5类植被类型中的RMSE值分别为200.59、46.58、63.48、165.47和40.95,在5种方法中均为最小值,在获取高保真和高质量的EVI时间序列数据方面优势更明显有效。本文的方法研究可以给植被遥感时序数据的去噪和生态环境的研究提供有益借鉴。

The MODIS Enhanced Vegetation Index(EVI)time-series data has been widely used in many research fields such as vegetation observation,ecological environment,and global meteorological changes.However,even though the EVI time series data has undergone strict preprocessing,there are still some noises in it.Therefore,this paper develops a simple and effective method to reconstruct EVI time-series data and eliminate the noise in EVI time-series data,especially some noise caused by atmospheric clouds and snow cover.The theory of the new method is derived from graph theory,using the relationship of the Laplacian matrix to assign the weight of the pixel of the selected neighborhood window in EVI to get the fitting of the center pixel.The new method has been applied to MODIS MOD13A1 products from 2016 to 2018 and compared with the S-G filtering method,Harmonic Analysis of Time Series method,Double Logistic function method,and Asymmetric Gaussian model function method.The results show that in the desert,grassland,and woodland,the absolute difference of the leave-one verification test of the new method is the smallest,which is better than other methods;when fitting EVI time-series data of different vegetation types,the graph theory neighbor method presents a better detailed fitting curve;the RMSE values of the new method in the five vegetation types are 200.59,46.58,63.48,165.47,and 40.95 respectively,which are the smallest values among the five methods and are more effective in obtaining high-fidelity and high-quality EVI time-series data.The method research in this article can provide a useful reference for the denoising of vegetation remote sensing time-series data and the study of the ecological environment.