基于秦岭样区的四种时序EVI函数拟合方法对比研究

函数曲线拟合方法是植被指数时间序列重建的一个重要方法,已经广泛应用于森林面积动态变化监测、农作物估产、遥感物候信息提取、生态系统碳循环研究等领域。基于秦岭样区多年MODIS EVI遥感数据及其质量控制数据,探讨并改进了时序EVI重建过程中噪声点优化和对原始高质量数据保真能力的评价方法;在此基础上,比较了常用的非对称性高斯函数拟合法(AG)、双Logistic函数拟合法(DL)和单Logistic函数拟合法(SL)。基于SL方法,调整了模型形式并重新定义d的参数意义,提出了最值优化单Logistic函数拟合法(MSL),并与其他3种方法进行对比。结果表明;在噪声点优化及保留原始高质量数据方面,AG方法和DL方法二者整体差别不大,而在部分像元的处理上AG方法表现出更好的拟合效果;MSL方法和SL方法相比于AG方法和DL方法其效果更为突出;在地形气候复杂,植被指数噪声较多的山区,MSL方法表现出更好的适用性。

Analysis of four time series EVI data reconstruction methods

Time-series vegetation index data are contaminated with residual noise and cannot be used in land-cover change detection and crop yield estimation directly. To remove noise effectively, researchers have developed a series of methods for vegetation-index data reconstruction. Function curve-fitting methods are popular in the reconstruction of time-series vegetation index data and have been widely applied in many fields. Different function curve-fitting methods have specific adaptabilities to different geographical environments. In practice, researchers usually have to compare many function curve-fitting methods and select the most suitable one according to the characteristics of regional time-series vegetation index curve fluctuation. Therefore, the means of comparing different function curve fitting methods objectively and quantitatively is very important. Based on ten-year MODIS EVI data of evenly distributed sample areas and its quality control data from the Qinling Mountains, the evaluation method for EVI time-series data reconstruction was discussed and developed in this study. The new evaluation method can compare different function curve fitting methods objectively and quantitatively on two important aspects. One is the function curve-fitting effect under the disturbance of noise points, and another is the ability of retaining original high-quality data. In this study, we used EVI time series data of the sampling area in the Qinling Mountains to analyze the stability of the maximums and minimums of the EVI curves and found that the maximums are more stable in the EVI time series data than the minimums. Then we modified the form of the single logistic model on the basis of the above analysis. Finally, the Maximum optimization Logistic function fitting method (MSL) was proposed to improve the accuracy of the EVI time series reconstruction with large noise in complex mountains. In this study, a new evaluation method was used to compare the Asymmetry Gauss function fitting method (AG), Double Logistic function fitting method (DL), and Single Logistic function fitting method (SL) with the Maximum optimization Logistic function fitting method (MSL). The results show the following: (1) For the function curve fitting effect under the disturbance of the noise points while maintaining original high-quality data, AG showed better results in the treatment of several pixels. (2) Compared to the AG and DL, the fitting effect of SL and MSL is more significant. They not only undisturbed by the noise points but also have a stronger ability to maintain the original high-quality data than AG and DL. (3) Compared to the other methods, MSL was found to be more applicable for EVI time-series data reconstruction in complex mountains with large noise.