一种基于图论重构MODIS EVI时间序列数据集的新方法

doi:10.12082/dqxxkx.2022.210181

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (4): 738-749.doi: 10.12082/dqxxkx.2022.210181

一种基于图论重构MODIS EVI时间序列数据集的新方法

谌稳¹^,²(), 孙立群¹^,^*(), 李晴岚¹, 陈晨³, 李家叶³

1.中国科学院深圳先进技术研究院,深圳 518055
2.湘潭大学,湘潭 411100
3.东莞理工学院,东莞 523808

收稿日期:2021-04-06 修回日期:2021-07-06 出版日期:2022-04-25 发布日期:2022-06-25
通讯作者: ^*孙立群（1981— ）,男,博士,江苏常州人,助理研究员,主要从事生态遥感研究。E-mail: lq.sun@siat.ac.cn
作者简介:谌稳（1996— ）,男,硕士生,湖南怀化人,主要从事气象相关方面研究。E-mail: 201821511214@smail.xtu.edu.cn
基金资助:
国家自然科学基金项目(51679233);“十三五”国家重点研发计划项目(2017YFC0403600);广东省科技发展专项资金项目(2017A030310057)

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

CHEN Wen¹^,²(), SUN Liqun¹^,^*(), LI Qinglan¹, CHEN Chen³, LI Jiaye³

1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2. Xiangtan University, Xiangtan 411100, China
3. Dongguan University of Technology, Dongguan 523808, China

Received:2021-04-06 Revised:2021-07-06 Online:2022-04-25 Published:2022-06-25
Supported by:
National Natural Science Foundation of China(51679233);13^thFive-Year National Key Research and Development Program of China(2017YFC0403600);Special Fund for Science and Technology Development of Guangdong Province(2017A030310057)

摘要/Abstract

摘要：

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。

关键词: 图论邻点方法, EVI, 时序数据, 植被遥感, MODIS, 曲线拟合, 去噪, 重构方法

Abstract:

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.

Key words: graph theory neighbor point method, EVI, time-series data, vegetation remote sensing, MODIS, curve fitting, denoising, reconstruction method

谌稳, 孙立群, 李晴岚, 陈晨, 李家叶. 一种基于图论重构MODIS EVI时间序列数据集的新方法[J]. 地球信息科学学报, 2022, 24(4): 738-749.DOI:10.12082/dqxxkx.2022.210181

CHEN Wen, SUN Liqun, LI Qinglan, CHEN Chen, LI Jiaye. A New Method to Reconstruct MODIS EVI Time Series Data Set based on Graph Theory[J]. Journal of Geo-information Science, 2022, 24(4): 738-749.DOI:10.12082/dqxxkx.2022.210181

图/表 15

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代号	质量情况（PR）	描述
255	无数据	未处理
0	良好数据	可直接使用
1	边缘数据	部分可用,具体参考质量数据
2	雪/冰	不可用,雪/冰覆盖
3	云	不可用,云层覆盖

类型	普通值			最大值			最小值
类型	真实值	邻点插值	绝对差值	真实值	邻点插值	绝对差值	真实值	邻点插值	绝对差值
林地	5619	5579.871	39.129	7124	7049.797	74.203	1058	941.051	116.949
灌木	1116	1166.937	50.937	1888	1745.814	142.186	657	704.244	47.244
草原	857	895.397	38.397	1826	1655.251	170.749	430	526.679	96.679
农作物	3128	3474.913	346.913	5715	5487.952	227.048	897	944.294	47.294
荒漠	886	882.815	3.185	982	958.190	23.810	548	605.805	57.805

类型	真实值	线性插值数据	邻点插值	S-G方法	HANTS方法	A-G方法	D-L方法	最小绝对差
林地	5619	5488.500	5579.871	5481.347	5211.238	5476.249	5684.763	邻点插值
灌木	1116	1132.000	1166.937	1127.458	996.877	1137.098	1149.358	S-G方法
草原	857	867.500	895.397	801.331	743.219	799.136	786.916	邻点插值
农作物	3128	3291.000	3474.913	3318.579	2879.664	3218.487	3279.521	A-G方法
荒漠	886	875.000	882.815	863.136	923.531	844.394	837.469	邻点插值

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

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