Next Article in Journal
Automatic Object Extraction from Electrical Substation Point Clouds
Previous Article in Journal
Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(11), 15583-15604;

Satellite Image Time Series Decomposition Based on EEMD

1,2, 1,*, 3,†, 1,† and 1,2,†
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Raul Zurita-Milla, Josef Kellndorfer and Prasad Thenkabail
Received: 15 June 2015 / Revised: 29 October 2015 / Accepted: 12 November 2015 / Published: 19 November 2015
PDF [2004 KB, uploaded 19 November 2015]


Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD) is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs). EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD). Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), and Global Environment Monitoring Index (GEMI) time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes. View Full-Text
Keywords: satellite image time series; ensemble empirical mode decomposition; seasonal component; trend component satellite image time series; ensemble empirical mode decomposition; seasonal component; trend component

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Kong, Y.-L.; Meng, Y.; Li, W.; Yue, A.-Z.; Yuan, Y. Satellite Image Time Series Decomposition Based on EEMD. Remote Sens. 2015, 7, 15583-15604.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top