A Novel Compound Smoother-RMMEH to Reconstruct MODIS NDVI Time Series

被引:34
作者
Jin, Zhenyu [1 ,2 ]
Xu, Bing [1 ,2 ,3 ]
机构
[1] Univ Utah, Dept Geog, Salt Lake City, UT 84112 USA
[2] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
[3] Tsinghua Univ, Coll Environm, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
MODIS normalized difference vegetation index (NDVI) time series; phenology; remote sensing; smoothing; DATA SET;
D O I
10.1109/LGRS.2013.2253760
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
In this letter, we develop a novel, fast, nonlinear, and automated compound smoother, called RMMEH, to efficiently reduce noise of the normalized difference vegetation index (NDVI) time series and to reconstruct the MODIS NDVI time-series data with the two following main advantages: 1) ancillary data is not required, and 2) the whole procedure is automatically taken without any expert support. This new method involves several operations, including running medians smoother, arithmetic average, maximum (MAX) operation, and weighted moving average (WMA). The method is tested with the MODIS NDVI time series of MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid. Compared with other widely used smoothing techniques, this novel technique is proven to be more robust. It is simple in theory, easy to implement, efficient to operate, and resistant to most types of noise.
引用
收藏
页码:942 / 946
页数:5
相关论文
共 18 条
[1]  
[Anonymous], P ASPRS ANN C PORTL
[2]   A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter [J].
Chen, J ;
Jönsson, P ;
Tamura, M ;
Gu, ZH ;
Matsushita, B ;
Eklundh, L .
REMOTE SENSING OF ENVIRONMENT, 2004, 91 (3-4) :332-344
[3]   Remote Sensing-Based Time-Series Analysis of Cheatgrass (Bromus tectorum L.) Phenology [J].
Clinton, Nicholas E. ;
Potter, Christopher ;
Crabtree, Bob ;
Genovese, Vanessa ;
Gross, Peggy ;
Gong, Peng .
JOURNAL OF ENVIRONMENTAL QUALITY, 2010, 39 (03) :955-963
[4]   Noise reduction of NDVI time series: An empirical comparison of selected techniques [J].
Hird, Jennifer N. ;
McDermid, Gregory J. .
REMOTE SENSING OF ENVIRONMENT, 2009, 113 (01) :248-258
[6]   Seasonality extraction by function fitting to time-series of satellite sensor data [J].
Jönsson, P ;
Eklundh, L .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2002, 40 (08) :1824-1832
[7]  
Lieth H., 2013, Phenology and Seasonality Modeling
[8]   Reconstructing pathfinder AVHRR land NDVI time-series data for the Northwest of China [J].
Ma, Mingguo ;
Veroustraete, Frank .
NATURAL HAZARDS AND OCEANOGRAPHIC PROCESSES FROM SATELLITE DATA, 2006, 37 (04) :835-840
[9]  
Pallant J., 2010, Survival manual: a step by step guide to data analysis using IBM SPSS, DOI DOI 10.4324/9781003117452
[10]   MEASURING PHENOLOGICAL VARIABILITY FROM SATELLITE IMAGERY [J].
REED, BC ;
BROWN, JF ;
VANDERZEE, D ;
LOVELAND, TR ;
MERCHANT, JW ;
OHLEN, DO .
JOURNAL OF VEGETATION SCIENCE, 1994, 5 (05) :703-714