DocumentCode
2318830
Title
Improvement of MODIS 8-day LAI/FPAR product with temporal filters to generate high quality time-series product
Author
Zhang, Huifang ; Shi, Runhe ; Zhong, Honglin ; Qu, Peiqing ; Sun, Juan ; Lin, Wenpeng ; Li, Su
Author_Institution
Key Lab. of Geographic Inf. Sci., East China Normal Univ., Shanghai, China
fYear
2009
fDate
20-22 May 2009
Firstpage
1
Lastpage
7
Abstract
Numerous studies have reported that the time-series terrestrial parameters such as the Normalized Difference Vegetation Index (NDVI), Leaf Area of Index (LAI), Fraction of Absorbed Photosynthetic Active Radiation (FPAR), derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, have played significant roles in researching the global environment, terrestrial ecosystems and related ecological researches. However, the remotely sensed signals are interfered severely by atmospheric conditions especially clouds and such noises exists in the time-series products as well. Therefore, to obtain a high quality time-series of terrestrial parameter is a necessary step before further studies. At present several methods have been applied to reduce the noise to construct a fine time-series of NDVI, but few studies concerning the other key terrestrial parameters, such as LAI, FPAR etc. In this paper, after comparing general methods in literatures, we designed a new method based on the Savitzky-Golay filter, which was applied to improve the quality of MODIS 8-Day LAI/FPAR Product to generate time-series of LAI and FPAR with high quality. Our validation results indicate that more smooth and realistic time-series curves of LAI/FPAR can be obtained by using this new method, which exemplify the dynamic change of forests, crop or plants and key input parameters in modeling the complex land surface processing.
Keywords
ecology; filtering theory; forestry; geophysical signal processing; remote sensing; time series; vegetation; AQUA/MODIS; NOAA/AVHRR; SPOT/VEGETATION; Savitzky-Golay filter; TERRA; absorbed photosynthetic active radiation fraction; clouds; complex land surface processing; crop; forests; index leaf area; normalized difference vegetation index; plants; remote sensing; terrestrial ecosystems; time-series product; Clouds; Crops; Design methodology; Ecosystems; Filters; Land surface; MODIS; Noise reduction; Vegetation; Working environment noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Urban Remote Sensing Event, 2009 Joint
Conference_Location
Shanghai
Print_ISBN
978-1-4244-3460-2
Electronic_ISBN
978-1-4244-3461-9
Type
conf
DOI
10.1109/URS.2009.5137494
Filename
5137494
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