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ISSN : 2287-8327

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Vol.28 No.4
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Analysis on Cloud-Originated Errors of MODIS Leaf Area Index and Primary Production Image : Effect of Monsoon Climate in Korea

Journal of Ecology and Environment / Journal of Ecology and Environment, (P)2287-8327; (E)2288-1220
2005, v.28 no.4, pp.215-222

(2005). Analysis on Cloud-Originated Errors of MODIS Leaf Area Index and Primary Production Image : Effect of Monsoon Climate in Korea. Journal of Ecology and Environment, 28(4), 215-222.
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Abstract

MODIS (Moderate Resolution Image Spectrometer) is a core satellite sensor boarded on Terra andAqua satelite of NASA Earth Observing System since 1999 and 2001, respectively. MODIS LAI, FPAR, and GPP provide useful means to monitor plant phenology and material cycles in terrestrial ecosystems. In this study, LAI, FPAR, and GPP in Korea were evaluated and errors aso ciated with cloud contamination on MODIS pixels were eliminated for years 2001~2003. Three-year means of cloud-corrected annual GP were 1836, 1369, and 1460 g C m-2y-1for evergreen needleleaf forest, deciduous broadleaf forest, and mixed forest, respectively. The cloud-originated errors were 8.5%, 13.1%, and 8.4% for FPAR, LAI, and GPP, respectively. Summertime errors from June to September explained by 78% of the annual accumulative errors in GPP. This study indicates that cloud-originated errors should be mitigated for practical use of MODIS vegetation products to monitor seasonal and annual changes in plant phenology and vegetation production in Korea.

keywords
Cloud contamination, MODIS, Plant phenology, Primary production, Cloud contamination, MODIS, Plant phenology, Primary production

Reference

1.

강신규 ; 김영일 ; 김영진, (2005) MODIS 총일차생산성 산출물의 오차요인 분석: 입력기상자료의 영향, 한국농림기상학회지

2.

준, (2005) 한국 생태계의 탄소와 물 순환의이해를 통한 기후변화협약대응. ,

3.

Cihlar,J, (160-170.) ,R. Latifovic,J. Chen and Z. Li. 1999. Testing near-real timedetection of contaminated pixels in AVHRR composites. CanadianJournal of Remote Sensing,

4.

Cohen, (1999) linking in situ and satellite measurements. RemoteSensing of Environment 70,

5.

Hwang, T.H. 2004. Spatial analysis of carbon and water processes withRHESSys model in the Gwangneung experimental forest. A thesisin Master Degree, Seoul National University

6.

Kang, (2003) C. Park and R. Loehman.2003. A MODIS-based climatological phenology model fordetecting onset of growing seasons in temperate mixed forests inKorea. Remote Sensing of Environment 86,

7.

Kang, (2004) D. Lee and J.S. Kimball. 2004. The effects of spatial aggregationof complex topography on hydro-ecological process simulationswithin a rugged forest landscape,

8.

Kang, (2005) J.S. Kimball and J. Glassy. 2005.Improving continuity of MODIS terrestrial photosynthesis productsusing an interpolation scheme for cloudy pixels. InternationalJournal of Remote Sensing 26,

9.

Kim,S, (2003) product over temperate forest. Korean Journal ofRemote Sensing 19:1-9.,

10.

Lee, Y. Park and K. Jang. 2003. Generation of forest leafarea index map using multispectral satellite data and fieldmeasurements. Korean Journal of Remote Sensing 19,

11.

Morisette, (2002) J.L. Privette and C.O. Justice. 2002. A framework forthe validation of MODIS Land products. Remote Sensing ofEnvironment 83,

12.

R.B, (2002) R.R. Nemani and S.W. Running.2002. Global products of vegetation leaf area and fractionabsorbed PAR from year one of MODIS data. Remote Sensing ofEnvironment 83,

13.

R.R, (1999) R.B. Myneni and S.W. Running. 2003. Climate-drivenincreases in global terrestrial net primary production from 1982 to1999. Science 300, Nemani

14.

S.D, (1995) Aremote sensing approach. Journal of Biogeography 22, Prince

15.

Running, P. Teillet and D. Carneggie. 1994. Terrestrialremote sensing science and algorithms planned for EOS/MODIS.International Journal of Remote Sensing 15,

16.

Running, (1999) ecosystem modeling and EOSsatellite data. Remote Sensing of Environment 70,

17.

Turner, (2003) overboreal and deciduous forest landscapes in support of MODIS GPPproduct validation. Remote Sensing of Environment 88,

18.

White, (1997) P.E. Thornton and S.W. Running. 1997. A continentalphenology model for monitoring vegetation responses to interannualclimatic variability. Global Biogeochemical Cycles 11,

19.

Zhao, (2005) R.R. Nemani and S.W. Running. 2005.Improvements of the MODIS terrestrial gross and net primaryproduction global dataset. Remote Sensing of Environment 95,

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