Paper
1 September 2005 An analysis of seasonal and interannual variability of LAI during 1982 to 2004
Qifeng Lu, Wei Gao, Zhiqiang Gao, Wanli Wu, Xiaoling Pan, Bingyu Du, James R. Slusser
Author Affiliations +
Abstract
Using AVHRR (1982-1999) and MODIS (2000-2004) LAI products from 1982 to 1999, seasonal and interannual variability of LAI in China were analyzed. The results indicate that the LAI in most of China increase at different ratio while decreases in some area of Tibetan Plateau, the south of Xinjiang, Yunnan, and Sichuang Provinces and part of northeastern China. With the correlative degree method, it is demonstrated that temperature is the most closely related to the LAI change in China in this period; It also shows such LAI change has close relationship with soil moisture and precipitation as well. We compared the multi-year-averaged monthly LAI from AVHRR during 1982-1999 with the monthly LAI from MODIS during 2000-2004, finding that in winter, the LAI from AVHRR agrees to the LAI from MODIS, but in summer, during which the vegetation is growing up, the LAI from AVHRR is larger than from MODIS. The LAI from AVHRR is in good agreement with MODIS in each month in arid and semi-arid region. This long-term LAI data can be used in land surface model. Analysis was conducted to examine how sensitive land surface parameters are to LAI interannual variability. The result shows that the response is mainly in the range of ±15%, but for the fraction of the direct beam absorbed by canopy, the relative error is even larger than 40%, which suggest that the inter-annual difference of LAI must be considered in climate models.
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Qifeng Lu, Wei Gao, Zhiqiang Gao, Wanli Wu, Xiaoling Pan, Bingyu Du, and James R. Slusser "An analysis of seasonal and interannual variability of LAI during 1982 to 2004", Proc. SPIE 5884, Remote Sensing and Modeling of Ecosystems for Sustainability II, 58840U (1 September 2005); https://doi.org/10.1117/12.612983
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KEYWORDS
MODIS

Vegetation

Climatology

Reflectivity

Soil science

Data modeling

Environmental sensing

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