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Estimating Crop Leaf Area Index from Remotely Sensed Data: Scale Effects and Scaling Methods
1Y. Dong , 1J. Wang , 2C. Li , 2G. Yang, 2X. Song, 2W. Huang
1. Beijing Research Center for Information Technology in Agriculture;Institute of Agricultural Remote Sensing & Information System Application, Zhejiang University
2. Beijing Research Center for Information Technology in Agriculture
Leaf area index (LAI) of crop canopies is significant for growth condition monitoring and crop yield estimation, and estimating LAI based on remote sensing observations is the normal way to assess regional crop growth. However, the scale effects of LAI make multi-scale observations harder to be fully and effectively utilized for LAI estimation. A systematical statistical strategy is proposed to analyze scale effects of LAI in this paper. Firstly, appropriate model is selected for crop LAI estimation. Secondly, the best spatial resolution for regional LAI monitoring is extracted by analyzing the spatial heterogeneity of LAI. Finally, scaling method and ground observations are selected for results analysis and validation. Winter wheat in Beijing in 2008 is selected as experimental object. Taking ground observations and remote sensing observations as data sources, results and performances confirm the feasibility and validity of this new proposed strategy in scale effects of LAI analyzing.
Keyword: Precision agriculture, Scale effects, Scaling methods, Leaf area index (LAI)
Y. Dong     J. Wang     C. Li     G. Yang    X. Song    W. Huang     Remote Sensing Applications in Precision Agriculture    Poster    2012
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