Scientia Agricultura Sinica ›› 2006, Vol. 39 ›› Issue (06): 1138-1145 .

• TILLAGE & CULTIVATION·PHYSIOLOGY & ECOLOGY • Previous Articles     Next Articles

Soybean LAI Estimation with in-situ Collected Hyperspectral Data Based on BP-Neural Networks

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  • Received:2005-08-10 Revised:2006-02-13 Online:2006-06-10 Published:2006-06-10

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Abstract: 【Objective】An experiment was carried out to evaluate the precision of hyperspectral reflectance model for monitoring soybean leaf area index (LAI).【Method】Soybean canopy reflectance data collected with ASD spectroradiometers (350- 1 050nm), which were cultivated in water-fertilizer coupled control conditions, and soybean LAI were collected simultaneously with LI-COR LAI-2000. Firstly, correlation between reflectance, derivative reflectance against soybean LAI were conducted; secondly, five vegetation indices with reflectance at bands 801nm and 670nm were applied to regress against soybean LAI; finally, ANN-BP was established for soybean LAI estimation with changeable nodes in hidden layers. 【Result】It was found that soybean canopy reflectance showed a negative correlation with soybean LAI, while it showed a positive correlation with soybean LAI in near infrared region. Reflectance derivative had an intimate Co relation with soybean LAI in blue, green and red edge spectral region, and got maximum correlation coefficient in red edge region. All five vegetation indices had an intimate correlation with soybean LAI, with regression determination coefficient R2 ranged from 0.84 to 0.88. ANN-BP model could greatly improve soybean LAI estimation accuracy. Determination coefficient (R2 = 0.92) obtained with 2 nodes in hidden layers, however, R2 still can be improved with nodes in hidden layers increasing, and R2 = 0.96 with 8 nodes in hidden layers. Still, it should be noticed that without indecent phonological soybean data participate model establishing, ANN-BP model could improve estimation accuracy with large room, and Determination coefficient (R2 = 0.99) could be obtained with 8 nodes in hidden layers. 【Conclusion】By above analysis, it is concluded that ANN-BP model could be applied to in-situ collected hyperspectral data for vegetation LAI estimation with quite accurate prediction, and in the future, ANN-BP model still should be applied to hyperspectral data for other vegetation biophysical and biochemical parameters estimation.

Key words: Hyperspectral, Soybean LAI, Vegetation index, ANN-BP

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