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14 July 2003 Rice Leaf Area Index (LAI) estimates from hyperspectral data
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Abstract
In this paper, we report some correlation analysis results between hyperspectral data in the spectral range of approximately 350nm-935nm and LAI of rice. Hyperspectral measurements were taken using an Analytical Spectral Devices (ASD) FieldSpec UV/VNIR Spectroradiometer at the experiment farm in 1999 and 2000.The potential of hyperspectral data for estimating LAI was evaluated using univariate correlation method with different types of predictors: original and the first-order derivative spectra, vegetation index (VI) based, spectral position-based, area-based predictors. The 6 VIs were constructed from the green-peek and red-well spectra bands; spectral position-based, predictors consisted of parameters extracted from the blue, yellow, and red edges, the green-peek and the red-well; area-based variables were calculated as the sum of the first derivative values at each of the three edges. Results showed that for univariate correlation analysis, the better results were obtained for LAI. The best LAI was obtained with the area-based predictors in prediction models for LAI. In univariate correlation analysis, it seems that only wavelength at maximum value of 1st derivative within red edge (Wr), reflectance at green-peak and at red-well, and their VIs may be employed to predict LAI, and betterR2 valued were obtained from the maximum first derivative spectra of blue edge (SDb) and red edge(SDr). In general, the results obtained from the accuracy assessment the best predictors are area-based ones, the VIs of SDb and SDr. Results from the correlation analysis showed that in the regions of the "three edges" for estimating LAI, sum of 1st derivative values within red edge was the most effective, sum of 1st derivative values within blue edge was the mere effective, sum of 1st derivative values within yellow edge was not effective.
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Xiuzhen Wang, Jingfeng Huang, Yunmei Li, and Renchao Wang "Rice Leaf Area Index (LAI) estimates from hyperspectral data", Proc. SPIE 4890, Ecosystems Dynamics, Ecosystem-Society Interactions, and Remote Sensing Applications for Semi-Arid and Arid Land, (14 July 2003); https://doi.org/10.1117/12.466486
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