Estimating forage biomass and quality in a mixed sown pasture based on partial least squares regression with waveband selection
Estimating forage biomass and quality in a mixed sown pasture based on partial least squares regression with waveband selection
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DOI：
10.1111/j.1744-697x.2008.00116.x
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发表时间：
2008-09-01
影响因子：
1.3
通讯作者：
Inoue, Yoshio
中科院分区：
文献类型：
--
作者：
Kawamura, Kensuke;Watanabe, Nariyasu;Inoue, Yoshio
尽管偏最小二乘回归是一种全谱双线性回归方法，在牧草质量的实验室校准中得到了广泛的应用，但越来越多的证据表明，偏最小二乘回归模型中包含了一些冗余波长。因此，更仔细的波长选择可能会提高其预测精度，特别是在现场应用中。我们比较了PLS模型在400-2350 nm范围内对牧草地上生物量（BM）、粗蛋白质（CP）、酸性洗涤纤维（ADF）和中性洗涤纤维浓度的预测能力。2006年8月，在日本北海道某混合播种牧场的86个点进行了冠层反射率测量和植物取样。去除PLS模型中加权回归系数的最小值，实现了逐步的波段选择。对于所有牧场参数，交叉验证的决定系数（R-CV(2)）和均方根误差值分别随着波段的去除而增大和减小，直到达到最佳波段数。选定的波段数量在6个（占全部277个波段的2.2%）到47个（占17%）之间，这表明超过83%的波段是多余的或无用的。总体而言，在PLS模型中使用选定的波段可获得较高的R-2值和较低的预测均方根误差。特别是，波段选择大大提高了使用一阶导数反射光谱时BM （R-2 = 0.51-0.72）和ADF （R-2 = 0.30-0.65）的预测，以及CP （R-2 = 0.38-0.62）的预测。这些结果表明，利用PLS回归模型可以通过原位冠层反射率预测牧草质量和生物量，并且可以通过优化重要波段来提高模型的预测能力。
Although partial least squares (PLS) regression, a full-spectrum bilinear regression method, is widely used in laboratory calibrations of pasture quality, increasing evidence indicates that PLS models include some redundant wavelengths. Consequently, more careful wavelength selection might improve their predictive accuracy, especially in field applications. We compared the predictive ability of PLS models using whole and selected wavebands from in situ canopy reflectance spectra over 400-2350 nm to predict above-ground biomass (BM) and concentrations of crude protein (CP), acid detergent fiber (ADF) and neutral detergent fiber in herbage. Canopy reflectance measurements and plant sampling were conducted at 86 selected points in a mixed sown pasture in Hokkaido, Japan, in August 2006. Removing the minimum value of the weighted regression coefficient in the PLS model enabled stepwise waveband selection. For all pasture parameters, cross-validated coefficients of determination (R-CV(2)) and root mean square error values, respectively, increased and decreased with removal of wavebands until the optimum number of wavebands was reached. The number of selected wavebands ranged between six (2.2% of full 277 wavebands) and 47 (17%), suggesting that over 83% wavebands were redundant or useless. Overall, higher R-2 values and lower root mean squared errors of prediction were obtained using selected wavebands in the PLS model. Particularly, waveband selection greatly improved BM (R-2 = 0.51-0.72) and ADF (R-2 = 0.30-0.65) predictions when using the first derivative reflectance spectrum, and CP (R-2 = 0.38-0.62) prediction for reflectance. These results suggest that pasture quality and BM can be predicted by in situ canopy reflectance using a PLS regression model, and that the predictive ability of the model can be improved by optimizing important wavebands.