南疆地区沙尘多、 灰尘大, 枣树叶片表面经常覆盖一定程度的粗颗粒度沙尘, 为了有效去除沙尘、 灰尘在枣树叶片水分光谱测量过程中产生的散射噪声和基线漂移, 研究一种适用于风沙较大地区的枣树叶片水分含量的快速检测方法, 以不同灌溉梯度下的枣树叶片为研究对象, 通过近红外光谱仪获取120个叶片样本的1 000～1 800 nm的光谱数据, 并同步测量叶片水分含量, 采用归一化、 移动窗口平滑、 SavitZky-Golay（SG）卷积平滑、 SG求导、 标准正态变量校正(SNV)和多元散射校正(MSC)等方法对原始光谱进行预处理, 分析对比不同方法对散射噪声的处理能力, 采用偏最小二乘回归分析方法筛选了敏感波段和建立预测模型。 实验结果表明, 枣树叶片水分含量强吸收峰为1 443 nm, 波谷为1 661 nm; 归一化光谱并未消除1 000～1 400 nm波段的散射噪声; 移动窗口平滑和SG卷积平滑并未改进光谱曲线, 散射噪声仍然存在; SG导数光谱的光谱特征峰和特征谷明显左移, 光谱曲线不够平滑, 噪声明显; SNV和MSC方法具有较好的散射噪声消除能力。 偏最小回归分析方法筛选特征波长的结果表明（设置筛选波长数量为5）, 基于原始光谱未筛选到1 443 nm的强波峰和1 661 nm的波谷附近的波段; 基于归一化光谱在1 450 nm波峰附近筛选的波长有一定的偏差, 在1 661 nm波谷附近的筛选的波长明显高于1 700 nm; 基于移动窗口和SG卷积平滑光谱在1 443 nm具有一定的筛选能力, 但并未筛选到1 661 nm附近的波长; 导数光谱并未筛选到1 443和1 661 nm波段; SNV和MSC在波峰和波谷位置附近均筛选了敏感的光谱波段, 其中MSC略优于SNV方法恰好在波峰和波谷位置, 共筛选了1 002, 1 383, 1 411, 1 443和1 661 nm五个特征波段, 也证明了MSC方法散射噪声和基线漂移处理能力最优, 提高了敏感波长的筛选能力。 偏最小二乘回归模型结果表明, 不同预处理方法的RMSE值均较低, SNV和MSC方法改进了模型的预测结果, R2高于0.7, 其中基于MSC方法的模型具有最高的R2和最低的RMSEP和RMSEPCV, R2=0.750 4, RMSEP=0.034 3, RMSECV=0.021 5, 预测结果较优。 证明MSC方法对沙尘和颗粒度引入的散射噪声具有较好的去除能力, 可改进波长的筛选、 提高预测模型精度, 为南疆沙尘区的枣树叶片水分含量的无损检测提供了有效方法。

Precision irrigation for jujube crop in southern Xinjiang, China, is underlying to optimize the water use in such a drought-affected region. Water stress can be remotely assessed by evaluating the leaf moisture content using spectroscopy. These measurements are however affected by the presence of coarse sand and dust of the leaves induced by dry climates. This paper studied different methods to correct the spectral data in order to reduce the scattering noise with a baseline induced by such a jujube leaf covering. The reflectance of 120 leaf samples were measured by means of a near-infrared spectrometer (1 000～1 800 nm) and their moisture content was obtained by conventional drying method. The original reflectance spectrums were pre-processed by the normalization method, the moving smoothing method, the Savitzky-Golay (SG) convolution smoothing method, the SG first derivative method, the standard normal variables (SNV) method and the multiple scatter correction (MSC) method. The results of these different methods were compared and analyzed by means of partial leastsquares regressions (PLSR) allowing selecting sensitive spectral bands and establishing prediction models. The results showed that a significant reflectance peak related to the water content of the jujube leaves was located at 1 443 nm and that a local minimum of reflectance occurred at 1 661 nm. The predicition model based on the MSC method presented the best scattering noise reduction. The model performances were R2=0.750 4, RMSEP=0.034 3 and RMSEPCV=0.021 5. The five characteristic wavelengths were 1 002, 1 383, 1 411, 1 443 and 1 661 nm. In this experiment, the MSC method had a good ability to reduce the scattering noise generated by sand and dust covering. The preprocessing improved the selection ability of characteristic wavelengths and the accuracy of the prediction model. The results can therefore provide an effective detection method for the jujube leaf water in the sandy and dusty area of Southern Xinjiang, China.