水分胁迫下冬小麦脯氨酸含量高光谱监测

Hyperspectral monitoring on proline content in winter wheat under water stress

干旱灾害频发会严重影响冬小麦的生长发育。通过干旱灾害的模拟,进行不同水分胁迫处理(田间持水量的80%、60%、45%、35%、30%),测定冬小麦游离脯氨酸含量(Pro),研究水分胁迫下冬小麦Pro含量对冠层光谱反射率的响应,通过相关分析法和逐步多元线性回归(CA+SMLR)、偏最小二乘法和逐步多元线性回归(PLS+SMLR)、连续投影算法(SPA)对Pro高光谱特征区域及波段进行提取,使用偏最小二乘回归(PLSR)和多元线性回归(MLR)方法建立Pro预测模型。结果表明:水分胁迫下,冬小麦Pro含量出现了一定积累,冠层光谱反射率在不同波段范围内发生了规律性变化,说明冬小麦Pro含量对水分胁迫响应敏感。相关分析发现,Pro含量与冠层光谱反射率红边区域的相关性较高,且754、756和761 nm波段对Pro含量变化敏感。构建的PLSR模型表现较好,MLR模型次之,但均有着较好的预测能力和较高的预测精度,说明利用高光谱技术对冬小麦Pro含量进行快速无损监测是可行的。

Frequent occurrence of drought disaster will seriously affect the growth and development of winter wheat(Triticum aestivum).We set different water stress treatments(80%,60%,45%,35%,30%of field water capacity)to simulate the severity of drought disaster.We measured free proline content(Pro)of winter wheat,and investigated the responses of Pro to canopy spectral reflectance under water stress.Three methods,i.e.,correlation analysis and stepwise multiple linear regression(CA+SMLR),partial least squares and stepwise multiple linear regression(PLS+SMLR),and successive projections algorithm(SPA)were used to extract the hyperspectral cha-racteristic region and characteristic band of proline.Furthermore,partial least square regression(PLSR)and multiple linear regression(MLR)methods were used to establish the predicted models.The results showed that Pro content of winter wheat was higher under water stress,and that the spectral reflectance of canopy changed regularly in different bands,indicating that Pro content of winter wheat was sensitive to water stress.The content of Pro was highly correlated with the red edge of canopy spectral reflectance,with the 754,756 and 761 nm bands being sensitive to Pro change.The PLSR model performed good,followed by the MLR model,both showing good predictive ability and high model accuracy.In general,it was feasible to monitor Pro content of winter wheat by hyperspectral technique.