通过高光谱遥感估测条锈病胁迫下的小麦冠层水分含量。 通过人工田间诱发不同等级小麦条锈病, 在不同生育期测定感染不同严重程度条锈病的冬小麦冠层光谱、 相对含水量(relative water content, RWC) 以及调查小麦条锈病病情指数(disease index, DI)。 研究发现随着小麦RWC的减少, 冠层光谱反射率在近红外区域(900～1 300 nm)逐渐降低, 而在短波红外区域(1 300～2 500 nm)逐渐增大, 且RWC与DI间具有强负相关性。 对冠层光谱进行平滑处理, 利用冠层光谱近红外与短波红外水分敏感波段构建比值指数, 然后建立以比值指数为变量的反演RWC线性模型, 并分析对比各模型反演RWC的精度以及稳定性, 结果发现比值指数R1 300/R1 200反演RWC的精度及稳定性(R2=0.63)都优于其他指数, 其线性模型反演绝对误差为3.43, 相对误差(relative error, RE)为4.78%。 该研究结果不仅为判别小麦病害提供辅助信息, 而且也为未来利用高光谱图像反演植物含水量提供理论与方法支持。

The aim of this paper is to estimate canopy relative water contents (RWC) of winter wheat under yellow rust stress by using hyperspectral remote sensing. The canopy reflectance of winter wheat that infected different severity yellow rust was collected and the disease index (DI) of the wheat was investigated respectively in the fields, whereafter the wheat was sampled corresponding to the canopy reflectance measurements and the RWC of the whole wheat were measured in the Laboratory. The research showed that the canopy spectra reflectance gradually decreased in the near-infrared (NIR) region (900-1 300 nm) with RWC reduction, however, canopy spectra reflectance gradually increased in the short-wave-infrared (SWIR) region (1 300-2 500 nm), and there was just higher minus correlation between RWC and DI. Smoothing the canopy spectra, the ratio indices were built by using the sensitive bands for water in NIR and SWIR, and then the estimation RWC linear models were built by using ratio indices as variables, and the model inversion precision and stability were analyzed and compared for estimation RWC. The result indicated that the inversion precision and the stability of the model with ratio index R1 300/R1 200 as variable excel other models, the linear model’s RMSE is 3.43, and the relative error is 4.78%. So, this study results not only can provide assistant information for diagnosing wheat disease but also can supply theories and methods for inversion vegetation RWC by using hyperspectral images in the future.