基于红光波段冠层SIF降尺度的小麦条锈病遥感监测

为减弱冠层几何结构等因素对传感器探测到的冠层日光诱导叶绿素荧光（Solar-induced chlorophyll fluorescence，SIF）的影响，探讨了条锈病胁迫下红光波段荧光（Red SIF，RSIF）的响应特性，并以RSIF为自变量构建了小麦条锈病遥感监测的线性回归（Simple linear regression，SLR）及非线性回归（Non-linear regression，NLR）模型。结果表明：叶片尺度RSIF在小麦条锈病遥感监测中具有较大优势，其与小麦条锈病病情严重度（Severity level，SL）间相关系数较远红光波段SIF（Far-red SIF，FRSIF）提高132%，以叶片尺度RSIF为自变量构建的SLR及NLR模型预测DSL与实测DSL之间R2较FRSIF分别增加9.8%、38.9%，RMSE分别降低23.1%、36.4%。此外，降尺度处理能够提高RSIF监测小麦条锈病的精度，叶片尺度RSIF与DSL之间R2较冠层尺度增加126.3%，以叶片尺度RSIF为自变量构建的SLR和NLR模型预测DSL与实测DSL间R2较冠层尺度分别提高114.3%和233.3%，RMSE分别降低16.7%、15.4%。本文提出方法可提高小麦条锈病遥感监测精度，同时对其它胁迫的遥感监测具有一定的参考价值。

Remote Sensing Monitoring of Wheat Stripe Rust Based on Canopy Downscaling Using Red SIF

In order to reduce the influence of canopy geometry and other factors on the sensor detected solar-induced chlorophyll fluorescence (SIF), the response characteristics of red SIF (RSIF) fluorescence under stripe rust stress were discussed. Simple linear regression (SLR) and non-linear regression (NLR) models for remote sensing monitoring of wheat stripe rust were constructed with RSIF as the independent variable. The results showed that the leaf scale RSIF had a significant advantage in the remote sensing monitoring of wheat stripe rust, with a 13.2% higher correlation with the severity level (SL) of wheat stripe rust compared with far-red SIF(FRSIF). Compared with FRSIF, the R2 between predicted DSL and measured DSL was increased by 9.8% and 38.9%, and the RMSE was decreased by 23.1% and 36.4%, respectively, using the linear regression model and non-linear regression model constructed with the blade scale RSIF as the independent variable. In addition, downscaling can improve the accuracy of RSIF monitoring of wheat stripe rust. The R2 between leaf scale RSIF and DSL was increased by 126.3% compared with the canopy scale. The R2 between DSL and measured DSL predicted by SLR and NLR models using leaf scale RSIF as independent variable was increased by 114.3% and 233.3%, respectively, compared with the canopy scale, and RMSE was decreased by 16.7% and 15.4%, respectively. The research results were of great significance for improving the remote sensing monitoring accuracy of wheat stripe rust, and also had certain reference value for remote sensing monitoring of other stresses.