小麦条锈病是由条形柄锈菌（Puccinia striiformis f. sp. tritici）引起的一种低温、高湿、强光型真菌性大区域流行性病害，是我国小麦病害防治的主要对象之一。高光谱数据包含精细的地物反射率光谱信息，能够准确无损地反映受病害胁迫作物的物理生化组分状况和冠层结构变化，但直接利用高维小样本数据的效果不佳。提取高光谱数据集中对条锈病敏感的光谱特征不仅可以降低计算成本，且能提高遥感监测作物病害模型的精度及其泛化能力。基于此，本文以小麦条锈病为研究对象，综合冠层高光谱数据和地面调查数据遥感监测小麦条锈病，基于重采样后不同波段宽度的冠层反射率数据计算了各光谱分辨率下植被指数对条锈病的敏感度，以期为条锈病监测中的高光谱数据降维和不同光谱分辨率下的最佳植被指数选取提供参考。论文的主要研究内容如下：

（1）为降低高光谱数据的维度、提高遥感监测小麦条锈病的模型精度，论文根据小麦冠层光谱在可见光-近红外光谱范围内对条锈病的特定响应以及日光诱导叶绿素荧光（Solar-induced Chlorophyll Fluorescence, SIF）对小麦光合生理变化的反映选取了对条锈病敏感的植被指数、“三边”参数、冠层SIF参量和特征波段构成初始特征集，分别利用相关系数（Correlation Coefficient, CC）分析和最大相关最小冗余（Maximum-relevance and Minimum-redundancy, mRMR）算法对初始特征集中各参量进行特征优选，并将选取的特征组合作为极限梯度提升（eXtreme Gradient Boosting, XGBoost）算法和梯度提升回归树（Gradient Boosting Regression Tree, GBRT）算法的自变量，从而构建冠层尺度的小麦条锈病遥感监测模型，并利用大田调查数据进行验证。研究结果表明，与CC分析相比，mRMR选择的特征在XGBoost和GBRT模型中的监测精度较CC分析分别平均提高了12%和17%。其中，mRMR-XGBoost模型取得了最好的监测精度（R²=0.8894，RMSE=0.1135），与mRMR-GBRT、CC-XGBoost和CC-GBRT模型相比，mRMR-XGBoost算法预测DI和实测DI之间的R²平均提高了5%、12%和22%。这些结果表明XGBoost更适合小麦条锈病的遥感监测，而mRMR在特征选择上比常用的CC分析更有优势。大田调查实验数据进一步验证了mRMR-XGBoost算法具有优异的泛化性和扩展性。

（2）为探究光谱分辨率对小麦条锈病遥感监测精度的影响，论文按照指定光谱宽度（5nm）将冠层高光谱数据重采样为5~80nm光谱分辨率的冠层反射率数据，分别计算了各光谱分辨率下常用于条锈病监测的13个植被指数值，并计算了每个植被指数对光谱分辨率的敏感度系数和植被指数在不同光谱分辨率下对病情指数的敏感度系数，基于各指数的最佳拟合模型构建了不同光谱分辨率下小麦条锈病的遥感监测模型。结果表明归一化差值指数（NDVI）和结构无关色素指数（SIPI）受光谱分辨率干扰小，适用于各光谱分辨率的传感器监测小麦条锈病。在植被指数对条锈病病情指数的敏感度分析中发现，三角植被指数（TVI）、植物衰老反射指数（PSRI）、氮反射指数（NRI）、归一化叶绿素比值指数（NPCI）、改进的简单比值植被指数（MSR）、红边植被胁迫指数（RVSI）、花青素反射指数（ARI）和光化学反射指数（PRI）在5~80nm波段范围内对DI均有明显响应。对比分析植被指数在不同光谱分辨率下遥感监测DI的精度得出，指数MSR、NRI、NDVI、PRI、SIPI和TVI在任何光谱分辨率下都能达到较高的监测精度。

（3）为确定不同卫星传感器监测条锈病时的适宜植被指数，论文模拟了6个常用于作物病害监测的卫星影像数据（SPOT-6、GF6-PMS、GF6-WFV、ZY-3、Landsat 8和Sentinel 2），基于卫星多光谱数据计算了13个多光谱指数，并与条锈病病情指数进行相关分析，从而对比各多光谱指数在不同卫星传感器平台与条锈病的相关性。研究结果表明，在不考虑空间分辨率影响的情况下，各植被指数在不同卫星传感器水平与DI的相关性差异不明显。高光谱数据和模拟多光谱数据用于同一研究区小麦条锈病病情严重度监测的精度对比结果显示，基于NDVI、NPCI和NRI在轻度发病时对小麦条锈病的敏感性不及中重度，而TVI则对轻度条锈病胁迫更为敏感。

Wheat stripe rust is a low-temperature, high-humidity and strong-light fungal epidemic disease caused by Puccinia striiformis f. sp. tritici. It is one of the major types of wheat disease prevention in China. The actual production is generally dominated by undifferentiated regional prevention and control, which increases the cost of wheat planting and pesticide residues in cultivated land. The advancement of agricultural informatization and the development of spectral detection technology provide technical support for rapid, non-destructive, and efficient monitoring of stripe rust. Hyperspectral data can provide detailed surface reflectance spectral information, which can accurately and non-destructively reflect the physical and biochemical components and canopy structure changes of disease-stressed crops. However, the high-dimensional and small sample data characteristics of hyperspectral data make their direct application ineffective. Extracting spectral features sensitive to stripe rust in hyperspectral datasets can not only reduce the computational cost but also improve the generalization ability of the model and the model accuracy of remote sensing monitoring of crop diseases. On this basis, the paper took wheat stripe rust as the research object, and integrated canopy hyperspectral data and ground survey data to monitor wheat stripe rust by remote sensing, and the sensitivity of vegetation index to stripe rust at each spectral resolution was calculated using the resampling canopy reflectance data, so as to provide a reference for data dimensionality reduction of hyperspectral data in stripe rust monitoring and the selection of the optimal vegetation index under different spectral resolutions. The major research contents of this dissertation are as follows:

(1) In order to reduce the dimension of hyperspectral data and improve the model accuracy of wheat stripe rust monitoring. According to the specific response of wheat plants to stripe rust in the visible-near-infrared spectral range and the reflection of solar-induced Chlorophyll Fluorescence (SIF) on the physiological changes of wheat photosynthesis. The paper selected the vegetation index, "trilateral" parameters, canopy SIF parameters, and characteristic bands that sensitive to stripe rust to form the initial feature set. Then, the max-relevance and min-redundancy (mRMR) algorithm and correlation coefficient (CC) analysis were used to reduce the dimensionality of the initial feature set, respectively. Features selected by mRMR and CC were input as independent variables into the extreme gradient boosting regression (XGBoost) and gradient boosting regression tree (GBRT) to monitor the severity of stripe rust. The results of this experiment show that, compared with CC analysis, the monitoring accuracy of the features selected by mRMR in the XGBoost and GBRT models increased by 12% and 17% on average, respectively. Meanwhile, mRMR-XGBoost model achieved the best monitoring accuracy (R²=0.8894, RMSE=0.1135). The R² between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable to the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability.

(2) In order to explore the effect of spectral resolution on the monitoring accuracy of wheat stripe rust by remote sensing, this paper simulated canopy hyperspectral data as multispectral data with a spectral resolution of 5-80 nm according to the specified spectral interval (5 nm). Thirteen vegetation index values commonly used for stripe rust monitoring were calculated under each band width, and the sensitivity coefficients of each vegetation index to different band widths and the sensitivity coefficient of vegetation index to DI under different band widths were quantified. Based on the optimal fitting model of each index, the remote sensing monitoring model of wheat stripe rust under different band widths was constructed. The experimental results indicated that the normalized difference vegetation index (NDVI) and structural independent pigment index (SIPI) are less affected by the band interference, and they are suitable for sensors of various spectral resolutions to monitor wheat stripe rust. Comparative analysis of the sensitivity of vegetation index to stripe rust disease index found that triangular vegetation index (TVI), plant senescence reflectance index (PSRI), nitrogen reflectance index (NRI), normalized pigment chlorophyll ratio index (NPCI), modified simple ratio index (MSR), red-edge vegetation stress index (RVSI), anthocyanin reflectance index (ARI) and photochemical reflectance index (PRI) have obvious responses to DI in the spectral resolution range of 5~80nm. The comparison experiments on the accuracy of vegetation index remote sensing monitoring DI in different wavelength bands show that the indices MSR, NRI, NDVI, PRI, SIPI and TVI can achieve higher monitoring accuracy in any spectral resolution.

(3) To determine the appropriate vegetation index for monitoring stripe rust with different sensors, this paper simulated 6 satellite image data (SPOT-6, GF6-PMS, GF6-WFV, ZY-3, Landsat 8, Sentinel 2) which are commonly used in crop disease monitoring. Based on satellite multispectral data, 13 multispectral indices were calculated, and correlation analysis was carried out with the stripe rust disease index, so as to compare the correlation between each multispectral index in different satellite sensors and stripe rust disease. Without considering the effect of spatial resolution, the correlation between each vegetation indices and DI at different satellite sensor levels was not significantly different. The accuracy comparison of hyperspectral data and simulated multispectral data for monitoring the severity of wheat stripe rust in the same study area showed that the sensitivity to wheat stripe rust based on NDVI, NPCI and NRI was less than moderate and severe in mild disease, while TVI was more sensitive to mild stripe rust stress.