Timely and accurate detection of crop water stress is vital for precision irrigation. Whether the accuracy of the prevailing diagnosis of crop water stress using vegetation indices (VIs) and spectral reflectance can be improved still remains to be investigated. The crop surface characteristics such as grayscale or color vary under different water stress, so in this study one more variable, image texture, was utilized together to diagnose water stress. For this end, the canopy image of winter wheat in bloom was obtained by unmanned aerial vehicle (UAV) equipped with multispectral sensor, and the effect of soil background was eliminated using vegetation index threshold method. On this basis, Grey level co-occurrence matrix (GLCM) was used to calculate the mean (MEA), variance (VAR), homogeneity (HOM), contrast (CON), dissimilarity (DIS), entropy (ENT), second moment (SEC) and correlation (COR) of the image texture under different spatial resolutions (0.008 m, 0.01 m, 0.02 m, 0.05 m, 0.1 m and 0.2 m). Next, the canopy vegetation indices were obtained by mathematical transformation of canopy reflectance, and then sensitive image texture and vegetation indices by full subset regression method. Finally, Cubist, BPNN (Back Propagation Neural Network) and ELM (Extreme Learning Machine) methods were adopted to build the estimation models of the stomatal conductance (Gs) of winter wheat (between the sensitive image texture and Gs, and between vegetation index and Gs), and the water stress map was plotted based on the optimal Gs estimation model. The result showed: (i) the image texture obtained from the high-resolution multispectral image had a high correlation with Gs, and the image texture (VAR, HOM, CON, DIS, ENT and SEC) at 550 nm had the most significant correlation; (ii) the higher the ground resolution, the higher the correlation between the Gs and the image texture, the vegetation indices, respectively. The image texture with a ground resolution of 0.008 m combined with VIs and Gs had the highest correlation, and combining image texture and vegetation index can significantly improve the estimation accuracy of winter wheat Gs; (iii) Among the three estimation models, the BPNN model constructed by combining the image texture and VIs (MEA, VAR, ENT, DWSI and EXG) had the best estimation performance (Calibration:$R_{\mathrm{c}}^2$ = 0.899, RMSE_c = 0.01, MAE_c = 0.006; Validation:$R_{\mathrm{c}}^2$ = 0.834, RMSE_v =;0.018, MAE_v = 0.014), and an accurate estimation could even be achieved at a lower Gs value. Compared with the BPNN model solely based on VIs or image texture, the $R_{\mathrm{c}}^2$ of the BPNN model based on the combined variables increased by 24% and 22.48%, respectively. Therefore, combining UAV multispectral image texture and VIs to estimate Gs provides a feasible and accurate method for water stress diagnosis of winter wheat.

及时准确地检测作物水分胁迫对于精准灌溉至关重要。是否可以提高使用植被指数（VI）和光谱反射率对作物水分胁迫进行普遍诊断的准确性仍有待研究。作物表面特征（如灰度或颜色）在不同水分胁迫下会发生变化，因此在本研究中，另外一种变量图像纹理被一起用于诊断水分胁迫。为此，利用搭载多光谱传感器的无人机获取冬小麦盛开的冠层图像，并采用植被指数阈值法消除土壤背景的影响。在此基础上，采用灰度共生矩阵（GLCM）计算均值（MEA）、方差（VAR）、同质性（HOM）、对比度（CON）、相异性（DIS）、不同空间分辨率（0.008 m、0.01 m、0.02 m、0.05 m、0.1 m和0.2 m）下图像纹理的熵（ENT）、二阶矩（SEC）和相关性（COR）。接着，通过冠层反射率的数学变换得到冠层植被指数，然后通过全子集回归方法得到敏感图像纹理和植被指数。最后，采用Cubist、BPNN（Back Propagation Neural Network）和ELM（Extreme Learning Machine）方法建立冬小麦气孔导度（Gs）（敏感图像纹理与Gs之间，植被指数与Gs之间）估计模型。 Gs)，并根据最优 Gs 估计模型绘制水分胁迫图。结果表明：（i）高分辨率多光谱图像得到的图像纹理与Gs具有较高的相关性，550 nm 处的图像纹理（VAR、HOM、CON、DIS、ENT 和 SEC）具有最显着的相关性；(ii) 地面分辨率越高，Gs 与图像纹理、植被指数之间的相关性就越高。地面分辨率为0.008 m的图像纹理结合VIs和Gs的相关性最高，结合图像纹理和植被指数可以显着提高冬小麦Gs的估计精度；(iii) 在三种估计模型中，结合图像纹理和 VIs（MEA、VAR、ENT、DWSI 和 EXG）构建的 BPNN 模型具有最好的估计性能（Calibration：分别为植被指数。地面分辨率为0.008 m的图像纹理结合VIs和Gs的相关性最高，结合图像纹理和植被指数可以显着提高冬小麦Gs的估计精度；(iii) 在三种估计模型中，结合图像纹理和 VIs（MEA、VAR、ENT、DWSI 和 EXG）构建的 BPNN 模型具有最好的估计性能（Calibration：分别为植被指数。地面分辨率为0.008 m的图像纹理结合VIs和Gs的相关性最高，结合图像纹理和植被指数可以显着提高冬小麦Gs的估计精度；(iii) 在三种估计模型中，结合图像纹理和 VIs（MEA、VAR、ENT、DWSI 和 EXG）构建的 BPNN 模型具有最好的估计性能（Calibration：${\mathrm{电阻}}_{\mathrm{C}}^2$ = 0.899，RMSE _c = 0.01，MAE _c = 0.006；验证：${\mathrm{电阻}}_{\mathrm{C}}^2$ = 0.834, RMSE _v =;0.018, MAE _v = 0.014)，甚至可以在较低的 Gs 值下实现准确的估计。与仅基于 VI 或图像纹理的 BPNN 模型相比，${\mathrm{电阻}}_{\mathrm{C}}^2$基于组合变量的 BPNN 模型分别增加了 24% 和 22.48%。因此，结合无人机多光谱图像纹理和VIs估计Gs为冬小麦水分胁迫诊断提供了一种可行且准确的方法。