基于低空遥感光谱与纹理信息的冬马铃薯冠层等效水厚度反演

Retrieval of Canopy Equivalent Water Thickness in Winter Potato Based on Low-altitude Remote Sensing and Spectral and Textural Information

摘要:

作物冠层水分状态监测对优化灌溉策略具有重要意义。本研究应用无人机低空遥感技术实现冬马铃薯冠层等效水厚度（Canopy equivalent water thickness, CEWT）反演，通过田间试验，采用无人机搭载多光谱相机获取不同灌水处理下各生育期冬马铃薯遥感影像，并同时测定3种水分指标：叶片含水率（Leaf water content, LWC）、叶片等效水厚度（Leaf equivalent water thickness, LEWT）和CEWT。对多光谱遥感影像剔除土壤背景，获取平均反射率（Average spectral reflectance, ASR）、植被指数（Vegetation indices, VIs）和纹理信息（Textures），基于相关系数分析降低自变量共线性构建数据集，结合偏最小二乘回归（Partial least squares regression, PLSR）、随机森林（Random forest, RF）和极限学习机（Extreme learning machine, ELM）构建定量反演模型，并获得试验区冬马铃薯CEWT空间分布信息。结果表明，冬马铃薯冠层水分指标随灌溉量增加呈上升趋势，各生育阶段ASR随着波长增加呈现先降低后上升特征。相对于LWC和LEWT，CEWT与ASR、VIs、Textures的相关性更优。基于ASR+VIs+Textures的RF模型表现最佳，预测能力较强，校正集、预测集决定系数分别为0.875和0.771，均方根误差分别为0.062、0.065mm，RPD为2.055。研究表明多变量融合可显著提高冬马铃薯CEWT反演准确性，为冬马铃薯冠层水分状态评估提供了参考。

Abstract:

Monitoring crop canopy water status is crucial for optimizing irrigation strategies. Low-altitude remote sensing technology was applied via an unmanned aerial vehicle (UAV) to retrieve the canopy equivalent water thickness (CEWT) of winter potato. Field experiments were conducted by using a UAV with multispectral cameras to capture images of winter potato under different irrigation treatments across various growth stages. Simultaneously, three water indicators were determined: leaf water content (LWC), leaf equivalent water thickness (LEWT), and CEWT. Soil backgrounds were removed from the multispectral remote sensing images to obtain average spectral reflectance (ASR), vegetation indices (VIs), and Textures. A dataset was constructed by reducing multicollinearity among independent variables through correlation analysis. Quantitative inversion models were developed by using partial least squares regression (PLSR), random forest (RF), and extreme learning machine (ELM) to obtain spatial distribution information of winter potato canopy water content in the experimental area. The results showed that the canopy water indicators of winter potatoes were increased with the rising of irrigation amounts, while the ASR across growth stages exhibited a pattern of initial decrease followed by an increase with wavelength. Compared with LWC and LEWT, CEWT showed a better correlation with ASR, VIs, and Textures. The RF model based on ASR+VIs+Textures exhibited the best performance, demonstrating strong predictive capability. The determination coefficients of the calibration and prediction datasets were 0.875 and 0.771, respectively, the root mean square errors were 0.062mm and 0.065mm, respectively, and the RPD was 2.055. The research result demonstrated that multivariable fusion can significantly enhance the accuracy of CEWT retrieval for winter potato, providing a reference for assessing the winter potato canopy water status.