叶片含水量（leaf water content, LWC）的快速监测对于作物的干旱诊断和灌溉决策至关重要。 以叶片、 冠层两个尺度, 原始、 一阶导数两种光谱处理形式的高光谱数据为基础, 采用两波段植被指数如归一化差分（normalized difference spectral index, NDSI）和比值光谱指数（ratio spectral index, RSI）, 偏最小二乘回归（partial least squares regression, PLSR）和竞争自适应重加权采样-偏最小二乘回归（competitive adaptive reweighted sampling-partial least squares regression, CARS-PLSR）三种方法对叶片含水量进行建模分析, 以确定最佳冬小麦叶片含水量预测模型。 结果表明: 三种方法中, 基于叶片一阶导数光谱的CARS-PLSR模型对LWC的预测效果最好, LWC的预测值与实测值高度重合（R2=0.969, RMSE=0.164, RRMSE=6.00%）。 相同条件下, 三种方法的叶片光谱模型对LWC的预测效果均优于冠层光谱模型。 在两波段指数模型以及PLSR模型中, 原始光谱模型对LWC的预测效果优于一阶导数光谱模型, 而在CARS-PLSR模型中结果相反。 在两波段指数模型中, RSI比NDSI具有更高的估算精度。 研究表明, 通过竞争自适应重加权采样方法提取敏感波段后所建立的偏最小二乘回归预测模型, 无论是预测精度还是建模精度, 与两波段指数和偏最小二乘回归模型相比都有了显著提高, 该方法可为精准快速地监测冬小麦旱情以及灌溉决策提供参考。

Fast and nondestructive monitoring of leaf water content (LWC) is critical to crop drought diagnosis and irrigation decision. In order to quantify and predict the LWC with hyperspectral remote sensing data, field experiments of winter wheat with different water-deficit stress levels were conducted for two consecutive years (2016—2017 and 2017—2018). Hyperspectral reflectance was recorded at four growth stages. Then, normalized difference spectral index (NDSI) and ratio spectral index (RSI) were calculated in all possible combinations within 350-2500 nm, and their correlations with LWC were quantified to identify the best indices. Spectral data were also used to build partial least squares regression (PLSR) and competitive adaptive reweighted sampling- partial least squares regression (CARS-PLSR) model to calculate LWC. Two different data forms (original and first derivative reflectance) and two different observation scales (leaf and canopy) were used to explore the suitability of these three algorithms on estimating LWC for winter wheat. Additionally, in order to avoid sampling uncertainty when constructing calibration and validation datasets, a method of increasing the number of sampling times was proposed to improve the robustness of prediction models. The results showed that the best spectral indices for estimating LWC of winter wheat were NDSI (R1 162, R1 321) and RSI（R1 162, R1 321） with R2 of 0.871 and 0.872 respectively, which were both based on original leaf reflectance. RSI models had higher estimation accuracy than NDSI models under the same situation. The PLSR model based on original leaf reflectance obtained the best performance for predicting LWC with R2 of 0.953. CARS-PLSR based on the first derivative leaf reflectance was the most accurate model (R2=0.969; RMSE=0.164; RRMSE=6%). It was indicated that in terms of different forms of hyperspectral data, the original spectral-based models were better than the first derivative spectral-based models in two-band vegetation index and PLSR models, but the results were reversed for the CARS-PLSR model. While for different observation scales, LWC had stronger correlations with leaf reflectance-based models than that of the canopy based models. Overall, CARS-PLSR delivered better performance than the other two methods. In light of this, CARS was a feasible band selection algorithm and the prediction accuracy of CARS-PLSR model for LWC estimation of winter wheat was better than that of the other two models. CARS-PLSR method provides a promising approach for accurate and rapid monitoring of winter wheat drought.