中文摘要：

在田间利用高光谱技术监测土壤含水率（Soil Moisture Content,SMC）成为精准农业研究的热点之一,但农田原状光谱受到土壤表层属性如表面粗糙度、质地、微聚体和其它环境因素的影响,且小尺度区域SMC空间差异较小,增加了SMC光谱信息的提取难度,导致SMC的估算精度较低;基于实验室内经过筛制备的土壤样品的光谱数据建模,虽然模型精度较高,但人为改变土壤结构和紧实度的预处理方式无法表征农田SMC的实际状况.因此,该文尝试提出一种耦合土样原状光谱数据和标准光谱数据估算农田SMC的新方法.通过获取江汉平原潮土土样的原状光谱反射率（Rund）和烘干光谱反射率（Rdry）;基于Rdry确定研究区同一土壤类型在烘干状态下（SMC为0）的标准光谱（Std-R）;采用差值、比值、归一化的方法耦合Rund和Std-R,得到耦合光谱（Cpl-RS、CplRD、Cpl-RN）;提取耦合光谱中水分敏感波段的光谱（Moe-RS、Moe-RD、Moe-RN）,基于偏最小二乘回归方法（PLSR）建立SMC的估算模型.结果表明,标准光谱具有良好的代表性,能够为光谱耦合提供统一且稳定的背景值;耦合土样的原状光谱和标准光谱可以有效地削减土壤水分以外其它因素对土壤高光谱观测的影响;利用耦合光谱的水分敏感波段建立的SMC估算模型相较基于Rund建立的模型,有效降低了模型的复杂度,精度有较大程度地提升,建模集R2c从0.46最高上升至0.61,验证集R2p从0.49最高上升至0.71,RPD值从1.39最高上升至1.72,模型的稳定性、拟合度和预测能力都得到提升.该方法简单、易推广,为快速准确评估农田墒情提供了新途径.

英文摘要：

It becomes a hot spot in precision agricultural research to monitor soil moisture content （SMC） situation by using hyperspectral data in the field. However, the soil undisturbed spectral reflectance is affected by the soil surface properties such as surface roughness, texture and microcapsules as well as other environmental factors, and SMC spatial variation is not significant in small-sale region, making it more difficult to extract spectral information about SMC, which leading to the lower SMC estimation accuracy. In addition, the model based on hyperspectral data of soil samples by the laboratory preparation has higher accuracy, but the experimental samples have anthropogenic chan- ges in soil structure and tightness which are not able to show the actual status of SMC in the field. Therefore, the present work attempts to propose a new method by coupling undisturbed spectral data to standard spectral data to estimate SMC of farmland. Undisturbed spectral reflectance （Rund） and drying spectral reflectance （Rdry） of fluvo-aquic soil samples were collected; the standard spectral reflectance （Std-R） in the dry state （SMC is 0%） was determined based on Rdry ; Runa and Std-R were coupled by the algorithm of subtraction, division and normalization. Then the coupled spectral reflectance （Cpl-Rs, Cpl-RD and Cpl-RN） is generated; the moisture sensitive bands spectral reflectance（Moe-Rs, Moe-RD, Moe-RN） was extracted from coupled spectral reflectance; SMC estimation model is established using partial least squares regression （PLSR） method. The results showed that standard spectral reflectance had excellent representa- tion, which could provide a uniform and stable background value for coupling spectral methods; the coupled spectral reflectance reduced the influence of other factors except soil moisture on soil hyperspectral observations~ the model based on coupled spectral reflectance of moisture sensitive bands had a noticeable promotion compared to the model based on Rund （Rc^2 increase