高光谱的草本植物水分含量检测模型构建
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摘要
基于高光谱开展植物水分盈缺检测是当前植物生理学研究的热点。羊茅草是我国北方草坪使用量最大的草本植物之一,其生长对水分需求量大,水分亏缺会使其叶片颜色、纹理、形态等物理特征和组织生理特性发生系列变化。开展基于高光谱的草本植物水分含量检测模型构建,可实现对羊茅草等草本植物水分盈缺状况的快速无损监测,利于全面、可靠的诊断草本植物水分状况并及时采取应对措施,为预测未来气候变化下北方常见草本植物生理响应及变化过程提供重要依据。以北方使用量最大的草本植物——羊茅草为例,采用盆栽控制实验法开展基于高光谱的植物水分含量观测模拟实验研究。实验在恒温恒湿培养箱中进行。经查阅有关文献,该实验共设置空气CO_2浓度(CX)(包括400和700μmol·mol~(-1)两个梯度)和土壤持水量(WX)(包括:100%田间持水量, 40%田间持水量, 20%田间持水量三个梯度)两个变量,共六种不同情景;在不同情景条件下,借助ASD Field Spec HandHeld光谱仪于每天10:00—14:00测定羊茅草叶片光谱反射参数,主要包括:光谱反射率(R_i)、一阶导数光谱(Dλ_i)、红边幅值(Dλr)、红边位置(λr)、红谷吸收深度(D)、红边面积(Sr)、光化学反射指数(PRI)、叶绿素指数(Rch)、归一化植被指数(NDVI)、比值植被指数(RVI)、归一化光谱指数(NDSI)、比值光谱指数(RSI)、分形维数(Fd)等。通过采集不同情景下植物光谱反射参数,采用多元线性逐步回归分析、方差分析、数学统计模型构建等多种方法,探讨不同生境条件下羊茅草叶片水分含量与光谱反射率(R_i)、红边幅值(Dλr)、红边面积(Sr)等光谱参数之间的量化关系,筛选出可以用于检测羊茅草水分含量状况的最优光谱特征参数,并构建了基于高光谱的羊茅草水分含量检测模型公式。研究结果表明:归一化植被指数(NDVI)、叶绿素指数(Rch)、分形维数(Fd)与羊茅草叶片含水量之间相关性在99%置信水平上达到极显著水平(p<0.01),且对于不同土壤水分胁迫条件下的羊茅草长势分辨效果较好,是监测羊茅草水分含量的有效参数和最优参数。同时发现,羊茅草叶片水分含量(Y)与诸多光谱特征参数(X)之间具有良好的多元线性关系,拟合得到羊茅草水分含量检测模型公式为:Y=-0.125X_(Rch)+1.714X_(NDVI)-0.023X_(Fd)+0.018,相关系数平方(R~2)达到0.89,通过F检验,模型检验达到极显著水平(F=15.588>7.21,p<0.01),说明建立的回归模型具备统计学意义,可以用于羊茅草水分含量检测。为快速便捷且准确无损的监测羊茅草受旱程度,指导大面积草坪灌溉和管理等提供;重要的技术支撑,对于丰富植被水分光谱探测研究具有重要理论与实践意义。
The detection of plant water deficit based on hyperspectral technology is the current research hotspot. Fescue is one of the major herbaceous plants which have the maximum usage in northern China, and its growth has a large demand for water, and it will have a series of changes in physical characteristics(color, texture, shape, etc.) and physiological characteristics under the condition of water deficit. By studying the establishment of plant water content detection model based on hyperspectral technology, the rapid non-destructive monitoring and assessment for the plant water deficit can be achieved, and the plant water status can be diagnosed comprehensively and reliably. The research can provide important basis for predicting the physiological response and change of common herbaceous plants in the North under future climate change. Fescue was sampled to carry out pot control simulation research under constant temperature and humidity conditions. The experiment involves two variables of CO_2 concentration(CX) and soil water holding capacity(WX). Two CO_2 gradients were set, 400 and 700 μmol·mol~(-1), respectively. Three water holding capacity treatments were carried out at each CO_2 gradient, 100%, 40% and 20% of water holding capacity respectively. And then an ASD Field spec Hand Held spectrometer was used to measure the spectral reflection parameters of the fescue at 10:00—14:00 per day, including Spectral Reflectance(Ri), First Derivative Spectrum(Dλi), Red Ddge Magnitude(Dλr), Red Edge Position(λr), Red Valley Absorption Depth(D), Red Edge Area(Sr), Photochemical Reflectance Index(PRI), Chlorophyll Index(Rch), Normalized Difference Vegetation Index(NDVI), Ratio Vegetation Index(RVI), Normalized Difference Spectral Index(NDSI), Ratio Spectrum Index(RSI), Fractal Dimension(Fd), etc. Regression analysis and statistical model building were applied to analyze the quantitative relationships between spectral parameters and physiological parameters. The complex relationships between spectral characteristic parameters and Fescue leaf water content were analyzed using statistical methods to extract optimal spectral characteristic parameters and subsequently to establish the estimation models of spectral characteristics and water deficit. The results showed that Normalized Difference Vegetation Index(NDVI), Chlorophyll Index(Rch), and Fractal Dimension(Fd) were significantly correlated with leaf water content at the 99% confidence level. So, we reckoned that the three spectral characteristic indicators are the most effective parameters for monitoring water deficit of Fescue. There are good linearities between leaf water content and spectral characteristic parameters, and the formula of detection model is, Y=-0.125X_(Rch)+1.714X_(NDVI)-0.023X_(Fd)+0.018, and the model test is significant at the 99% confidence level. The results can provide the technical supports for rapid non-destructive monitoring of the drought degree of Fescue, and provide a scientific guidance for large scale irrigation and management of Fescue.
The detection of plant water deficit based on hyperspectral technology is the current research hotspot. Fescue is one of the major herbaceous plants which have the maximum usage in northern China, and its growth has a large demand for water, and it will have a series of changes in physical characteristics(color, texture, shape, etc.) and physiological characteristics under the condition of water deficit. By studying the establishment of plant water content detection model based on hyperspectral technology, the rapid non-destructive monitoring and assessment for the plant water deficit can be achieved, and the plant water status can be diagnosed comprehensively and reliably. The research can provide important basis for predicting the physiological response and change of common herbaceous plants in the North under future climate change. Fescue was sampled to carry out pot control simulation research under constant temperature and humidity conditions. The experiment involves two variables of CO_2 concentration(CX) and soil water holding capacity(WX). Two CO_2 gradients were set, 400 and 700 μmol·mol~(-1), respectively. Three water holding capacity treatments were carried out at each CO_2 gradient, 100%, 40% and 20% of water holding capacity respectively. And then an ASD Field spec Hand Held spectrometer was used to measure the spectral reflection parameters of the fescue at 10:00—14:00 per day, including Spectral Reflectance(Ri), First Derivative Spectrum(Dλi), Red Ddge Magnitude(Dλr), Red Edge Position(λr), Red Valley Absorption Depth(D), Red Edge Area(Sr), Photochemical Reflectance Index(PRI), Chlorophyll Index(Rch), Normalized Difference Vegetation Index(NDVI), Ratio Vegetation Index(RVI), Normalized Difference Spectral Index(NDSI), Ratio Spectrum Index(RSI), Fractal Dimension(Fd), etc. Regression analysis and statistical model building were applied to analyze the quantitative relationships between spectral parameters and physiological parameters. The complex relationships between spectral characteristic parameters and Fescue leaf water content were analyzed using statistical methods to extract optimal spectral characteristic parameters and subsequently to establish the estimation models of spectral characteristics and water deficit. The results showed that Normalized Difference Vegetation Index(NDVI), Chlorophyll Index(Rch), and Fractal Dimension(Fd) were significantly correlated with leaf water content at the 99% confidence level. So, we reckoned that the three spectral characteristic indicators are the most effective parameters for monitoring water deficit of Fescue. There are good linearities between leaf water content and spectral characteristic parameters, and the formula of detection model is, Y=-0.125X_(Rch)+1.714X_(NDVI)-0.023X_(Fd)+0.018, and the model test is significant at the 99% confidence level. The results can provide the technical supports for rapid non-destructive monitoring of the drought degree of Fescue, and provide a scientific guidance for large scale irrigation and management of Fescue.
引文
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[2]JIANG Zi-zhu,ZHU Heng-guang,ZHANG Qian(姜籽竹,朱恒光,张倩).Crops(作物杂志),2015,(3):23.
[3]ZHANG Shu-yong,XIA Jiang-bao,ZHANG Guang-can,et al(张淑勇,夏江宝,张光灿,等).Acta Ecologica Sinica(生态学报),2014,34(8):1937.
[4]WANG Shu-wen,ZHAO Shan,ZHANG Chang-li,et al(王树文,赵珊,张长利,等).Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2016,32(13):149.
[5]LI Lan-tao,WANG Shan-qin,REN Tao,et al(李岚涛,汪善勤,任涛,等).Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2016,32(14):209.
[6]YUE Xue-jun,QUAN Dong-ping,HONG Tian-sheng,et al(岳学军,全东平,洪添胜,等).Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2015,31(8):207.
[7]LIU Xiao-jun,TIAN Yong-chao,YAO Xia,et al(刘小军,田永超,姚霞,等).Scientia Agricultura Sinica(中国农业科学),2012,45(3):435.
[8]LIANG Liang,ZHANG Lian-peng,LIN Hui,et al(梁亮,张连蓬,林卉,等).Scientia Agricultura Sinica(中国农业科学),2013,46(1):19.
[9]WU Long-guo,HE Jian-guo,LIU Gui-shan,et al(吴龙国,何建国,刘贵珊,等).Journal of Optoelectronics·Laser(光电子·激光),2014,25(1):135.
[10]LONG Guang-qiang,CHEN Gong,GUO Bin,et al(龙光强,陈功,郭滨,等).Journal of Yunnan Agricultural University(云南农业大学学报),2008,23(4):468.
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