华北低山丘陵区核桃叶片水分含量的高光谱估算
|
摘要
为了快速准确地诊断核桃(Juglans regia L.)水分状况从而及时调整水分管理措施、促进核桃产业的发展,以华北低山丘陵地区5年生核桃为研究对象,通过脱水试验探究叶片光谱反射率变化与水分状况改变的关系,系统分析了350～2 500 nm波段随机组合而成的反射率参数(R)、比值光谱指数(R(SI))、归一化光谱指数(N_(DSI))和差值光谱指数(D_(SI))与叶片含水量(EWT)的相关性,建立了基于光谱指数的快速、无损诊断核桃叶片水分含量估算模型并验证。结果表明:核桃叶片的高光谱反射特征随含水量改变呈明显规律性变化,在760～2 500 nm范围内,叶片光谱反射率随水分的降低而增高;基于双波段的光谱指数(N_(DSI)、R_(SI)和D_(SI))模型拟合度大于单波段的反射率参数;一阶导数处理后的光谱指数对叶片含水量的估算能力大于同一类型的原始光谱指数;基于一阶导数处理的光谱指数dR_(SI(1310,1405))和d N_(DSI(1310,1405))模型拟合度R~2=0.737(P<0.01)验证结果显示,(V-R2)分别为0.829和0.830,模型表现出良好的敏感性和稳定性。因此,dR_(SI(1310,1405))和dN_(DSI(1310,1405))模型均可用于核桃叶片含水量的定量监测。
To achieve rapid and non-destructive diagnosis of leave moisture content in walnut and timely adjust the water management measures to promote the development of the walnut industry,with the 5-year-old walnut tree( Juglans regia L.) in the hilly area of North China,the relationship between the spectral reflectance and the change of water condition by dehydration test were analyzed,and correlation relationship of reflectance parameter( R),ratio spectral index( RSI),normalized spectral index( N_(DSI)) and difference spectral index( DSI) of wavelength combination in the range of 350-2 500 nm with leaf moisture content( EWT) were compared,estimation models for the moisture content of walnut leaves based on spectral index were established and verified. The hyperspectral reflection characteristics of walnut leaves showed a regular change with the change of water content. In 760-2 500 nm,the spectral reflectance of leaves increased with the decrease of water content with the dual-band spectral index( N_(DSI),RSI and DSI). The model fit is greater than the single-band reflectance parameter( R); the first-order de-rivative-treated spectral index has greater ability to estimate leaf moisture content than the same type of original spectral index; the model fit of the spectral indices dR_(SI(1310,1405)) and d N_(DSI(1310,1405)) with the first derivative ref-lectance of R~2= 0.737( P = 0),the verification results show that V-R~2 are 0.829 and 0.830,respectively,and the models show good sensitivity and stability. Therefore,both dR_(SI(1310,1405))and d N_(DSI(1310,1405)) models can be used for quantitative monitoring of water content in walnut leaves.
To achieve rapid and non-destructive diagnosis of leave moisture content in walnut and timely adjust the water management measures to promote the development of the walnut industry,with the 5-year-old walnut tree( Juglans regia L.) in the hilly area of North China,the relationship between the spectral reflectance and the change of water condition by dehydration test were analyzed,and correlation relationship of reflectance parameter( R),ratio spectral index( RSI),normalized spectral index( N_(DSI)) and difference spectral index( DSI) of wavelength combination in the range of 350-2 500 nm with leaf moisture content( EWT) were compared,estimation models for the moisture content of walnut leaves based on spectral index were established and verified. The hyperspectral reflection characteristics of walnut leaves showed a regular change with the change of water content. In 760-2 500 nm,the spectral reflectance of leaves increased with the decrease of water content with the dual-band spectral index( N_(DSI),RSI and DSI). The model fit is greater than the single-band reflectance parameter( R); the first-order de-rivative-treated spectral index has greater ability to estimate leaf moisture content than the same type of original spectral index; the model fit of the spectral indices dR_(SI(1310,1405)) and d N_(DSI(1310,1405)) with the first derivative ref-lectance of R~2= 0.737( P = 0),the verification results show that V-R~2 are 0.829 and 0.830,respectively,and the models show good sensitivity and stability. Therefore,both dR_(SI(1310,1405))and d N_(DSI(1310,1405)) models can be used for quantitative monitoring of water content in walnut leaves.
引文
[1]桑玉强,张劲松.华北山区核桃液流变化特征及对不同时间尺度参考作物蒸散量的响应[J].生态学报,2014,34(23):6828-6836.
[2]毛罕平,高洪燕,张晓东.生菜叶片含水率光谱特征模型研究[J].农业机械学报,2011,42(5):166-170.
[3]刘良云,王纪华,张永江,等.叶片辐射等效水厚度计算与叶片水分定量反演研究[J].遥感学报,2007,11(3):289-295.
[4]薛利红,罗卫红,曹卫星,等.作物水分和氮素光谱诊断研究进展[J].遥感学报,2003,7(1):73-80.
[5]张寄阳,段爱旺,孙景生,等.作物水分状况自动监测与诊断的研究进展[J].农业工程学报,2006,22(1):174-178.
[6]COLOMBO R,MERONI M,MARCHESI A,et al.Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling[J].Remote Sensing of Environment,2008,112(4):1820-1834.
[7]CHENG T,RIVARD B,SNCHEZ-AZOFEIFA A.Spectroscopic determination of leaf water content using continuous wavelet analysis[J].Remote Sensing of Environment,2010,115(2):659-670.
[8]洪霞,江洪,余树全.高光谱遥感在精准农业生产中的应用[J].安徽农业科学,2010,38(1):529-531.
[9]张佳华,许云,姚凤梅,等.植被含水量光学遥感估算方法研究进展[J].中国科学:技术科学,2010,40(10):1121-1129.
[10]林毅,李倩,王宏博,等.干旱条件下春玉米高光谱特征及土壤含水量反演[J].生态学杂志,2016(5):1323-1329.
[11]FANG M H,JU W M,ZHAN W F,et al.A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves[J].Remote Sensing of Environment,2017,196:13-27.
[12]THOMAS J R,NAMKEN L N,OERTHER G F,et al.Estimating leaf water content by reflectance measure-ment[J].Agronomy Journal,1971,63:845-847.
[13]CAO Z X,WANG Q,ZHENG C L.Best hyperspectral indices for tracing leaf water status as determined from leaf dehy-dration experiments[J].Ecological Indicators,2015,54:96-107.
[14]FENG W,ZHANG H Y,ZHANG Y S,et al.Remote detection of canopy leaf nitrogen concentration in winter wheat by using water resistance vegetation indices from in-situ hyperspectral data[J].Field Crops Research,2016,198:238-246.
[15]王溥,武建军,聂建亮,等.不同植被水分指数对小麦水分状况监测效果对比[J].国土资源遥感,2010(3):97-100.
[16]吴见,谭靖,邓凯,等.基于优化指数的玉米冠层含水量遥感估测[J].湖南农业大学学报(自然科学版),2015,41(6):685-690.
[17]王强,易秋香,包安明,等.棉花冠层水分含量估算的高光谱指数研究[J].光谱学与光谱分析,2013,33(2):507-512.
[18]刘畅,孙鹏森,刘世荣.水分敏感的反射光谱指数比较研究:以锐齿槲栎为例[J].植物生态学报,2017,41(8):850-861.
[19]程志庆,张劲松,孟平,等.基于高光谱信息的107杨叶片等效水厚度估算模型的研究[J].林业科学研究,2016,29(6):826-833.
[20]朱西存,姜远茂,赵庚星,等.基于光谱指数的苹果叶片水分含量估算模型研究[J].中国农学通报,2014,30(4):120-126.
[21]胡珍珠,潘存德,潘鑫,等.基于光谱水分指数的核桃叶片含水量估算模型[J].林业科学,2016,52(12):39-49.
[22]PENUELAS J,PINOL J,OGAYA R,et al.Estimation of plant water concentration by the reflectance Water Index WI(R900/R970)[J].International Journal of Remote Sensing,1997,18(13):2869-2875.
[23]RIEDELL W E,BLACKMER T M.Leaf reflectance spectra of cereal aphid-damaged wheat[J].Crop Science,1999,39(6):1835-1840.
[24]HUNT E R Jr,ROCK B N.Detection of changes in leaf water content using near-and middle-infrared reflectances[J].Remote Sensing of Environment,1989,30(1):43-54.
[25]HARDISKY M A,SMART R M,KLEMAS V.Growth response and spectral characteristics of a short Spartina alterniflora salt marsh irrigated with freshwater and sewage effluent[J].Remote Sensing of Environment,1983,13(1):57-67.
[26]WU C Y,NIU Z,TANG Q,et al.Predicting vegetation water content in wheat using normalized difference water indices derived from ground measurements[J].Journal of Plant Research,2009,122(3):317-326.
[27]浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社,2000.
[28]唐延林,王纪华,黄敬峰,等.水稻成熟过程中高光谱与叶绿素、类胡萝卜素的变化规律研究[J].农业工程学报,2003,19(6):167-173.
[29]杨曦光,范文义,于颖.基于Hyperion数据的森林叶绿素含量反演[J].东北林业大学学报,2010,38(6):123-124.
[30]童庆禧,张兵,郑兰芬.高光谱遥感[M].北京:高等教育出版社,2006.
[31]徐道青,刘小玲,王维,等.淹水胁迫下棉花叶片高光谱特征及叶绿素含量估算模型[J].应用生态学报,2017,28(10):3289-3296.
[32]李珺,宋文龙.基于光谱反射特征的草莓叶片含水率模型[J].东北林业大学学报,2016,44(1):72-74.
[33]CORTI M,GALLINA P M,CAVALLI D,et al.Hyperspectral imaging of spinach canopy under combined water and ni-trogen stress to estimate biomass,water,and nitrogen content[J].Biosystems Engineering,2017,158:38-50.
[34]张银,周孟然.近红外光谱分析技术的数据处理方法[J].红外技术,2007,29(6):345-348.
[35]叶勤,姜雪芹,李西灿,等.基于高光谱数据的土壤有机质含量反演模型比较[J].农业机械学报,2017,48(3):164-172.
[2]毛罕平,高洪燕,张晓东.生菜叶片含水率光谱特征模型研究[J].农业机械学报,2011,42(5):166-170.
[3]刘良云,王纪华,张永江,等.叶片辐射等效水厚度计算与叶片水分定量反演研究[J].遥感学报,2007,11(3):289-295.
[4]薛利红,罗卫红,曹卫星,等.作物水分和氮素光谱诊断研究进展[J].遥感学报,2003,7(1):73-80.
[5]张寄阳,段爱旺,孙景生,等.作物水分状况自动监测与诊断的研究进展[J].农业工程学报,2006,22(1):174-178.
[6]COLOMBO R,MERONI M,MARCHESI A,et al.Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling[J].Remote Sensing of Environment,2008,112(4):1820-1834.
[7]CHENG T,RIVARD B,SNCHEZ-AZOFEIFA A.Spectroscopic determination of leaf water content using continuous wavelet analysis[J].Remote Sensing of Environment,2010,115(2):659-670.
[8]洪霞,江洪,余树全.高光谱遥感在精准农业生产中的应用[J].安徽农业科学,2010,38(1):529-531.
[9]张佳华,许云,姚凤梅,等.植被含水量光学遥感估算方法研究进展[J].中国科学:技术科学,2010,40(10):1121-1129.
[10]林毅,李倩,王宏博,等.干旱条件下春玉米高光谱特征及土壤含水量反演[J].生态学杂志,2016(5):1323-1329.
[11]FANG M H,JU W M,ZHAN W F,et al.A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves[J].Remote Sensing of Environment,2017,196:13-27.
[12]THOMAS J R,NAMKEN L N,OERTHER G F,et al.Estimating leaf water content by reflectance measure-ment[J].Agronomy Journal,1971,63:845-847.
[13]CAO Z X,WANG Q,ZHENG C L.Best hyperspectral indices for tracing leaf water status as determined from leaf dehy-dration experiments[J].Ecological Indicators,2015,54:96-107.
[14]FENG W,ZHANG H Y,ZHANG Y S,et al.Remote detection of canopy leaf nitrogen concentration in winter wheat by using water resistance vegetation indices from in-situ hyperspectral data[J].Field Crops Research,2016,198:238-246.
[15]王溥,武建军,聂建亮,等.不同植被水分指数对小麦水分状况监测效果对比[J].国土资源遥感,2010(3):97-100.
[16]吴见,谭靖,邓凯,等.基于优化指数的玉米冠层含水量遥感估测[J].湖南农业大学学报(自然科学版),2015,41(6):685-690.
[17]王强,易秋香,包安明,等.棉花冠层水分含量估算的高光谱指数研究[J].光谱学与光谱分析,2013,33(2):507-512.
[18]刘畅,孙鹏森,刘世荣.水分敏感的反射光谱指数比较研究:以锐齿槲栎为例[J].植物生态学报,2017,41(8):850-861.
[19]程志庆,张劲松,孟平,等.基于高光谱信息的107杨叶片等效水厚度估算模型的研究[J].林业科学研究,2016,29(6):826-833.
[20]朱西存,姜远茂,赵庚星,等.基于光谱指数的苹果叶片水分含量估算模型研究[J].中国农学通报,2014,30(4):120-126.
[21]胡珍珠,潘存德,潘鑫,等.基于光谱水分指数的核桃叶片含水量估算模型[J].林业科学,2016,52(12):39-49.
[22]PENUELAS J,PINOL J,OGAYA R,et al.Estimation of plant water concentration by the reflectance Water Index WI(R900/R970)[J].International Journal of Remote Sensing,1997,18(13):2869-2875.
[23]RIEDELL W E,BLACKMER T M.Leaf reflectance spectra of cereal aphid-damaged wheat[J].Crop Science,1999,39(6):1835-1840.
[24]HUNT E R Jr,ROCK B N.Detection of changes in leaf water content using near-and middle-infrared reflectances[J].Remote Sensing of Environment,1989,30(1):43-54.
[25]HARDISKY M A,SMART R M,KLEMAS V.Growth response and spectral characteristics of a short Spartina alterniflora salt marsh irrigated with freshwater and sewage effluent[J].Remote Sensing of Environment,1983,13(1):57-67.
[26]WU C Y,NIU Z,TANG Q,et al.Predicting vegetation water content in wheat using normalized difference water indices derived from ground measurements[J].Journal of Plant Research,2009,122(3):317-326.
[27]浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社,2000.
[28]唐延林,王纪华,黄敬峰,等.水稻成熟过程中高光谱与叶绿素、类胡萝卜素的变化规律研究[J].农业工程学报,2003,19(6):167-173.
[29]杨曦光,范文义,于颖.基于Hyperion数据的森林叶绿素含量反演[J].东北林业大学学报,2010,38(6):123-124.
[30]童庆禧,张兵,郑兰芬.高光谱遥感[M].北京:高等教育出版社,2006.
[31]徐道青,刘小玲,王维,等.淹水胁迫下棉花叶片高光谱特征及叶绿素含量估算模型[J].应用生态学报,2017,28(10):3289-3296.
[32]李珺,宋文龙.基于光谱反射特征的草莓叶片含水率模型[J].东北林业大学学报,2016,44(1):72-74.
[33]CORTI M,GALLINA P M,CAVALLI D,et al.Hyperspectral imaging of spinach canopy under combined water and ni-trogen stress to estimate biomass,water,and nitrogen content[J].Biosystems Engineering,2017,158:38-50.
[34]张银,周孟然.近红外光谱分析技术的数据处理方法[J].红外技术,2007,29(6):345-348.
[35]叶勤,姜雪芹,李西灿,等.基于高光谱数据的土壤有机质含量反演模型比较[J].农业机械学报,2017,48(3):164-172.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |