基于CWT的人类不同程度干扰下干旱区土壤有机质含量估算研究
|
摘要
为了研究人类干扰活动对土壤有机质含量的影响以及提高干旱区土壤有机质估算精度,以新疆北部阜康市的土壤为研究对象,对90个采样点的高光谱曲线分别进行连续小波变换(CWT),并与两种常用光谱变换R′、lg(1/R)进行对比。结果表明,随着人类干扰程度的增加,土壤有机质的空间变异性随之增强;常用光谱变换中,Ⅰ、Ⅱ、Ⅲ区R′与土壤有机质所建立的偏最小二乘决定系数模型R~2均高于R、lg(1/R);经过CWT变换后所建模型精度更高,验证模型精度R~2分别为0.717、0.689、0.630,与R所建模型的R~2相比最大分别提高了0.382、0.4、0.389,且相对分析误差分别达到2.150、2.090、2.013,均能很好地预测土壤有机质含量,说明利用CWT不会因人类干扰程度的提高而使模型精度大幅度降低,更加适用于干旱区有机质含量的预测。
In order to explore the influence of human disturbance activities on soil organic matter content and improve the estimation accuracy of soil organic matter in arid area.The soil in Fukang City,northern Xinjiang,is studied.The hyperspectral curves of 90 sampling points are successively transformed by continuous wavelet transform(CWT)and compared with R′and lg(1/R),two common spectral transformation methods.The results show that the spatial variability of soil organic matter is enhanced with the increase of human disturbance degree.Moreover,in the common spectral transformation methods,R~2 of partial least squares models established by the soil organic matter institute and R′in zone Ⅰ,zone Ⅱ and zone Ⅲ are both higher than those by Rand lg(1/R).The precision of the model established after CWT is very high.Compared with R~2 of the model established by R,R~2 of the measured value and the predicted value are 0.717,0.689,0.630,increased by 0.382,0.4,0.389,respectively.In addition,the relative percent deviation correspondingly reach 2.150,2.090,2.013,indicating that CWT can well predict the soil organic matter contents.The usage of CWT does not greatly reduce the model accuracy with the increase of human disturbance degree and is more suitable for the prediction of organic matter content in arid regions.
In order to explore the influence of human disturbance activities on soil organic matter content and improve the estimation accuracy of soil organic matter in arid area.The soil in Fukang City,northern Xinjiang,is studied.The hyperspectral curves of 90 sampling points are successively transformed by continuous wavelet transform(CWT)and compared with R′and lg(1/R),two common spectral transformation methods.The results show that the spatial variability of soil organic matter is enhanced with the increase of human disturbance degree.Moreover,in the common spectral transformation methods,R~2 of partial least squares models established by the soil organic matter institute and R′in zone Ⅰ,zone Ⅱ and zone Ⅲ are both higher than those by Rand lg(1/R).The precision of the model established after CWT is very high.Compared with R~2 of the model established by R,R~2 of the measured value and the predicted value are 0.717,0.689,0.630,increased by 0.382,0.4,0.389,respectively.In addition,the relative percent deviation correspondingly reach 2.150,2.090,2.013,indicating that CWT can well predict the soil organic matter contents.The usage of CWT does not greatly reduce the model accuracy with the increase of human disturbance degree and is more suitable for the prediction of organic matter content in arid regions.
引文
[1]Shan H B,Jiang P A,Yan A,et al.Inversion of organic matter content in grey desert soil of northern Xinjiang oasis farmland based on hyper-spectral data[J].Journal of Agricultural Resources and Environment,2018,35(3):276-282.单海斌,蒋平安,颜安,等.基于高光谱数据的北疆绿洲农田灰漠土有机质反演[J].农业资源与环境学报,2018,35(3):276-282.
[2]Liu H J,Zhang B,Zhao J,et al.Spectral models for prediction of organic matter in black soil[J].Acta Pedologica Sinica,2007,44(1):27-32.刘焕军,张柏,赵军,等.黑土有机质含量高光谱模型研究[J].土壤学报,2007,44(1):27-32.
[3]Qiao J F,Xiong H G,Wang X P,et al.Spatial inversion of soil organic matter content in wasteland based on optimal model[J].Jiangsu Journal of Agricultural Sciences,2018,34(1):68-75.乔娟峰,熊黑钢,王小平,等.基于最优模型的荒地土壤有机质含量空间反演[J].江苏农业学报,2018,34(1):68-75.
[4]Han Z Y,Zhu X C,Liu Q,et al.Hyperspectral inversion models for soil organic matter content in the Yellow River Delta[J].Journal of Plant Nutrition and Fertilizer,2014,20(6):1545-1552.韩兆迎,朱西存,刘庆,等.黄河三角洲土壤有机质含量的高光谱反演[J].植物营养与肥料学报,2014,20(6):1545-1552.
[5]Fang S W,Yang M H,Zhao X M,et al.Spectral characteristics and quantitative estimation of SOM in red soil typical of Ji′an county,Jiangxi province[J].Acta Pedologica Sinica,2014,51(5):1003-1010.方少文,杨梅花,赵小敏,等.红壤区土壤有机质光谱特征与定量估算:以江西省吉安县为例[J].土壤学报,2014,51(5):1003-1010.
[6]Zheng M D,Xiong H G,Qiao J F,et al.Hyperspectral based estimation model about organic matter in desert soil at different levels of human disturbance[J].Arid Land Geography,2018,41(2):384-392.郑曼迪,熊黑钢,乔娟峰,等.基于高光谱的不同人类干扰程度下荒漠土壤有机质含量估算模型[J].干旱区地理,2018,41(2):384-392.
[7]Nan F,Zhu H F,Bi R T.Hyperspectral prediction of soil organic matter content in the reclamation cropland of coal mining areas in the Loess Plateau[J].Scientia Agricultura Sinica,2016,49(11):2126-2135.南锋,朱洪芬,毕如田.黄土高原煤矿区复垦农田土壤有机质含量的高光谱预测[J].中国农业科学,2016,49(11):2126-2135.
[8]Lin P D,Tong Z J,Zhang J Q,et al.Inversion of black soil organic matter content with field hyperspectral reflectance based on continuous wavelet transformation[J].Research of Soil and Water Conservation,2018,25(2):46-52,57.林鹏达,佟志军,张继权,等.基于CWT的黑土有机质含量野外高光谱反演模型[J].水土保持研究,2018,25(2):46-52,57.
[9]Yu L,Hong Y S,Zhou Y,et al.Inversion of soil organic matter content using hyperspectral data based on continuous wavelet transformation[J].Spectroscopy and Spectral Analysis,2016,36(5):1428-1433.于雷,洪永胜,周勇,等.连续小波变换高光谱数据的土壤有机质含量反演模型构建[J].光谱学与光谱分析,2016,36(5):1428-1433.
[10]Cai L H,Ding J L.Inversion of soil moisture content based on hyperspectral multi-scale decomposition[J].Laser&Optoelectronics Progress,2018,55(1):013001.蔡亮红,丁建丽.基于高光谱多尺度分解的土壤含水量反演[J].激光与光电子学进展,2018,55(1):013001.
[11]Jiang X Q,Ye Q,Lin Y,et al.Inverting study on soil water content based on harmonic analysis and hyperspectral remote sensing[J].Acta Optica Sinica,2017,37(10):1028001.姜雪芹,叶勤,林怡,等.基于谐波分析和高光谱遥感的土壤含水量反演研究[J].光学学报,2017,37(10):1028001.
[12]Zhou C,Liu N W,He T B,et al.Application of wavelet threshold denoising technique in expired gas analysis based on laser spectroscopy[J].Chinese Journal of Lasers,2017,44(11):1111003.周超,刘宁武,何天博,等.小波阈值去噪技术在呼出气体激光光谱诊断中的应用研究[J].中国激光,2017,44(11):1111003.
[13]Bai S W,Sun G,Li X B,et al.Application of wavelet analysis in research of near-surface turbulence coherent structure[J].Acta Optica Sinica,2018,38(5):0501002.白士伟,孙刚,李学彬,等.小波分析在近地面湍流相干结构研究中的应用[J].光学学报,2018,38(5):0501002.
[14]Liao Q H,Gu X H,Li C J,et al.Estimation of fluvo-aquic soil organic matter content from hyperspectral reflectance based on continuous wavelet transformation[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(23):132-139,298.廖钦洪,顾晓鹤,李存军,等.基于连续小波变换的潮土有机质含量高光谱估算[J].农业工程学报,2012,28(23):132-139,298.
[15]Xiao R.Spatiotemporal changes of soil and vegetation in response to anthropogenic activities[D].Hangzhou:Zhejiang University,2014.肖锐.人类活动影响下流域土壤及植被的时空格局变化[D].杭州:浙江大学,2014.
[16]Zhang S,Wang L C,Zhou H F,et al.Analysis of soil respiration and influencing factors in maize farmland under different tillage patterns in hilly area in Southwest China[J].Acta Ecologica Sinica,2014,34(21):6244-6255.张赛,王龙昌,周航飞,等.西南丘陵区不同耕作模式下玉米田土壤呼吸及影响因素[J].生态学报,2014,34(21):6244-6255.
[17]Xiong P S.Research review of the impact factors on soil respiration in terrestrial ecosystem[J].Soil and Fertilizer Sciences in China,2017(4):1-7.熊平生.陆地生态系统土壤呼吸的影响因素研究综述[J].中国土壤与肥料,2017(4):1-7.
[18]Zhang L J,Yue M,Zhang Y D,et al.Characteristics of plant community species diversity of oasis desert ecotone in Fukang,Xinjiang[J].Scientia Geographica Sinica,2003,23(3):329-334.张林静,岳明,张远东,等.新疆阜康绿洲荒漠过渡带植物群落物种多样性特征[J].地理科学,2003,23(3):329-334.
[19]Yu Z Y,Peng S L,Ding M M.Ecological studies on vegetation rehabilitation of tropical and subtropical degraded ecosystems[M].Guangzhou:Guangdong Science and Technology Press,1996.余作岳,彭少麟,丁明懋.热带亚热带退化生态系统植被恢复生态学研究[M].广州:广东科技出版社,1996.
[20]Bao S D.Soil agrochemical analysis[M].3rd ed.Beijing:China Agricultural Press,2008.鲍士旦.土壤农化分析[M].3版.北京:中国农业出版社,2008.
[21]Fang S H,Le Y,Liang Q.Retrieval of chlorophyll content using continuous wavelet analysis across a range of vegetation species[J].Geomatics and Information Science of Wuhan University,2015,40(3):296-302.方圣辉,乐源,梁琦.基于连续小波分析的混合植被叶绿素反演[J].武汉大学学报(信息科学版),2015,40(3):296-302.
[22]Cui X,Song Q J,Zhang Y Y,et al.Estimation of soil organic carbon content in alpine grassland using hyperspectral data[J].Acta Prataculturae Sinica,2017,26(10):20-29.崔霞,宋清洁,张瑶瑶,等.基于高光谱数据的高寒草地土壤有机碳预测模型研究[J].草业学报,2017,26(10):20-29.
[23]Viscarra Rossel R A,McGlynn R N,McBratney AB.Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy[J].Geoderma,2006,137(1/2):70-82.
[24]Mao B H,Sun H,Liu H J,et al.Prediction of winter wheat chlorophyll content based on gramschmidt and SPXY algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(S1):160-165.毛博慧,孙红,刘豪杰,等.基于正交变换与SPXY样本划分的冬小麦叶绿素诊断[J].农业机械学报,2017,48(S1):160-165.
[25]Peng J,Zhou Q,Zhang Y Z,et al.Effect of soil organic matter on spectral characteristics of soil[J].Acta Pedologica Sinica,2013,50(3):517-524.彭杰,周清,张杨珠,等.有机质对土壤光谱特性的影响研究[J].土壤学报,2013,50(3):517-524.
[26]He T,Wang J,Lin Z J,et al.Spectral features of soil organic matter[J].Geomatics and Information Science of Wuhan University,2006,31(11):975-979.何挺,王静,林宗坚,等.土壤有机质光谱特征研究[J].武汉大学学报(信息科学版),2006,31(11):975-979.
[27]Liu L,Shen R P,Ding G X.Studies on the estimation of soil organic matter content based on hyper-spectrum[J].Spectroscopy and Spectral Analysis,2011,31(3):762-766.刘磊,沈润平,丁国香.基于高光谱的土壤有机质含量估算研究[J].光谱学与光谱分析,2011,31(3):762-766.
[2]Liu H J,Zhang B,Zhao J,et al.Spectral models for prediction of organic matter in black soil[J].Acta Pedologica Sinica,2007,44(1):27-32.刘焕军,张柏,赵军,等.黑土有机质含量高光谱模型研究[J].土壤学报,2007,44(1):27-32.
[3]Qiao J F,Xiong H G,Wang X P,et al.Spatial inversion of soil organic matter content in wasteland based on optimal model[J].Jiangsu Journal of Agricultural Sciences,2018,34(1):68-75.乔娟峰,熊黑钢,王小平,等.基于最优模型的荒地土壤有机质含量空间反演[J].江苏农业学报,2018,34(1):68-75.
[4]Han Z Y,Zhu X C,Liu Q,et al.Hyperspectral inversion models for soil organic matter content in the Yellow River Delta[J].Journal of Plant Nutrition and Fertilizer,2014,20(6):1545-1552.韩兆迎,朱西存,刘庆,等.黄河三角洲土壤有机质含量的高光谱反演[J].植物营养与肥料学报,2014,20(6):1545-1552.
[5]Fang S W,Yang M H,Zhao X M,et al.Spectral characteristics and quantitative estimation of SOM in red soil typical of Ji′an county,Jiangxi province[J].Acta Pedologica Sinica,2014,51(5):1003-1010.方少文,杨梅花,赵小敏,等.红壤区土壤有机质光谱特征与定量估算:以江西省吉安县为例[J].土壤学报,2014,51(5):1003-1010.
[6]Zheng M D,Xiong H G,Qiao J F,et al.Hyperspectral based estimation model about organic matter in desert soil at different levels of human disturbance[J].Arid Land Geography,2018,41(2):384-392.郑曼迪,熊黑钢,乔娟峰,等.基于高光谱的不同人类干扰程度下荒漠土壤有机质含量估算模型[J].干旱区地理,2018,41(2):384-392.
[7]Nan F,Zhu H F,Bi R T.Hyperspectral prediction of soil organic matter content in the reclamation cropland of coal mining areas in the Loess Plateau[J].Scientia Agricultura Sinica,2016,49(11):2126-2135.南锋,朱洪芬,毕如田.黄土高原煤矿区复垦农田土壤有机质含量的高光谱预测[J].中国农业科学,2016,49(11):2126-2135.
[8]Lin P D,Tong Z J,Zhang J Q,et al.Inversion of black soil organic matter content with field hyperspectral reflectance based on continuous wavelet transformation[J].Research of Soil and Water Conservation,2018,25(2):46-52,57.林鹏达,佟志军,张继权,等.基于CWT的黑土有机质含量野外高光谱反演模型[J].水土保持研究,2018,25(2):46-52,57.
[9]Yu L,Hong Y S,Zhou Y,et al.Inversion of soil organic matter content using hyperspectral data based on continuous wavelet transformation[J].Spectroscopy and Spectral Analysis,2016,36(5):1428-1433.于雷,洪永胜,周勇,等.连续小波变换高光谱数据的土壤有机质含量反演模型构建[J].光谱学与光谱分析,2016,36(5):1428-1433.
[10]Cai L H,Ding J L.Inversion of soil moisture content based on hyperspectral multi-scale decomposition[J].Laser&Optoelectronics Progress,2018,55(1):013001.蔡亮红,丁建丽.基于高光谱多尺度分解的土壤含水量反演[J].激光与光电子学进展,2018,55(1):013001.
[11]Jiang X Q,Ye Q,Lin Y,et al.Inverting study on soil water content based on harmonic analysis and hyperspectral remote sensing[J].Acta Optica Sinica,2017,37(10):1028001.姜雪芹,叶勤,林怡,等.基于谐波分析和高光谱遥感的土壤含水量反演研究[J].光学学报,2017,37(10):1028001.
[12]Zhou C,Liu N W,He T B,et al.Application of wavelet threshold denoising technique in expired gas analysis based on laser spectroscopy[J].Chinese Journal of Lasers,2017,44(11):1111003.周超,刘宁武,何天博,等.小波阈值去噪技术在呼出气体激光光谱诊断中的应用研究[J].中国激光,2017,44(11):1111003.
[13]Bai S W,Sun G,Li X B,et al.Application of wavelet analysis in research of near-surface turbulence coherent structure[J].Acta Optica Sinica,2018,38(5):0501002.白士伟,孙刚,李学彬,等.小波分析在近地面湍流相干结构研究中的应用[J].光学学报,2018,38(5):0501002.
[14]Liao Q H,Gu X H,Li C J,et al.Estimation of fluvo-aquic soil organic matter content from hyperspectral reflectance based on continuous wavelet transformation[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(23):132-139,298.廖钦洪,顾晓鹤,李存军,等.基于连续小波变换的潮土有机质含量高光谱估算[J].农业工程学报,2012,28(23):132-139,298.
[15]Xiao R.Spatiotemporal changes of soil and vegetation in response to anthropogenic activities[D].Hangzhou:Zhejiang University,2014.肖锐.人类活动影响下流域土壤及植被的时空格局变化[D].杭州:浙江大学,2014.
[16]Zhang S,Wang L C,Zhou H F,et al.Analysis of soil respiration and influencing factors in maize farmland under different tillage patterns in hilly area in Southwest China[J].Acta Ecologica Sinica,2014,34(21):6244-6255.张赛,王龙昌,周航飞,等.西南丘陵区不同耕作模式下玉米田土壤呼吸及影响因素[J].生态学报,2014,34(21):6244-6255.
[17]Xiong P S.Research review of the impact factors on soil respiration in terrestrial ecosystem[J].Soil and Fertilizer Sciences in China,2017(4):1-7.熊平生.陆地生态系统土壤呼吸的影响因素研究综述[J].中国土壤与肥料,2017(4):1-7.
[18]Zhang L J,Yue M,Zhang Y D,et al.Characteristics of plant community species diversity of oasis desert ecotone in Fukang,Xinjiang[J].Scientia Geographica Sinica,2003,23(3):329-334.张林静,岳明,张远东,等.新疆阜康绿洲荒漠过渡带植物群落物种多样性特征[J].地理科学,2003,23(3):329-334.
[19]Yu Z Y,Peng S L,Ding M M.Ecological studies on vegetation rehabilitation of tropical and subtropical degraded ecosystems[M].Guangzhou:Guangdong Science and Technology Press,1996.余作岳,彭少麟,丁明懋.热带亚热带退化生态系统植被恢复生态学研究[M].广州:广东科技出版社,1996.
[20]Bao S D.Soil agrochemical analysis[M].3rd ed.Beijing:China Agricultural Press,2008.鲍士旦.土壤农化分析[M].3版.北京:中国农业出版社,2008.
[21]Fang S H,Le Y,Liang Q.Retrieval of chlorophyll content using continuous wavelet analysis across a range of vegetation species[J].Geomatics and Information Science of Wuhan University,2015,40(3):296-302.方圣辉,乐源,梁琦.基于连续小波分析的混合植被叶绿素反演[J].武汉大学学报(信息科学版),2015,40(3):296-302.
[22]Cui X,Song Q J,Zhang Y Y,et al.Estimation of soil organic carbon content in alpine grassland using hyperspectral data[J].Acta Prataculturae Sinica,2017,26(10):20-29.崔霞,宋清洁,张瑶瑶,等.基于高光谱数据的高寒草地土壤有机碳预测模型研究[J].草业学报,2017,26(10):20-29.
[23]Viscarra Rossel R A,McGlynn R N,McBratney AB.Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy[J].Geoderma,2006,137(1/2):70-82.
[24]Mao B H,Sun H,Liu H J,et al.Prediction of winter wheat chlorophyll content based on gramschmidt and SPXY algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(S1):160-165.毛博慧,孙红,刘豪杰,等.基于正交变换与SPXY样本划分的冬小麦叶绿素诊断[J].农业机械学报,2017,48(S1):160-165.
[25]Peng J,Zhou Q,Zhang Y Z,et al.Effect of soil organic matter on spectral characteristics of soil[J].Acta Pedologica Sinica,2013,50(3):517-524.彭杰,周清,张杨珠,等.有机质对土壤光谱特性的影响研究[J].土壤学报,2013,50(3):517-524.
[26]He T,Wang J,Lin Z J,et al.Spectral features of soil organic matter[J].Geomatics and Information Science of Wuhan University,2006,31(11):975-979.何挺,王静,林宗坚,等.土壤有机质光谱特征研究[J].武汉大学学报(信息科学版),2006,31(11):975-979.
[27]Liu L,Shen R P,Ding G X.Studies on the estimation of soil organic matter content based on hyper-spectrum[J].Spectroscopy and Spectral Analysis,2011,31(3):762-766.刘磊,沈润平,丁国香.基于高光谱的土壤有机质含量估算研究[J].光谱学与光谱分析,2011,31(3):762-766.