不同盐结皮光谱特征及其盐渍化信息预测研究
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摘要
盐碱地是我国重要的后备耕地资源,为及时、准确获取土壤盐渍化信息,以宁夏银北地区不同盐结皮土壤为研究对象,运用土壤学和地统计学方法,以土壤野外原位光谱数据和室内盐分指标测定数据为基本信息源,系统分析不同盐结皮光谱特征,确定对结皮层pH值、电导率(EC)和盐分离子含量最敏感的土壤光谱反射率转换形式、波段和光谱盐分指数,进而建立并验证基于敏感盐分指数的盐分指标预测模型。结果表明:研究区白碱结皮光谱反射率在450～1 050 nm波段最高,马尿碱结皮次之,黑油碱结皮最低。通过野外光谱反射率可以将研究区主要盐结皮类型进行分类。反射率平滑后再经过一阶微分、倒数对数一阶微分、连续统去除和连续统去除一阶微分转换后,最大相关系数比只作平滑处理的反射率显著增大。与pH值、EC和CO2-3、Mg2+含量相关性最强的转换方式是连续统去除一阶微分,与SO2-4、Ca2+、K+含量相关性最强的转换方式是倒数对数一阶微分,与HCO-3、Cl-、Na+含量相关性最强的转换方式是一阶微分。整体上连续统去除一阶微分与各盐分指标的相关性最强。整体来看,盐分敏感区域在蓝光450、470、485 nm附近,绿光501、575 nm附近,红光680 nm附近和近红外多个波段。与各盐分指标相关性最强的盐分指数分别为:pH值为盐分指数S1,Cl-、K+含量为盐分指数SI3,SO2-4、Mg2+含量为盐分指数S2,EC和HCO-3、Na+含量为盐分指数S3,CO2-3、Ca2+含量为盐结皮指数SCI。除CO2-3含量外,利用敏感盐分指数可以准确预测土壤结皮层pH值、EC和其他盐分离子含量,其中对Na+含量的拟合度最大。该研究可为银北地区不同盐结皮土壤分类及盐渍化信息的准确预测提供科学依据。
Saline-alkali land is an important reserve land resource in China. Real-time and accurate acquisition of soil information is important for the classification and evaluation of soil salinization to prevent its degradation and realize agriculture sustainable development. Selecting different soil salt crusts in northern Ningxia Yinchuan as the study objects,based on soil science and geostatistics methods,and taking the spectra data of different soil salt crusts and measured soil salinization parameters in 0 ～ 5 cm layer of laboratory as the source of information,the characteristics of spectra reflectance of different salt crusts were analyzed,the sensitive spectral wavelengths or index to pH value,EC and salt ions in crust layer were selected,and then the soil salinization monitoring models were established and confirmed.Results showed that the spectral reflectance of white alkali crust was the highest among different soil saline crusts; the reflectance of equine caustic crust was next,and the reflectance of black alkali crustwas the lowest. The main salt crust types in the study region could be classified by the spectral reflectance of the field. The highest correlation coefficients between the transformations of smoothing reflectance through the first order differential,the first derivate differential of logarithmic reciprocal of reflectance,continuum removal,the first derivative of continuum removal and salinity parameters were significantly improved than the transformation of smoothing reflectance gradually. The best transformation method of reflectance about soil pH value,EC,CO2-3 and Mg2 +were the first derivative of continuum removal; the best transformation method of reflectance about soil SO2-4,Ca2 +,K+were first derivate differential of logarithmic reciprocal of reflectance; the best transformation method of reflectance about soil HCO-3,Cl-and Na+were the first order differential. There was the strongest correlation between the first derivative of continuum removal and salinity parameters. On the whole from different salinity parameters,the sensitive wavelength was 450 nm,470 nm and 485 nm in blue region; 501 nm and575 nm in green region; 680 nm in red region; there were many sensitive wavelengths in infrared region.The highest correlation coefficients between pH value,EC,CO2-3,HCO-3,Cl-,SO2-4,Ca2 +,Mg2 +,K+and Na+and nine salinity indexes was S1(Salinity index),S3(Salinity index),SCI(Soil curst index),S3,SI3(Salinity index 3),S2(Salinity index),SCI,S2(Salinity index),SI3 and S3,respectively. Except for CO2-3,the models were suitable for predicting the content of soil pH value,EC,and other salinity parameters,and there was the highest R2 about Na+in this region. The study would provide some beneficial references for regional soil salinity classification and prediction.
Saline-alkali land is an important reserve land resource in China. Real-time and accurate acquisition of soil information is important for the classification and evaluation of soil salinization to prevent its degradation and realize agriculture sustainable development. Selecting different soil salt crusts in northern Ningxia Yinchuan as the study objects,based on soil science and geostatistics methods,and taking the spectra data of different soil salt crusts and measured soil salinization parameters in 0 ～ 5 cm layer of laboratory as the source of information,the characteristics of spectra reflectance of different salt crusts were analyzed,the sensitive spectral wavelengths or index to pH value,EC and salt ions in crust layer were selected,and then the soil salinization monitoring models were established and confirmed.Results showed that the spectral reflectance of white alkali crust was the highest among different soil saline crusts; the reflectance of equine caustic crust was next,and the reflectance of black alkali crustwas the lowest. The main salt crust types in the study region could be classified by the spectral reflectance of the field. The highest correlation coefficients between the transformations of smoothing reflectance through the first order differential,the first derivate differential of logarithmic reciprocal of reflectance,continuum removal,the first derivative of continuum removal and salinity parameters were significantly improved than the transformation of smoothing reflectance gradually. The best transformation method of reflectance about soil pH value,EC,CO2-3 and Mg2 +were the first derivative of continuum removal; the best transformation method of reflectance about soil SO2-4,Ca2 +,K+were first derivate differential of logarithmic reciprocal of reflectance; the best transformation method of reflectance about soil HCO-3,Cl-and Na+were the first order differential. There was the strongest correlation between the first derivative of continuum removal and salinity parameters. On the whole from different salinity parameters,the sensitive wavelength was 450 nm,470 nm and 485 nm in blue region; 501 nm and575 nm in green region; 680 nm in red region; there were many sensitive wavelengths in infrared region.The highest correlation coefficients between pH value,EC,CO2-3,HCO-3,Cl-,SO2-4,Ca2 +,Mg2 +,K+and Na+and nine salinity indexes was S1(Salinity index),S3(Salinity index),SCI(Soil curst index),S3,SI3(Salinity index 3),S2(Salinity index),SCI,S2(Salinity index),SI3 and S3,respectively. Except for CO2-3,the models were suitable for predicting the content of soil pH value,EC,and other salinity parameters,and there was the highest R2 about Na+in this region. The study would provide some beneficial references for regional soil salinity classification and prediction.
引文
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14 KHAN N M,RASTOSKUEV V V,SATO Y,et al.Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators[J].Agricultural Water Management,2005,77(1):96-109.
15 DOUAOUI A E K,NICOLAS H,WALTER C.Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data[J].Geoderma,2006,134(1):217-230.
16 ALLBED A,KUMAR L,ALDAKHEEL Y Y.Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:application in a date palm dominated region[J].Geoderma,2014,230:1-8.
17 FANG Shibo,YU Weiguo,Qi Yue.Spectra and vegetation index variations in moss soil crust in different seasons,and in wet and dry conditions[J].International Journal of Applied Earth Observation and Geoinformation,2015,38:261-266.
18王飞,丁建丽,魏阳,等.基于Landsat系列数据的盐分指数和植被指数对土壤盐度变异性的响应分析:以新疆天山南北典型绿洲为例[J].生态学报,2017,37(15):5007-5022.WANG Fei,DING Jianli,WEI Yang,et al.Sensitivity analysis of soil salinity and vegetation indices to detect soil salinity variation by using Landsat series images:applications in different oases in Xinjiang,China[J].Acta Ecologica Sinica,2017,37(15):5007-5022.(in Chinese)
19 DEHAAN R L,TAYLOR G R.Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization[J].Remote Sensing of Environment,2002,80:406-417.
20 ZHANG T T,QI J G,GAO Y,et al.Detecting soil salinity with MODIS time series VI data[J].Ecol.Indic.,2015,52:480-489.
21蒋来临,张惠文,马军.盐碱地的改良和利用[J].新疆农业科技,1996(5):25-26.
22颜安,蒋平安,武红旗,等.基于决策树方法的罗布泊“大耳朵”干盐湖区盐壳分类研究[J].遥感信息,2009(2):57-63,75.YAN An,JIANG Ping'an,WU Hongqi,et al.A study on salt crust classification based on decision tree technology in Lop Nur“Great Ear”dry salt lake area[J].Remote Sensing Information,2009(2):57-63,75.(in Chinese)
23 DE JONG S M,ADDINK E A,WAN BEEK L P H,et al.Physical characterization,spectral response and remotely sensed mapping of Mediterranean soil surface crusts[J].Catena,2011,86:24-35.
24马黎春.罗布泊干盐湖盐壳光谱特征初步研究[J].矿床地质,2010,29(增刊):387-388.
25王爽,丁建丽,王璐,等.基于地表光谱建模的区域土壤盐渍化遥感监测研究[J].干旱区地理,2016,39(1):190-198.WANG Shuang,DING Jianli,WANG Lu,et al.Remote sensing monitoring of soil salinization based on surface spectral modeling[J].Arid Land Geography,2016,39(1):190-198.(in Chinese)
26 ROSSEL R A V,BEHRENS T,BEN-DOR E,et al.A global spectral library to characterize the world's soil[J].Earth-Science Reviews,2016,155:198-230.
27 METTEMICHT G I,ZINCK J A.Remote sensing of soil salinity:potentials and constrains[J].Remote Sensing of Environment,2003,85:1-20.
28 SONG C Y,REN H X,HUANG C.Estimating soil salinity in the Yellow River Delta,Eastern China an integrated approach using spectral and terrain indices with the generalized additive model[J].Pedosphere,2016,26(5):626-635.
29 ZEWDU S,SURYABHAGAVAN K V,BALAKRISHNAN M.Geo-spatial approach for soil salinity mapping in Sego Irrigation Farm,South Ethiopia[J].Journal of the Saudi Society of Agricultural Sciences,2015,16(1):16-24.
30 SUDDUTH K A,KITCHEN N R,SADLER E J,et al.VNIR spectroscopy estimates of within-field variability in soil properties[J].Progress in Soil Science,2010,1(3):153-163.
31张芳,熊黑钢,丁建丽,等.碱化土壤的野外及实验室波谱响应特征及其转换[J].农业工程学报,2012,28(5):101-107.ZHANG Fang,XIONG Heigang,DING Jianli,et al.Characteristics of laboratory-field measured spectra responding to alkalinized soil and conversion[J].Transactions of the CSAE,2012,28(5):101-107.(in Chinese)
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34张俊华,秦君琴,李明.基于土壤光谱特征的宁夏银北地区盐碱地盐分预测研究[J].水土保持通报,2013,33(5):123-129,164.ZHANG Junhua,QIN Junqing,LI Ming.Prediction of soil salt content based on spectral characteristics of soil in Northern Yinchuan City,Ningxia Hui Autonomous Region[J].Bulletin of Soil and Water Conservation,2013,33(5):123-129,164.(in Chinese)
2张建国,徐新文,雷加强,等.极端干旱区咸水滴灌林地盐结皮对土壤蒸发的影响[J].农业工程学报,2010,26(9):34-39.ZHANG Jianguo,XU Xinwen,LEI Jiaqiang,et al.Effects of salt crust on soil evaporation condition with saline-water dripirrigation in extreme arid region[J].Transactions of the CSAE,2010,26(9):34-39.(in Chinese)
3 NIELD J M,NEUMAN M K,O'BRIEN P,et al.Evaporative sodium salt crust development and its wind tunnel derived transport dynamics under variable climatic conditions[J].Aeolian Research,2016,23:51-62.
4 MULLER S J,VAN NIEKERK A.Identification of World View-2 spectral and spatial factors in detecting salt accumulation in cultivated fields[J].Geoderma,2016,273:1-11.
5 FARIFTEH J,VAN DER MEER F,VAN DER MEIJDE M,et al.Spectral characteristics of salt-affected soils:a laboratory experiment[J].Geoderma,2008,145:196-206.
6 HOWARI F M,GOODELL P C,MIYAMOTO S.Spectral properties of salt crusts formed on saline soils[J].Journal of Environmental Qulity,2002,31:1453-1461.
7 SRIVASTAVA R,SETHI M,YADAV R K,et al.Visible-near infrared reflectance spectroscopy for rapid characterization of saltaffected soil in the indo-gangetic plains of haryana,India[J].Journal of the Indian Society of Remote Sensing,2017:45(2):307-315.
8王海江,蒋天池,YUNGER J A,等.基于支持向量机的土壤主要盐分离子高光谱反演模型[J/OL].农业机械学报.2018,49(15):263-270.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20180531&flag=1.DOI:10.6041/j.issn.1000-1298.2018.05.031.WANG Haijiang,JIANG Tianchi,YUNGER J A,et al.Hyperspectral inverse model for soil salt ions based on support vector machine in Xinjiang[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2018,49(15):263-270.(in Chinese)
9鲁如坤.土壤农业化学分析方法[M].北京:中国农业科技出版社,2000.
10胥勤勉,袁桂邦,孙云霞,等.不同时期古黄河三角洲土壤剖面盐渍化特征[J].地理科学,2011,31(8):941-946.XU Qinmian,YUAN Guibang,SUN Yunxia,et al.Characteristics of soil salinization of Huanghe river delta in different periods[J].Scientia Geographica Sinnca,2011,31(8):941-946.(in Chinese)
11王海江,张花玲,任少亭,等.基于高光谱反射特性的土壤水盐状况预测模型研究[J/OL].农业机械学报,2014,45(7):133-138.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20140721&flag=1.DOI:10.6041/j.issn.1000-1298.2014.07.021.WANG Haijiang,ZHANG Hualing,REN Shaoting,et al.Prediction model of soil water-salt based on hyperspectral reflectance characteristics[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2014,45(7):133-138.(in Chinese)
12彭杰,迟春明,向红英,等.基于连续统去除法的土壤盐分含量反演研究[J].土壤学报,2014,51(3):459-469.PENG Jie,CHI Chunming,XIANG Hongying,et al.Inversion of soil salt content based on continuum-removal method[J].Acta Pedologica Sinica,2014,51(3):459-469.(in Chinese)
13 ALLBED A,KUMAR L,ALDAKHEEL Y Y,et al.Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:applications in a date palm dominated region[J].Geoderma,2014,230-231:1-8.
14 KHAN N M,RASTOSKUEV V V,SATO Y,et al.Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators[J].Agricultural Water Management,2005,77(1):96-109.
15 DOUAOUI A E K,NICOLAS H,WALTER C.Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data[J].Geoderma,2006,134(1):217-230.
16 ALLBED A,KUMAR L,ALDAKHEEL Y Y.Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:application in a date palm dominated region[J].Geoderma,2014,230:1-8.
17 FANG Shibo,YU Weiguo,Qi Yue.Spectra and vegetation index variations in moss soil crust in different seasons,and in wet and dry conditions[J].International Journal of Applied Earth Observation and Geoinformation,2015,38:261-266.
18王飞,丁建丽,魏阳,等.基于Landsat系列数据的盐分指数和植被指数对土壤盐度变异性的响应分析:以新疆天山南北典型绿洲为例[J].生态学报,2017,37(15):5007-5022.WANG Fei,DING Jianli,WEI Yang,et al.Sensitivity analysis of soil salinity and vegetation indices to detect soil salinity variation by using Landsat series images:applications in different oases in Xinjiang,China[J].Acta Ecologica Sinica,2017,37(15):5007-5022.(in Chinese)
19 DEHAAN R L,TAYLOR G R.Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization[J].Remote Sensing of Environment,2002,80:406-417.
20 ZHANG T T,QI J G,GAO Y,et al.Detecting soil salinity with MODIS time series VI data[J].Ecol.Indic.,2015,52:480-489.
21蒋来临,张惠文,马军.盐碱地的改良和利用[J].新疆农业科技,1996(5):25-26.
22颜安,蒋平安,武红旗,等.基于决策树方法的罗布泊“大耳朵”干盐湖区盐壳分类研究[J].遥感信息,2009(2):57-63,75.YAN An,JIANG Ping'an,WU Hongqi,et al.A study on salt crust classification based on decision tree technology in Lop Nur“Great Ear”dry salt lake area[J].Remote Sensing Information,2009(2):57-63,75.(in Chinese)
23 DE JONG S M,ADDINK E A,WAN BEEK L P H,et al.Physical characterization,spectral response and remotely sensed mapping of Mediterranean soil surface crusts[J].Catena,2011,86:24-35.
24马黎春.罗布泊干盐湖盐壳光谱特征初步研究[J].矿床地质,2010,29(增刊):387-388.
25王爽,丁建丽,王璐,等.基于地表光谱建模的区域土壤盐渍化遥感监测研究[J].干旱区地理,2016,39(1):190-198.WANG Shuang,DING Jianli,WANG Lu,et al.Remote sensing monitoring of soil salinization based on surface spectral modeling[J].Arid Land Geography,2016,39(1):190-198.(in Chinese)
26 ROSSEL R A V,BEHRENS T,BEN-DOR E,et al.A global spectral library to characterize the world's soil[J].Earth-Science Reviews,2016,155:198-230.
27 METTEMICHT G I,ZINCK J A.Remote sensing of soil salinity:potentials and constrains[J].Remote Sensing of Environment,2003,85:1-20.
28 SONG C Y,REN H X,HUANG C.Estimating soil salinity in the Yellow River Delta,Eastern China an integrated approach using spectral and terrain indices with the generalized additive model[J].Pedosphere,2016,26(5):626-635.
29 ZEWDU S,SURYABHAGAVAN K V,BALAKRISHNAN M.Geo-spatial approach for soil salinity mapping in Sego Irrigation Farm,South Ethiopia[J].Journal of the Saudi Society of Agricultural Sciences,2015,16(1):16-24.
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