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
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摘要
基于不同地理区域,借助目前已有或者构建新的盐分和植被指数定量评估研究区的土壤盐度状况。但多数指数并未在盐渍化较为严重的中国新疆地区进行系统性对比分析。因此,以新疆阜北地区(采样数=37),玛纳斯河绿洲(采样数=68)和渭干河-库车河绿洲(采样数=38)为研究区,以灌区农田和盐渍地采样数据和Landsat TM/ETM+/OLI为数据源,利用线性模型和多个非线性模型(10个)测试上述指数(14个指数)和原始波段对于研究区土壤盐度的敏感性。结果显示,阜北地区基于遥感获取的扩展的增强型植被指数Extented Enhanced Vegetation Index(EEVI)在全样本和部分样本(盐渍化样本,土壤盐度>0.3%)两种模式下(0—10cm),较其他指数和波段而言较为敏感。在全样本和部分样本(土壤饱和溶液电导率<2ds/m)两种模式下,与玛纳斯流域各层土壤盐度最为敏感的为band 2,部分样本模式下土壤盐度变异性显著性探测最大下探深度为30cm。渭干河-库车河绿洲全样本模式下,最大土壤盐度变异性显著性探测深度为40cm,0—10cm和10—20cm深度表现最为敏感的是土壤盐分指数SI-T,20—40cm深度则为植被指数TGDVI。部分样本下(土壤饱和溶液电导率>2ds/m),0—10cm深度最为敏感的为band5,10—20cm深度最为敏感的为TGDVI,20—40cm深度则为EEVI。其他指数因地理环境的差异性(气候,土壤盐分类型,土壤类型,采样时间),与土壤盐度之间并未达到显著性(sig=0.05或者0.01)的水平。以上结果只是初步结论,但也暗示其中的某些指数在本区具有一定土壤盐度的识别潜力。此外,由于土壤本身的复杂性,需要采集更多的样本以深入分析不同盐度等级下上述指数的具体表现。
Several indices of vegetation and soil salinity have been developed to quantitatively evaluate soil salinization. Thisstudy was conducted to assess the soil salinity levels in the Fubei region( FG),Manas River Basin( MRB),and WeriganKuqa River Delta Oasis( WKRDO),which are distributed in the northern and southern Tianshan Mountains in Xinjiang,China. Ground measurements and remote sensing data were used to evaluate the sensitivity of vegetation and soil salinity indices to soil salinity variation in farmland and salt-affected land. A random sampling approach was used to collect soil samples from FG( n = 37,only at 0—10-cm depth),MRB( n = 58),and WKRDO( n = 38). A total of 14 broadband indices encompassing vegetation and soil salinity indices were extracted from Landsat images. The correlation coefficient based on linear and non-linear models( 10 models) between these indices,Landsat bands,and soil salinity was examined.The results showed that the extended enhanced vegetation index( EEVI) was the most effective for explaining the soil salinity variation at depths of 0—10 cm in two modes( all samples and partial samples with soil salinity( soil salt content)>0.3%) in FG. With the mode of all samples and partial samples( soil electric conductivity < 2 d S/m) in MRB,band 2yielded the best results for assessing the soil salinity of cultivated lands at the early stage of crop growth in April. The maximum depth of the significance test by using indices for detecting variation of soil salinity in this area was 30 cm. For all samples in WKRDO,the salinity index( SI-T) interpreted more variation of soil salinity than that by other indices at depths of 0—10 and 10—20 cm,and the three-band maximal gradient difference index( TGDVI) exhibited the highest signicant correlation with salinity at 20—40 cm. In the mode of partial samples( soil salinity >2 d S/m),the most sensitive index for variation of soil salinity at 0—10,10—20,and 20—40 cm were band 5,TGDVI,and EEVI. In addition,the correlation of other indices( excluding those mentioned above) and soil salinity was highly dependent on land cover heterogeneity and sample period,and showed no significant relationships( p > 0.05 or p > 0.01). These results are preliminary conclusions,but in general,the soil salinity in Xinjiang dominated by different salt types was successfully assessed by broadband vegetation and soil salinity indices extracted from the Landsat images. However,relationships between remote sensing indices and soil salinity within elds are highly complex and require further investigation with additional samples and by using various soil salinity classifications.
Several indices of vegetation and soil salinity have been developed to quantitatively evaluate soil salinization. Thisstudy was conducted to assess the soil salinity levels in the Fubei region( FG),Manas River Basin( MRB),and WeriganKuqa River Delta Oasis( WKRDO),which are distributed in the northern and southern Tianshan Mountains in Xinjiang,China. Ground measurements and remote sensing data were used to evaluate the sensitivity of vegetation and soil salinity indices to soil salinity variation in farmland and salt-affected land. A random sampling approach was used to collect soil samples from FG( n = 37,only at 0—10-cm depth),MRB( n = 58),and WKRDO( n = 38). A total of 14 broadband indices encompassing vegetation and soil salinity indices were extracted from Landsat images. The correlation coefficient based on linear and non-linear models( 10 models) between these indices,Landsat bands,and soil salinity was examined.The results showed that the extended enhanced vegetation index( EEVI) was the most effective for explaining the soil salinity variation at depths of 0—10 cm in two modes( all samples and partial samples with soil salinity( soil salt content)>0.3%) in FG. With the mode of all samples and partial samples( soil electric conductivity < 2 d S/m) in MRB,band 2yielded the best results for assessing the soil salinity of cultivated lands at the early stage of crop growth in April. The maximum depth of the significance test by using indices for detecting variation of soil salinity in this area was 30 cm. For all samples in WKRDO,the salinity index( SI-T) interpreted more variation of soil salinity than that by other indices at depths of 0—10 and 10—20 cm,and the three-band maximal gradient difference index( TGDVI) exhibited the highest signicant correlation with salinity at 20—40 cm. In the mode of partial samples( soil salinity >2 d S/m),the most sensitive index for variation of soil salinity at 0—10,10—20,and 20—40 cm were band 5,TGDVI,and EEVI. In addition,the correlation of other indices( excluding those mentioned above) and soil salinity was highly dependent on land cover heterogeneity and sample period,and showed no significant relationships( p > 0.05 or p > 0.01). These results are preliminary conclusions,but in general,the soil salinity in Xinjiang dominated by different salt types was successfully assessed by broadband vegetation and soil salinity indices extracted from the Landsat images. However,relationships between remote sensing indices and soil salinity within elds are highly complex and require further investigation with additional samples and by using various soil salinity classifications.
引文
[1]王芳芳,吴世新,乔木,李和平,杨涵,李义玲.基于3S技术的新疆耕地盐渍化状况调查与分析.干旱区研究,2009,26(3):365-371.
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[8]Tilley D R,Ahmed M,Son J H,Badrinarayanan H.Hyperspectral reflectance response of freshwater macrophytes to salinity in a brackish subtropical marsh.Journal of Environmental Quality,2007,36(3):780-789.
[9]Wang D,Poss J A,Donovan T J,Shannon M C,Lesch S M.Biophysical properties and biomass production of elephant grass under saline conditions.Journal of arid Environment,2002,52(4):447-456.
[10]Brunner P,Li H T,Kinzelbach W,Li W P.Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data.International Journal of Remote Sensing,2007,28(15):3341-3361.
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[12]Allbed A,Kumar L,Aldakheel Y Y Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:Applications in a date palm dominated region.Geoderma,2014,230-231:1-8.
[13]Wu W.The Generalized Difference Vegetation Index(GDVI)for Dryland Characterization.Remote Sensing,2014,6(2):1211-1233.
[14]Scudiero E,Skaggs T H,Corwin D L.Regional-scale soil salinity assessment using Landsat ETM+canopy reflectance.Remote Sensing of Environment,2015,169:335-343.
[15]Scudiero E,Skaggs T H,Corwin D L.Regional scale soil salinity evaluation using Landsat 7,western San Joaquin Valley,California,USA.Geoderma Regional,2014,2-3:82-90.
[16]Fernández-Buces N,Siebe C,Cram S,Palacio J L.Mapping soil salinity using a combined spectral response index for bare soil and vegetation:A case study in the former lake Texcoco,Mexico.Journal of Arid Environments,2006,65(4):644-667.
[17]陈红艳,赵庚星,陈敬春,王瑞燕,高明秀.基于改进植被指数的黄河口区盐渍土盐分遥感反演.农业工程学报,2015,31(5):107-114.
[18]Zhang C,Lu D S,Chen X,Zhang Y M,Maisupova B,Tao Y.The spatiotemporal patterns of vegetation coverage and biomass of the temperate deserts in Central Asia and their relationships with climate controls.Remote Sensing of Environment,2016,175:271-281.
[19]Zhang T T,Qi J G,Gao Y,Ouyang Z T,Zeng S L,Zhao B.Detecting soil salinity with MODIS time series VI data.Ecological Indicators,2015,52:480-489.
[20]Jiapaer G,Chen X,Bao A M.A comparison of methods for estimating fractional vegetation cover in arid regions.Agricultural and Forest Meteorology,2011,151(12):1698-1710.
[21]Wang Y G,Li Y.Land exploitation resulting in soil salinization in a desert-oasis ecotone.Catena,2013,100:50-56.
[22]Sheng J D,Ma L C,Jiang P A,Li B G,Huang F,Wu H Q.Digital soil mapping to enable classification of the salt-affected soils in desert agroecological zones.Agricultural Water Management,2010,97(12):1944-1951.
[23]江红南,丁建丽,塔西甫拉提·特依拜,赵睿,张飞.基于ETM+数据的干旱区盐渍化土壤信息提取研究.土壤学报,2008,45(2):222-228.
[24]Li X M,Yang J S,Liu M X,Liu G M,Yu M.Spatio-temporal changes of soil salinity in arid areas of south Xinjiang using electromagnetic induction.Journal of Integrative Agriculture,2012,11(8):1365-1376.
[25]徐涵秋,唐菲.新一代Landsat系列卫星:Landsat 8遥感影像新增特征及其生态环境意义.生态学报,2013,33(11):3249-3257.
[26]蒲智,于瑞德,尹昌应,陆亦农.干旱区典型盐碱土壤含盐量估算的最佳高光谱指数研究.水土保持通报,2012,32(6):129-133.
[27]Wang R S,Wan S Q,Kang Y H,Dou C Y.Assessment of secondary soil salinity prevention and economic benefit under different drip line placement and irrigation regime in northwest China.Agricultural Water Management,2014,131:41-49.
[28]樊自立,乔木,徐海量,张青青,李和平,张鹏,周生斌,卢磊.合理开发利用地下水是新疆盐渍化耕地改良的重要途径.干旱区研究,2011,28(5):737-743.
[29]Ding J L,Yu D L.Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan-Kuqa Oasis,China,using remote sensing and electromagnetic induction instruments.Geoderma,2014,235-236:316-322.
[30]Dehni A,Lounis M.Remote sensing techniques for salt affected soil mapping:Application to the Oran region of Algeria.Procedia Engineering,2012,33:188-198.
[31]Darvishzadeh R,Atzberger C,Skidmore A K,Abkar A A.Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position.International Journal of Remote Sensing,2009,30(23):6199-6218.
[32]Csillag F,Pásztor L,Biehl L L.Spectral band selection for the characterization of salinity status of soils.Remote Sensing of Environment,1993,43(3):231-242.
[33]Melendez-Pastor I,Navarro-Pedre1o J,Gómez I,Koch M.Identifying optimal spectral bands to assess soil properties with VNIR radiometry in semiarid soils.Geoderma,2008,147(3/4):126-132.
[2]Wang F,Chen X,Luo G P,Han Q F.Mapping of regional soil salinities in Xinjiang and strategies for amelioration and management.Chinese Geographical Science,2015,25(3):321-336.
[3]Grunwald S,Thompson J A,Boettinger J L.Digital soil mapping and modeling at continental scales:finding solutions for global issues.Soil Science Society of America Journal,2011,75(4):1201-1213.
[4]Allbed L,Kumar,L,Aldakheel.Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:Applications in a date palm dominated region.Geoderma,2014,230-231(17):1-8.
[5]Khan N M,Rastoskuev V V,Sato Y,Shiozawa S.Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators.Agricultural Water Management,2005,77(1/3):96-109.
[6]Douaoui A E K,Nicolas H,Walter C.Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data.Geoderma,2006,134(1/2):217-230.
[7]Abbas A,Khan S.Using remote sensing techniques for appraisal of irrigated soil salinity//Paper presented at the Advances and Applications for Management and Decision Making Land,Water and Environmental Management:Integrated Systems for Sustainability MODSIM07.Brighton:MODSIM,2007.
[8]Tilley D R,Ahmed M,Son J H,Badrinarayanan H.Hyperspectral reflectance response of freshwater macrophytes to salinity in a brackish subtropical marsh.Journal of Environmental Quality,2007,36(3):780-789.
[9]Wang D,Poss J A,Donovan T J,Shannon M C,Lesch S M.Biophysical properties and biomass production of elephant grass under saline conditions.Journal of arid Environment,2002,52(4):447-456.
[10]Brunner P,Li H T,Kinzelbach W,Li W P.Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data.International Journal of Remote Sensing,2007,28(15):3341-3361.
[11]Lobell D B,Lesch S M,Corwin D L,Ulmer M G,Anderson K A,Potts D J,Doolittle J A,Matos M R,Baltes M J.Regional-scale Assessment of Soil Salinity in the Red River Valley Using Multi-year MODIS EVI and NDVI.Journal of Environmental Quality,2009,39(1):35-41.
[12]Allbed A,Kumar L,Aldakheel Y Y Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:Applications in a date palm dominated region.Geoderma,2014,230-231:1-8.
[13]Wu W.The Generalized Difference Vegetation Index(GDVI)for Dryland Characterization.Remote Sensing,2014,6(2):1211-1233.
[14]Scudiero E,Skaggs T H,Corwin D L.Regional-scale soil salinity assessment using Landsat ETM+canopy reflectance.Remote Sensing of Environment,2015,169:335-343.
[15]Scudiero E,Skaggs T H,Corwin D L.Regional scale soil salinity evaluation using Landsat 7,western San Joaquin Valley,California,USA.Geoderma Regional,2014,2-3:82-90.
[16]Fernández-Buces N,Siebe C,Cram S,Palacio J L.Mapping soil salinity using a combined spectral response index for bare soil and vegetation:A case study in the former lake Texcoco,Mexico.Journal of Arid Environments,2006,65(4):644-667.
[17]陈红艳,赵庚星,陈敬春,王瑞燕,高明秀.基于改进植被指数的黄河口区盐渍土盐分遥感反演.农业工程学报,2015,31(5):107-114.
[18]Zhang C,Lu D S,Chen X,Zhang Y M,Maisupova B,Tao Y.The spatiotemporal patterns of vegetation coverage and biomass of the temperate deserts in Central Asia and their relationships with climate controls.Remote Sensing of Environment,2016,175:271-281.
[19]Zhang T T,Qi J G,Gao Y,Ouyang Z T,Zeng S L,Zhao B.Detecting soil salinity with MODIS time series VI data.Ecological Indicators,2015,52:480-489.
[20]Jiapaer G,Chen X,Bao A M.A comparison of methods for estimating fractional vegetation cover in arid regions.Agricultural and Forest Meteorology,2011,151(12):1698-1710.
[21]Wang Y G,Li Y.Land exploitation resulting in soil salinization in a desert-oasis ecotone.Catena,2013,100:50-56.
[22]Sheng J D,Ma L C,Jiang P A,Li B G,Huang F,Wu H Q.Digital soil mapping to enable classification of the salt-affected soils in desert agroecological zones.Agricultural Water Management,2010,97(12):1944-1951.
[23]江红南,丁建丽,塔西甫拉提·特依拜,赵睿,张飞.基于ETM+数据的干旱区盐渍化土壤信息提取研究.土壤学报,2008,45(2):222-228.
[24]Li X M,Yang J S,Liu M X,Liu G M,Yu M.Spatio-temporal changes of soil salinity in arid areas of south Xinjiang using electromagnetic induction.Journal of Integrative Agriculture,2012,11(8):1365-1376.
[25]徐涵秋,唐菲.新一代Landsat系列卫星:Landsat 8遥感影像新增特征及其生态环境意义.生态学报,2013,33(11):3249-3257.
[26]蒲智,于瑞德,尹昌应,陆亦农.干旱区典型盐碱土壤含盐量估算的最佳高光谱指数研究.水土保持通报,2012,32(6):129-133.
[27]Wang R S,Wan S Q,Kang Y H,Dou C Y.Assessment of secondary soil salinity prevention and economic benefit under different drip line placement and irrigation regime in northwest China.Agricultural Water Management,2014,131:41-49.
[28]樊自立,乔木,徐海量,张青青,李和平,张鹏,周生斌,卢磊.合理开发利用地下水是新疆盐渍化耕地改良的重要途径.干旱区研究,2011,28(5):737-743.
[29]Ding J L,Yu D L.Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan-Kuqa Oasis,China,using remote sensing and electromagnetic induction instruments.Geoderma,2014,235-236:316-322.
[30]Dehni A,Lounis M.Remote sensing techniques for salt affected soil mapping:Application to the Oran region of Algeria.Procedia Engineering,2012,33:188-198.
[31]Darvishzadeh R,Atzberger C,Skidmore A K,Abkar A A.Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position.International Journal of Remote Sensing,2009,30(23):6199-6218.
[32]Csillag F,Pásztor L,Biehl L L.Spectral band selection for the characterization of salinity status of soils.Remote Sensing of Environment,1993,43(3):231-242.
[33]Melendez-Pastor I,Navarro-Pedre1o J,Gómez I,Koch M.Identifying optimal spectral bands to assess soil properties with VNIR radiometry in semiarid soils.Geoderma,2008,147(3/4):126-132.