基于土壤重金属含量指数变化的冠层光谱遥感分析
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摘要
为提高水稻冠层光谱反演水稻田土壤重金属含量的精度,针对实测土壤重金属含量进行信息提取研究,对其分别进行了对数变换、最大值变换和Box-cox变换。在实验分析中,为得到土壤重金属(Cu、Pb、Cd)反演模型,将土壤重金属及其变换数据与冠层光谱及其变换后的数据进行偏最小二乘回归分析,并分析所得模型精度和稳定性。分析结果表明,对测定重金属数据进行变换能够普遍提高模型的精度与稳定性;对属重度污染重金属Pb和中度污染重金属Cd有较好的预测结果,而对未形成污染的Cu则难以形成有效的预测分析。其成果对耕地土壤重金属含量监测评价具有重要的参考价值。
In order to improve the accuracy of rice canopy spectral inversion of soil heavy metals, this study carried out the information extraction of the measured soil heavy metal content, logarithmic transformation, maximum transformation and Box-cox transformation respectively. In the experimental analysis, the partial least squares regression analysis was performed with canopy spectra and transformation data to study the soil heavy metals(Cu, Pb and Cd) inversion models, the accuracy of the model and the level of pollution in the study area were analyzed. The results showed that the accuracy and stability of the model could be generally improved by the changing the measured heavy metal data. In the prediction of heavy metals, it is found that there was a good prediction result for heavy metal Pb and moderately polluted heavy metal Cd in the study area, while it was difficult to form effective analysis for non-polluted Cu. The results are the important reference for monitoring and evaluation of soil heavy metal content in cultivated land.
In order to improve the accuracy of rice canopy spectral inversion of soil heavy metals, this study carried out the information extraction of the measured soil heavy metal content, logarithmic transformation, maximum transformation and Box-cox transformation respectively. In the experimental analysis, the partial least squares regression analysis was performed with canopy spectra and transformation data to study the soil heavy metals(Cu, Pb and Cd) inversion models, the accuracy of the model and the level of pollution in the study area were analyzed. The results showed that the accuracy and stability of the model could be generally improved by the changing the measured heavy metal data. In the prediction of heavy metals, it is found that there was a good prediction result for heavy metal Pb and moderately polluted heavy metal Cd in the study area, while it was difficult to form effective analysis for non-polluted Cu. The results are the important reference for monitoring and evaluation of soil heavy metal content in cultivated land.
引文
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[2]王平.农作物重金属污染胁迫遥感弱信息增强计算[D].长春:东北师范大学,2010.
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[5]朱叶青,屈永华,刘素红,等.重金属铜污染植被光谱响应特征研究[J].遥感学报,2014,18(2):344-352.
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[13]罗力兵,刘湘南,吴伶,等.镉胁迫水稻潜热通量与冠层结构参数变化遥感分析[J].农业环境科学学报,2017,36(5):832-838.
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[18]李继宁,魏源,赵龙,等.锑矿区土壤重金属生物可给性及人体健康风险评估[J].环境工程技术学报,2014,4(5):412-420.
[19]王奕涵,石铁柱,刘会增,等.水稻叶片氮含量反演偏最小二乘模型设计[J].遥感信息,2015,30(6):42-47.
[20]Ren H Y,Zhuang D F,Pan J J,et al.Hyper-spectral Remote Sensing to Monitor Vegetation Stress[J].Journal of Soils and Sediments,2008,8:323-326.
[21]王平.农作物重金属污染胁迫遥感弱信息增强与计算[D].长春:东北师范大学,2010.
[22]David J M.Twenty Five Years of Remote Sensing in Precision Agriculture:Key Advance and Remaining Knowledge Gaps[J].Bio-systems Engineering,2013,114(4):358-371.
[23]黄志平,徐斌,涂德浴,等.规模化猪场废水灌溉农田土壤Pb,Cd和As空间变异及影响因子分析[J].农业工程学报,2008,24(2):77-83.249-255.
[24]Khan S,Cao Q,Zheng Yuanming,et al.Health Risks of Heavy Metals in Contaminated Soils and Food Crops Irrigated with Wastewater in Beijing,China[J].Environmental Pollution,2007,152(3):686-692.
[25]赵利婷.遥感同化WOFOST模型监测水稻重金属胁迫方法研究[D].北京:中国地质大学,2015.
[26]Noh H,Zhang Q,Shin B,et al.A Neural Network Model of Crop Nitrogen Stress Assessment for a MultiSpectral Imaging Sensor[J].Biosystems Engineering,2006,94(4):477-485.
[27]周超.植被重金属含量光谱遥感反演方法研究[D].长春:吉林大学,2016.
[28]金铭.水稻重金属胁迫生理功能变化遥感分析方法研究[D].北京:中国地质大学,2017.
[29]江振蓝,杨玉盛,沙晋明.GWR模型在土壤重金属高光谱预测中的应用[J].地理学报,2017,72(3):533-544.
[2]王平.农作物重金属污染胁迫遥感弱信息增强计算[D].长春:东北师范大学,2010.
[3]袁祖新,马新明,韩锦峰,等.镉污染对烟草叶片超结构及部分元素含量的影响[J].生态学报,2005,25(11):2 919-2 927.
[4]Kooistra L,Leuven R,Wehrens R,et al.A Compare is on of Methods to Relate Grass Reflectance to Soil Metal Contamination[J].International Journal of Remote Sensing,2003,24(24):4 995-5 010.
[5]朱叶青,屈永华,刘素红,等.重金属铜污染植被光谱响应特征研究[J].遥感学报,2014,18(2):344-352.
[6]Baumgardner M F,Stoner E R,Silva L F,et.al.Reflective Properties of soils[A].Newyork:Academic Press,1985.
[7]Shrestha R P.Relating Soil Electrical Conductivity to Remote Sensing and Other Soil Properties for Assessing Soil Salinity in Northeast Thailand[J].Land Degradation&Development,2006,17(6):677-689.
[8]李巨宝,田庆久,吴均昭,等.滏阳河两岸农田土壤Fe、Zn、Se元素光谱响应研究[J].遥感信息,2005,20(3):10-13.
[9]刘美玲.融合多元环境因子的水稻重金属污染水平遥感评估模型[D].北京:中国地质大学,2011.
[10]林婷.水稻锌污染高光谱遥感监测模型研究[D].北京:中国地质大学,2011.
[11]郭云开,周烽松,丁美青,等.水稻冠层与耕地土壤高光谱反演土壤重金属对比研究[J].遥感信息,2017,32(2):173-179.
[12]赵利婷,刘湘南,丁超,等.遥感同化WOFOST模型动态监测水稻重金属污染胁迫[J].农业环境科学学报,2015,34(2):248-256.
[13]罗力兵,刘湘南,吴伶,等.镉胁迫水稻潜热通量与冠层结构参数变化遥感分析[J].农业环境科学学报,2017,36(5):832-838.
[14]郭云开,谢琼,安冠星,等.土壤重金属污染灰色综合评价模型[J].测绘科学,2017,42(5):175-182.
[15]李新会,张秀英,江洪,等.水稻对酸胁迫的高光谱响应研究[J].遥感信息,2014,29(2):94-99.
[16]周烽松.水稻冠层光谱反演水稻的重金属含量研究[D].长沙:长沙理工大学,2017.
[17]何志祥,朱凡,陈永华,等.长株潭矿山土壤重金属的分布及污染评价[J].中南林业科技大学学报,2011,31(4):196-199.
[18]李继宁,魏源,赵龙,等.锑矿区土壤重金属生物可给性及人体健康风险评估[J].环境工程技术学报,2014,4(5):412-420.
[19]王奕涵,石铁柱,刘会增,等.水稻叶片氮含量反演偏最小二乘模型设计[J].遥感信息,2015,30(6):42-47.
[20]Ren H Y,Zhuang D F,Pan J J,et al.Hyper-spectral Remote Sensing to Monitor Vegetation Stress[J].Journal of Soils and Sediments,2008,8:323-326.
[21]王平.农作物重金属污染胁迫遥感弱信息增强与计算[D].长春:东北师范大学,2010.
[22]David J M.Twenty Five Years of Remote Sensing in Precision Agriculture:Key Advance and Remaining Knowledge Gaps[J].Bio-systems Engineering,2013,114(4):358-371.
[23]黄志平,徐斌,涂德浴,等.规模化猪场废水灌溉农田土壤Pb,Cd和As空间变异及影响因子分析[J].农业工程学报,2008,24(2):77-83.249-255.
[24]Khan S,Cao Q,Zheng Yuanming,et al.Health Risks of Heavy Metals in Contaminated Soils and Food Crops Irrigated with Wastewater in Beijing,China[J].Environmental Pollution,2007,152(3):686-692.
[25]赵利婷.遥感同化WOFOST模型监测水稻重金属胁迫方法研究[D].北京:中国地质大学,2015.
[26]Noh H,Zhang Q,Shin B,et al.A Neural Network Model of Crop Nitrogen Stress Assessment for a MultiSpectral Imaging Sensor[J].Biosystems Engineering,2006,94(4):477-485.
[27]周超.植被重金属含量光谱遥感反演方法研究[D].长春:吉林大学,2016.
[28]金铭.水稻重金属胁迫生理功能变化遥感分析方法研究[D].北京:中国地质大学,2017.
[29]江振蓝,杨玉盛,沙晋明.GWR模型在土壤重金属高光谱预测中的应用[J].地理学报,2017,72(3):533-544.