受扰土壤全量氮磷钾的光谱反演
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摘要
选取无人为干扰区、人为干扰区共55个土壤样品测定全氮磷钾含量,结合实测光谱的微分变换建立预测模型,并筛选出不同人为干扰下最优模型。结果表明:人类活动造成土壤全氮磷钾含量降低,数据集中程度和分布状态均发生变化。无人为干扰区以二阶微分建立的全氮、全钾含量预测模型和全磷以对数的倒数一阶微分建立的模型建模集r~2均超过0.9,检验集相对分析误差(RPD)均大于2.0,预测效果最佳。人为干扰区以倒数一阶微分建立的全氮、全磷含量预测模型不仅RPD>2.0,而且r~2> 0.92,也达到极好水平。入选RPD> 2预测模型的敏感波段中,无人为干扰区均位于可见光近红外波段,而人为干扰区则在近红外波段。这为今后提高全氮磷钾含量预测精度提供了新视角。
Total nitrogen,phosphorus and potassium in 55 soil samples from unmanned interference area and human interference area were determined. The prediction models were established based on the differential transformation of the measured spectrums,and the best models under different human interference were screened out. The results showed that the total amount of nitrogen,phosphorus and potassium in soil decreased due to human activities,and the concentration and distribution of data varied. In the unmanned interference area,the model sets r~2 of the total nitrogen and potassium prediction models established by second-order differential and the first-order derivative of the inverse of the logarithm of total phosphorus were more than 0.9,and the test sets RPD were over 2.0.These models have better prediction results.The total nitrogen and phosphorus prediction models established by the reciprocal first-order differential in the human interference area not only achieve an excellent level of RPD>2.0 but also r~2>0.92. In the sensitive bands selected for the RPD>2 prediction models,the unmanned interference area were located in the visible near-infrared band while the human interference area were in the near infrared band. This provides a new perspective for improving the prediction accuracy of total nitrogen,phosphorus and potassium in the future.
Total nitrogen,phosphorus and potassium in 55 soil samples from unmanned interference area and human interference area were determined. The prediction models were established based on the differential transformation of the measured spectrums,and the best models under different human interference were screened out. The results showed that the total amount of nitrogen,phosphorus and potassium in soil decreased due to human activities,and the concentration and distribution of data varied. In the unmanned interference area,the model sets r~2 of the total nitrogen and potassium prediction models established by second-order differential and the first-order derivative of the inverse of the logarithm of total phosphorus were more than 0.9,and the test sets RPD were over 2.0.These models have better prediction results.The total nitrogen and phosphorus prediction models established by the reciprocal first-order differential in the human interference area not only achieve an excellent level of RPD>2.0 but also r~2>0.92. In the sensitive bands selected for the RPD>2 prediction models,the unmanned interference area were located in the visible near-infrared band while the human interference area were in the near infrared band. This provides a new perspective for improving the prediction accuracy of total nitrogen,phosphorus and potassium in the future.
引文
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[25]曹璞,潘涛,陈星旦.小型近红外玉米蛋白质成分分析仪器设计的波段选择[J].光学精密工程,2007,15(12):1952-1958.
[26]Viscarra R V,McGlyn R N,McBratney A B.Determing the composition of mineral-organic mixes using UV-vis-NIR diffuse Reflectance spectroscopy[J].Geoderma,2006,137(1/2):70-82.
[27]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.
[28]侯艳军,塔西甫拉提·特依拜,买买提·沙吾提,等.荒漠土壤有机质含量高光谱估算模型[J].农业工程学报,2014,30(16):113-120.
[29]陶兰花,塔西甫拉提·特依拜,姜红涛,等.克里雅河流域土壤盐分光谱定量分析[J].中国沙漠,2014,34(6):1562-1567.
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[2]Ben-Dor E,Banin A.Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties[J].Soil Science Society of America Journal,1995,59(2):364-372.
[3]屈晓晖,庄大方,彭望碌,等.基于ANN分类的农田遥感动态监测模型研究[J].自然资源学报,2007(2):193-197.
[4]张春桂,张星,陈敏艳,等.福建近岸海域悬浮泥沙浓度遥感定量监测研究[J].自然资源学报,2008(1):150-160.
[5]Kinoshita R,Bianca N,Moebius-Clune B N,et al.Strategies for soil quality assessment using visible and near-infrared reflectance spectroscopy in a Western Kenya Chronosequence[J].Soil Science Society of America Journal,2012,76(5):1776-1788.
[6]Vohland M,Ludwig M,Thiele-Bruhn S,et al.Determination of soil properties with visible to near-and mid-infrared spectroscopy:effects of spectral variable selection[J].Geoderma,2014,223-225(1):88-96.
[7]Pietrzykowski M,Chodak M.Near infrared spectroscopy-a tool for chemical properties and organic matter assessment of afforested mine soils[J].Ecological Engineering,2014,62(1):115-122.
[8]Kuang B,Mouazen A M.Non-biased prediction of soil organic carbon and total nitrogen with vis-NIR spectroscopy,as affected by soil moisture content and texture[J].Biosystems Engineering,2013,114(3):249-258.
[9]王璐,蔺启忠,贾东,等.多光谱数据定量反演土壤营养元素含量可行性分析[J].环境科学,2007,28(8):1822-1828.
[10]李颉,张小超,苑严伟,等.北京典型耕作土壤养分的近红外光谱分析[J].农业工程学报,2012,28(2):176-179.
[11]陈鹏飞,刘良云,王纪华,等.近红外光谱技术实时测定土壤中总氮及磷含量的初步研究[J].光谱学与光谱分析,2008,28(2):295-298.
[12]卢艳丽,白由路,王磊,等.黑土土壤中全氮含量的高光谱预测分析[J].农业工程学报,2010,26(1):256-261.
[13]王磊,顾晓鹤,王宝山,等.基于HJ-1A超光谱影像的县域尺度耕地土壤速效磷含量遥感制图研究[J].土壤通报,2015,46(6):1314-1320.
[14]夏楠,塔西甫拉提·特依拜,丁建丽,等.基于多光谱数据的荒漠矿区土壤有机质估算模型[J].农业工程学报,2016,32(6)263-267.
[15]秦山,潮洛濛.人为干扰对乌海市四合木小灌木景观的影响[J].生态学报,2014,34(21):6346-6354.
[16]李晓明,韩霁昌,李娟.典型半干旱区土壤盐分高光谱特征反演[J].光谱学与光谱分析,2014,34(4):1081-1084.
[17]段鹏程,熊黑钢,李荣荣,等.不同干扰程度的盐渍土与其光谱反射特征定量分析[J].光谱学与光谱分析,2017,37(2):571-576.
[18]Tsai F A,William P.A derivative-aided hyperspectral image analysis system for land-cover classification[J].IEEE Transactions on Geoscience and Remote Sensing,2002,10(2):416-425.
[19]Tsai F,William P.Derivative analysis of hyperspectral data[J].Remote Sensing of Environment,1998,66(1):41-51.
[20]Cloutis E A.Hyperspectral geological remote sensing:evaluation of analytical techniques[J].International Journal of Remote Sensing,1996,17(12):2215-2242.
[21]Huang Z,Turner B J,Dury S J,et al.Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis[J].Remote Sensing of Environment,2004,93(1/2):18-29.
[22]Kemper T,Sommer S.Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy[J].Environmental Sciences Technol,2002,36(12):2742.
[23]Serrano L,Penuelas J,Ustin S L.Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data:decomposing biochemical from structural signals[J].Remote Sensing of Environment,2002,81(2):355-364.
[24]黄敬峰,王福民,王秀珍.水稻高光谱遥感实验研究[M].杭州:浙江大学出版社,2010:22.
[25]曹璞,潘涛,陈星旦.小型近红外玉米蛋白质成分分析仪器设计的波段选择[J].光学精密工程,2007,15(12):1952-1958.
[26]Viscarra R V,McGlyn R N,McBratney A B.Determing the composition of mineral-organic mixes using UV-vis-NIR diffuse Reflectance spectroscopy[J].Geoderma,2006,137(1/2):70-82.
[27]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.
[28]侯艳军,塔西甫拉提·特依拜,买买提·沙吾提,等.荒漠土壤有机质含量高光谱估算模型[J].农业工程学报,2014,30(16):113-120.
[29]陶兰花,塔西甫拉提·特依拜,姜红涛,等.克里雅河流域土壤盐分光谱定量分析[J].中国沙漠,2014,34(6):1562-1567.
[30]史舟.土壤地面高光谱遥感原理与方法[M].北京:科学出版社,2014:36.
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