西河流域不同海拔区土壤有效钾的高光谱反演
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摘要
为探究不同海拔条件对土壤有效钾含量高光谱反演的影响以及筛选效果最好的光谱指标。采集118个土壤样本后进行其室内理化分析、光谱测量与处理等一系列工作,在土壤原始光谱(R)处理的基础上提取了反射率倒数一阶微分((1/R)')、反射率倒数的对数一阶微分((log(1/R))')和反射率对数的倒数一阶微分((1/(log R))')三种光谱变换指标,分析土壤原始光谱和三种变换后的光谱指标与不同海拔区土壤有效钾含量的相关性,并运用偏最小二乘回归法(PLSR)建立不同海拔条件下土壤有效钾的高光谱预测模型。结果表明:(1)比较土壤原始光谱和三种变换后的光谱指标,基于(log(1/R))'变换结果构建的PLSR模型在土壤有效钾的反演效果最好,其决定系数(R2)最高,为0.89,均方根误差(RMSE)为12.45 mg kg-1;(2)相比全区域而言,依据海拔分区所建立的模型能够更好的预测土壤有效钾的含量。该结果对今后地形复杂区域土壤养分的光谱预测具有一定的指导作用。
In order to analyze the influence of altitude conditions on the hyperspectral inversion of soil available potassium(AK) and to select the best spectral indicators, 118 soil samples were collected in three sub-regions that divided by altitudes and their AK concentrations and spectral data were measured. After that, the relationships between the concentrations of soil AK and the four spectral indices, including the raw spectral reflectance(R),first-order differential from reciprocal reflectance(1/R)', first-order differential from logarithm of reciprocal reflectance(log(1/R))' and first-order differential from reciprocal of logarithm reflectance(1/(log R))', were conducted in the whole region and the three sub-regions. Furthermore, partial least-squares regression(PLSR) method was used to build quantitative inversion model of AK based on the results of relationship analysis. The results showed that the spectral data that transformed by(log(1/R))' was the best indicator to predict soil AK concentrations with the highest value of determination coefficient(R2) and relative percent deviation(RPD) and the lowest value of root mean square error(RMSE) compared with the other three spectral transformation indices. The values of R2 and RMSE of the prediction model by using spectral data that transformed by(log(1/R))' were 0.89 and 12.45 mg kg-1, respectively. Models predicting for soil AK concentrations built in sub-regions were better than those in the whole region. The results play an instructing role on hyperspectral estimation of soil nutrients in the complicated terrain areas.
In order to analyze the influence of altitude conditions on the hyperspectral inversion of soil available potassium(AK) and to select the best spectral indicators, 118 soil samples were collected in three sub-regions that divided by altitudes and their AK concentrations and spectral data were measured. After that, the relationships between the concentrations of soil AK and the four spectral indices, including the raw spectral reflectance(R),first-order differential from reciprocal reflectance(1/R)', first-order differential from logarithm of reciprocal reflectance(log(1/R))' and first-order differential from reciprocal of logarithm reflectance(1/(log R))', were conducted in the whole region and the three sub-regions. Furthermore, partial least-squares regression(PLSR) method was used to build quantitative inversion model of AK based on the results of relationship analysis. The results showed that the spectral data that transformed by(log(1/R))' was the best indicator to predict soil AK concentrations with the highest value of determination coefficient(R2) and relative percent deviation(RPD) and the lowest value of root mean square error(RMSE) compared with the other three spectral transformation indices. The values of R2 and RMSE of the prediction model by using spectral data that transformed by(log(1/R))' were 0.89 and 12.45 mg kg-1, respectively. Models predicting for soil AK concentrations built in sub-regions were better than those in the whole region. The results play an instructing role on hyperspectral estimation of soil nutrients in the complicated terrain areas.
引文
[1]占丽平,李小坤,鲁剑巍,等.土壤钾素运移的影响因素研究进展[J].土壤, 2012, 44(4):548-553.
[2]连纲,郭旭东,傅伯杰,等.黄土高原小流域土壤养分空间变异特征及预测[J].生态学报, 2008, 28(3):946-954.
[3]谢建昌,周健民, HARDTER R.钾与中国农业[M].南京:河海大学出版社, 2000.
[4]魻BORN I, ANDRIST-RANGEL Y, ASKEKAARD M. Critical aspects of potassium management in agricultural systems[J]. Soil Use and Management, 2005, 21(s1):102-112.
[5] R魻MHELD V, KIRKBY E A. Research on potassium in agriculture:Needs and prospects[J]. Plant and Soil, 2010, 335(1):155-180.
[6] V魡GEN T G, SHEPHERD K D, WALSH M G. Sensing landscape level change in soil fertility following deforestation and conversion in the highlands of Madagascar using Vis-NIR spectroscopy[J].Geoderma, 2006, 133(3):281-294.
[7]史舟,王乾龙,彭杰,等.中国主要土壤高光谱反射特性分类与有机质光谱预测模型[J].中国科学:地球科学, 2014, 44(5):978-988.
[8] CASTALDI F, PALOMBO A, SANTINI F, et al. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon[J].Remote Sensing of Environment, 2016, 179:54-65.
[9] POGGIO L, GIMONA A. Assimilation of optical and radar remote sensing data in 3D mapping of soil properties over large areas[J].Science of the Total Environment, 2017, 579:1094-1110.
[10] CONFALONIERI M, FORNASIER F, URSINO A, et al. The potential of neat infrared reflectan ce spectroscopy as a tool for the chemical characterization of agricultural soils[J]. Journal of Near Infrared Spectroscope, 2001, 9(2):123-131.
[11]尤承增,杨新源,束安,等.土壤全钾含量高光谱估测模型[J].遥感信息, 2017, 32(4):92-97.
[12]叶英聪,张丽君,谢文,等.南方丘陵稻田土壤全钾和速效钾高光谱特征与反演模型研究[J].广东农业科学, 2015,(7):37-42.
[13]蒋璐璐,张瑜,王艳艳,等.基于光谱技术的土壤养分快速测试方法研究[J].浙江大学学报:农业与生命科学版, 2010, 36(4):445-450.
[14]胡芳,蔺启忠,王钦军,等.土壤钾含量高光谱定量反演研究[J].国土资源遥感, 2012,(4):157-162.
[15]刘秀英,石兆勇,常庆瑞,等.黄绵土钾含量高光谱估算模型研究[J].土壤学报, 2018, 55(2):325-337.
[16] CLOUTIS E A. Hyperspectral geological remote sensing:Evaluation of analytical techniques[J]. International Journal of Remote Sensing,1996, 17(12):2215-2242.
[17]谢伯承,薛绪掌,刘卫东,等.基于包络线法对土壤光谱特征的提取及其分析[J].土壤学报, 2005, 42(1):171-175.
[18] WOLD S, SJ魻STR魻M M, ERIKSSON L. PLS-regression:a basic tool of chemometrics[J]. Chemometrics and Intelligent Laboratory Systems,2001, 58(1):109-130.
[19]于雷,洪永胜,周勇,等.高光谱估算土壤有机质含量的波长变量筛选方法[J].农业工程学报, 2016, 32(13):95-102.
[20]谢建昌.我国土壤钾素肥力概况和钾肥使用的进展[A].中国化肥100年回眸———化肥在中国应用100年纪念[C]. 2002:11.
[21]高金龙,侯尧宸,白彦福,等.基于高光谱数据的高寒草甸氮磷钾含量估测方法研究———以青海省贵南县及玛沁县高寒草甸为例[J].草业学报, 2016, 25(3):9-21.
[22]赵小敏,杨梅花.江西省红壤地区主要土壤类型的高光谱特性研究[J].土壤学报, 2018, 55(1):31-42.
[23]蔡亮红,丁建丽,魏阳.基于多源数据的土壤水分反演及空间分异格局研究[J].土壤学报, 2017, 54(5):1057-1067.
[24]马创,申广荣,王紫君,等.不同粒径土壤的光谱特征差异分析[J].土壤通报, 2015, 46(2):292-298.
[25]薛利红,周鼎浩,李颖,等.不同利用方式下土壤有机质和全磷的可见近红外高光谱反演[J].土壤学报, 2014, 51(5):993-1002.
[26]叶勤,姜雪芹,李西灿,等.基于高光谱数据的土壤有机质含量反演模型比较[J].农业机械学报, 2017, 48(3):164-172.
[27]李焱,王让会,管延龙,等.基于高光谱反射特性的土壤全氮含量预测分析[J].遥感技术与应用, 2017, 32(1):173-179.
[28]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.
[29]陈红艳.土壤主要养分含量的高光谱估测研究[D].泰安:山东农业大学, 2012.
[30]李娟,陈超,王昭.基于不同变换形式的干旱区土壤盐分高光谱特征反演[J].水土保持研究, 2018, 25(1):197-201.
[31] DEBAENE G, NIEDZWIECKI J, PECIO A, et al. Effect of the number of calibration samples on the prediction of several soil propertiesat the farm-scale[J]. Geoderma, 2014, 214/215(2):114-125.
[32]贾生尧,杨祥龙,李光,等.近红外光谱技术结合递归偏最小二乘算法对土壤速效磷与速效钾含量测定研究[J].光谱学与光谱分析, 2015, 35(9):2516-2520.
[33]王筝,鲁剑巍,张文君,等.田间土壤钾素有效性影响因素及其评估[J].土壤, 2012, 44(6):898-904.
[34]郭永龙,毕如田,王瑾,等.华北典型山区忻州不同小生境坡耕地土壤肥力特征[J].水土保持学报, 2013, 27(5):205-208.
[35] VISCARRA ROSSEL R A, WALVOORT D J J, MCBRATNEY A B,et al. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties[J]. Geoderma, 2006, 131(1-2):59-75.
[36]于雷,洪永胜,耿雷,等.基于偏最小二乘回归的土壤有机质含量高光谱估算[J].农业工程学报, 2015, 31(14):103-109.
[37]彭杰,刘焕军,史舟,等.盐渍化土壤光谱特征的区域异质性及盐分反演[J].农业工程学报, 2014, 30(17):167-174.
[38] STENBERG B, ROSSEL R A V, MOUAZEN A M, et al. Visible and near infrared spectroscopy in soil science[J]. Advances in Agronomy,2010, 107:163-215.
[2]连纲,郭旭东,傅伯杰,等.黄土高原小流域土壤养分空间变异特征及预测[J].生态学报, 2008, 28(3):946-954.
[3]谢建昌,周健民, HARDTER R.钾与中国农业[M].南京:河海大学出版社, 2000.
[4]魻BORN I, ANDRIST-RANGEL Y, ASKEKAARD M. Critical aspects of potassium management in agricultural systems[J]. Soil Use and Management, 2005, 21(s1):102-112.
[5] R魻MHELD V, KIRKBY E A. Research on potassium in agriculture:Needs and prospects[J]. Plant and Soil, 2010, 335(1):155-180.
[6] V魡GEN T G, SHEPHERD K D, WALSH M G. Sensing landscape level change in soil fertility following deforestation and conversion in the highlands of Madagascar using Vis-NIR spectroscopy[J].Geoderma, 2006, 133(3):281-294.
[7]史舟,王乾龙,彭杰,等.中国主要土壤高光谱反射特性分类与有机质光谱预测模型[J].中国科学:地球科学, 2014, 44(5):978-988.
[8] CASTALDI F, PALOMBO A, SANTINI F, et al. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon[J].Remote Sensing of Environment, 2016, 179:54-65.
[9] POGGIO L, GIMONA A. Assimilation of optical and radar remote sensing data in 3D mapping of soil properties over large areas[J].Science of the Total Environment, 2017, 579:1094-1110.
[10] CONFALONIERI M, FORNASIER F, URSINO A, et al. The potential of neat infrared reflectan ce spectroscopy as a tool for the chemical characterization of agricultural soils[J]. Journal of Near Infrared Spectroscope, 2001, 9(2):123-131.
[11]尤承增,杨新源,束安,等.土壤全钾含量高光谱估测模型[J].遥感信息, 2017, 32(4):92-97.
[12]叶英聪,张丽君,谢文,等.南方丘陵稻田土壤全钾和速效钾高光谱特征与反演模型研究[J].广东农业科学, 2015,(7):37-42.
[13]蒋璐璐,张瑜,王艳艳,等.基于光谱技术的土壤养分快速测试方法研究[J].浙江大学学报:农业与生命科学版, 2010, 36(4):445-450.
[14]胡芳,蔺启忠,王钦军,等.土壤钾含量高光谱定量反演研究[J].国土资源遥感, 2012,(4):157-162.
[15]刘秀英,石兆勇,常庆瑞,等.黄绵土钾含量高光谱估算模型研究[J].土壤学报, 2018, 55(2):325-337.
[16] CLOUTIS E A. Hyperspectral geological remote sensing:Evaluation of analytical techniques[J]. International Journal of Remote Sensing,1996, 17(12):2215-2242.
[17]谢伯承,薛绪掌,刘卫东,等.基于包络线法对土壤光谱特征的提取及其分析[J].土壤学报, 2005, 42(1):171-175.
[18] WOLD S, SJ魻STR魻M M, ERIKSSON L. PLS-regression:a basic tool of chemometrics[J]. Chemometrics and Intelligent Laboratory Systems,2001, 58(1):109-130.
[19]于雷,洪永胜,周勇,等.高光谱估算土壤有机质含量的波长变量筛选方法[J].农业工程学报, 2016, 32(13):95-102.
[20]谢建昌.我国土壤钾素肥力概况和钾肥使用的进展[A].中国化肥100年回眸———化肥在中国应用100年纪念[C]. 2002:11.
[21]高金龙,侯尧宸,白彦福,等.基于高光谱数据的高寒草甸氮磷钾含量估测方法研究———以青海省贵南县及玛沁县高寒草甸为例[J].草业学报, 2016, 25(3):9-21.
[22]赵小敏,杨梅花.江西省红壤地区主要土壤类型的高光谱特性研究[J].土壤学报, 2018, 55(1):31-42.
[23]蔡亮红,丁建丽,魏阳.基于多源数据的土壤水分反演及空间分异格局研究[J].土壤学报, 2017, 54(5):1057-1067.
[24]马创,申广荣,王紫君,等.不同粒径土壤的光谱特征差异分析[J].土壤通报, 2015, 46(2):292-298.
[25]薛利红,周鼎浩,李颖,等.不同利用方式下土壤有机质和全磷的可见近红外高光谱反演[J].土壤学报, 2014, 51(5):993-1002.
[26]叶勤,姜雪芹,李西灿,等.基于高光谱数据的土壤有机质含量反演模型比较[J].农业机械学报, 2017, 48(3):164-172.
[27]李焱,王让会,管延龙,等.基于高光谱反射特性的土壤全氮含量预测分析[J].遥感技术与应用, 2017, 32(1):173-179.
[28]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.
[29]陈红艳.土壤主要养分含量的高光谱估测研究[D].泰安:山东农业大学, 2012.
[30]李娟,陈超,王昭.基于不同变换形式的干旱区土壤盐分高光谱特征反演[J].水土保持研究, 2018, 25(1):197-201.
[31] DEBAENE G, NIEDZWIECKI J, PECIO A, et al. Effect of the number of calibration samples on the prediction of several soil propertiesat the farm-scale[J]. Geoderma, 2014, 214/215(2):114-125.
[32]贾生尧,杨祥龙,李光,等.近红外光谱技术结合递归偏最小二乘算法对土壤速效磷与速效钾含量测定研究[J].光谱学与光谱分析, 2015, 35(9):2516-2520.
[33]王筝,鲁剑巍,张文君,等.田间土壤钾素有效性影响因素及其评估[J].土壤, 2012, 44(6):898-904.
[34]郭永龙,毕如田,王瑾,等.华北典型山区忻州不同小生境坡耕地土壤肥力特征[J].水土保持学报, 2013, 27(5):205-208.
[35] VISCARRA ROSSEL R A, WALVOORT D J J, MCBRATNEY A B,et al. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties[J]. Geoderma, 2006, 131(1-2):59-75.
[36]于雷,洪永胜,耿雷,等.基于偏最小二乘回归的土壤有机质含量高光谱估算[J].农业工程学报, 2015, 31(14):103-109.
[37]彭杰,刘焕军,史舟,等.盐渍化土壤光谱特征的区域异质性及盐分反演[J].农业工程学报, 2014, 30(17):167-174.
[38] STENBERG B, ROSSEL R A V, MOUAZEN A M, et al. Visible and near infrared spectroscopy in soil science[J]. Advances in Agronomy,2010, 107:163-215.