基于ArcGIS Engine的土壤氮肥空间分布成图系统
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摘要
为直观反映土壤中氮肥的分布情况,并为变量施肥决策提供技术支持,开发了氮肥空间分布成图系统。系统基于.NET平台,使用C#语言编写,利用ArcGIS Engine实现数据文件类型转换、插值分析、栅格图层渲染等GIS功能。利用近红外光谱技术获得土壤光谱数据,通过Python构建BP神经网络回归模型,实现氮素含量预测。利用C#命令行的方式实现C#对Python模型的调用。配合车载式土壤参数传感器采集的数据对系统进行了测试,结果表明:该系统能够根据光谱数据估算出采集点对应的氮素含量,并结合采集点的地理坐标,分别使用反距离加权插值算法和趋势面插值算法生成氮肥空间分布图,为制定相应的变量施肥决策提供理论依据。
Nitrogen in the soil is a key element in crop growth. The development of nitrogen spatial distribution mapping system not only reflects the distribution of nitrogen in the soil intuitively,but also provides technical support for variable fertilization decisions. Based on the. NET platform,written in C#,the GIS functions such as data file type conversion,interpolation analysis and raster layer rendering were realized by ArcGIS Engine,and based on soil spectral data obtained by near-infrared spectroscopy,the system made by Python can predict the N values through a BP neural network regression model. The integration of ArcGIS Engine and Python was achieved by adopting C# command line. The system was tested with the fifty-four soil samples which were collected by the vehicle-mounted soil parameter sensor.The results showed that the system can estimate the nitrogen content corresponding to the soil sample based on the spectral data. Both inverse distance weighted and trend surface interpolation can generate the nitrogen spatial distribution map combined with the geographical coordinates of the collection points.The IDW interpolation correlation coefficient reached 0. 753 which satisfied the precision requirements.And through the IDW spatial distribution map,the place where nitrogen was concentrated can be identified obviously. Through the TS spatial distribution map,the whole trend of nitrogen distribution can be mastered. In summary,the spatial distribution maps made by system can provide a theoretical basis for the formulation of corresponding variable fertilization decisions.
Nitrogen in the soil is a key element in crop growth. The development of nitrogen spatial distribution mapping system not only reflects the distribution of nitrogen in the soil intuitively,but also provides technical support for variable fertilization decisions. Based on the. NET platform,written in C#,the GIS functions such as data file type conversion,interpolation analysis and raster layer rendering were realized by ArcGIS Engine,and based on soil spectral data obtained by near-infrared spectroscopy,the system made by Python can predict the N values through a BP neural network regression model. The integration of ArcGIS Engine and Python was achieved by adopting C# command line. The system was tested with the fifty-four soil samples which were collected by the vehicle-mounted soil parameter sensor.The results showed that the system can estimate the nitrogen content corresponding to the soil sample based on the spectral data. Both inverse distance weighted and trend surface interpolation can generate the nitrogen spatial distribution map combined with the geographical coordinates of the collection points.The IDW interpolation correlation coefficient reached 0. 753 which satisfied the precision requirements.And through the IDW spatial distribution map,the place where nitrogen was concentrated can be identified obviously. Through the TS spatial distribution map,the whole trend of nitrogen distribution can be mastered. In summary,the spatial distribution maps made by system can provide a theoretical basis for the formulation of corresponding variable fertilization decisions.
引文
[1]张瑶,李民赞,郑立华,等.基于近红外光谱分析的土壤分层氮素含量预测[J].农业工程学报,2015,31(9):121-126.ZHANG Yao,LI Minzan,ZHENG Lihua,et al.Prediction of soil total nitrogen content in different layers based on near infrared spectral analysis[J].Transactions of the CSAE,2015,31(9):121-126.(in Chinese)
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[3]陈颂超,冯来磊,李硕,等.基于局部加权回归的土壤全氮含量可见-近红外光谱反演[J].土壤学报,2015,52(2):312-320.CHEN Songchao,FENG Lailei,LI Shuo,et al.VIS-NIR spectral inversion for prediction of soil total nitrogen content in laboratory based on locally weigthted regession[J].Acta Pedologica Sinica,2015,52(2):312-320.(in Chinese)
[4]安晓飞,李民赞,郑立华,等.土壤水分对近红外光谱实时检测土壤全氮的影响研究[J].光谱学与光谱分析,2013,33(3):677-681.AN Xiaofei,LI Minzan,ZHENG Lihua,et al.Effect of soil moisture on prediction of soil total nitrogen using NIR spectroscopy[J].Spectroscopy and Spectral Analysis,2013,33(3):677-681.(in Chinese)
[5]BARTHES B G,DIDIER B,EDMOND H,et al.Determining the distributions of soil carbon and nitrogen in particle size fractions using near-infrared reflectance spectrum of bulk soil samples[J].Soil Biology&Biochemistry,2008,40(6):1533-1537.
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[7]汪懋华.精细农业的实践与农业科技创新[J].中国软科学,1999(4):21-25.WANG Maohua.The practice of precision agriculture and the technology innovation of agricultural science[J].Soft Science of China,1999(4):21-25.(in Chinese)
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[9]李树强,孙红,张彦娥,等.作物长势信息空间分析系统设计[J].农业机械学报,2013,44(11):234-240.LI Shuqiang,SUN Hong,ZHANG Yan’e,et al.Development of crop growth spatial analysis system[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(11):234-240.(in Chinese)
[10]徐路铮,郭思逸.基于Arc GIS Engine的空间信息项目一体化管理系统的研究[J].地矿测绘,2018,34(3):20-22.XU Luzheng,GUO Siyi.Research on integrated management system of spatial information project based on Arc GIS Engine[J].Surveying and Mapping of Geology and Mineral Resources,2018,34(3):20-22.(in Chinese)
[11]黄晶.基于Arc GIS的室内地图数据处理及交付系统的设计与实现[D].北京:北京邮电大学,2016.HUANG Jing.Design and implementation of indoor map data processing and delivery system based on Arc GIS[D].Beijing:Beijing University of Posts and Telecommunications,2016.(in Chinese)
[12]李国强,王猛,胡峰,等.基于GIS的夏玉米氮肥精确管理系统设计与实现[J].贵州农业科学,2016,44(2):186-189.LI Guoqiang,WANG Meng,HU Feng,et al.Design and implement of GIS-based precision nitrogen management system in summer maize[J].Guizhou Agricultural Sciences,2016,44(2):186-189.(in Chinese)
[13]李忠武,任平,王振兴,等.基于Arc GIS Engine的水稻生产潜力预测系统的设计与实现[J].湖南大学学报(自科版),2011,38(11):76-81.LI Zhongwu,REN Ping,WANG Zhenxing,et al.Design and realization of rice productivity potential forecast system based on Arc GIS Engine[J].Journal of Hunan University(Natural Sciences),2011,38(11):76-81.(in Chinese)
[14]姜小刚,王海阳,郝勇,等.基于Arc GIS和Vis-NIR脐橙园土壤养分含量分布图预测研究[J].光谱学与光谱分析,2016,36(增刊1):128-129.JANG Xiaogang,WANG Haiyang,HAO Yong,et al.Prediction of navel orange orchard soil nutrient distribution based on Arc GIS and Vis-NIR[J].Spectroscopy and Spectral Analysis,2016,36(Supp.1):128-129.(in Chinese)
[15]WERE K,BUI D T,DICKB,et al.A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape[J].Ecological Indicators,2015,52:394-403.
[16]MOUAZEN A M,KUANG B,DE BAERDEMAEKER J,et al.Comparison among principal component,partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy[J].Geoderma,2010,158(1-2):23-31.
[17]郑立华,李民赞,潘娈,等.基于近红外光谱技术的土壤参数BP神经网络预测[J].光谱学与光谱分析,2008,28(5):1160-1164.ZHENG Lihua,LI Minzan,PAN Luan,et al.Estimation of soil organic matter and soil total nitrogen based on NIR spectroscopy and BP neural work[J].Spectroscopy and Spectral Aanlysis,2008,28(5):1160-1164.(in Chinese)
[18]李燕,孙秀云,王俊德.人工神经网络法测定五组分红外光谱体系[J].光谱学与光谱分析,2000,20(6):773-776.LI Yan,SUN Xiuyun,WANG Junde.Determination of five compounent infrared spectra system with artificial neural network[J].Spectroscopy and Spectral Analysis,2000,20(6):773-776.(in Chinese)
[19]李民赞,韩东海,王秀.光谱分析技术及其应用[M].北京:科学出版社,2006.
[20]VOHLAND M,MICHEL K,LUDWING B.Use of near-infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest-floor material in soils:usefulness of a genetic algorithm[J].Journal of Plant Nutrition and Soil Science,2011,174(5):695-701.
[21]王旭,刘仁杰,孙红,等.冬小麦叶绿素含量空间分布成图方法与精度分析[J].农业机械学报,2017,48(增刊):92-97.WANG Xu,LIU Renjie,SUN Hong,et al.Mapping method and accuracy analysis on spatial distribution of winter wheat chlorophyll content[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(Supp.):92-97.(in Chinese)
[22]臧辰龙.基于中国区域的气温和降水量的插值方法研究[D].泰安:山东农业大学,2014.ZANG Chenlong.Research temperature and precipitation interpolation methods of China[D].Taian:Shandong Agricultural University,2014.(in Chinese)
[2]张娟娟,田永超,姚霞,等.基于近红外光谱的土壤全氮含量估算模型[J].农业工程学报,2012,28(12):183-188.ZHANG Juanjuan,TIAN Yongchao,YAO Xia,et al.Estimating model of soil total nitrogen content based on near-infrared[J].Transactions of the CSAE,2012,28(12):183-188.(in Chinese)
[3]陈颂超,冯来磊,李硕,等.基于局部加权回归的土壤全氮含量可见-近红外光谱反演[J].土壤学报,2015,52(2):312-320.CHEN Songchao,FENG Lailei,LI Shuo,et al.VIS-NIR spectral inversion for prediction of soil total nitrogen content in laboratory based on locally weigthted regession[J].Acta Pedologica Sinica,2015,52(2):312-320.(in Chinese)
[4]安晓飞,李民赞,郑立华,等.土壤水分对近红外光谱实时检测土壤全氮的影响研究[J].光谱学与光谱分析,2013,33(3):677-681.AN Xiaofei,LI Minzan,ZHENG Lihua,et al.Effect of soil moisture on prediction of soil total nitrogen using NIR spectroscopy[J].Spectroscopy and Spectral Analysis,2013,33(3):677-681.(in Chinese)
[5]BARTHES B G,DIDIER B,EDMOND H,et al.Determining the distributions of soil carbon and nitrogen in particle size fractions using near-infrared reflectance spectrum of bulk soil samples[J].Soil Biology&Biochemistry,2008,40(6):1533-1537.
[6]CHANG C W,LAIRD D A,MAUSBACH M J,et al.Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties[J].Soil Science Society of America Journal,2001,65(2):480-490.
[7]汪懋华.精细农业的实践与农业科技创新[J].中国软科学,1999(4):21-25.WANG Maohua.The practice of precision agriculture and the technology innovation of agricultural science[J].Soft Science of China,1999(4):21-25.(in Chinese)
[8]郑立华,李民赞,冀荣华,等.基于GIS的农田土壤水分状况管理模型及应用[J].农业工程学报,2009,25(增刊2):13-17.ZHENG Lihua,LI Minzan,JI Ronghua,et al.Development of soil moisture management models based on GIS for farmland and its application[J].Transactions of the CSAE,2009,25(Supp.2):13-17.(in Chinese)
[9]李树强,孙红,张彦娥,等.作物长势信息空间分析系统设计[J].农业机械学报,2013,44(11):234-240.LI Shuqiang,SUN Hong,ZHANG Yan’e,et al.Development of crop growth spatial analysis system[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(11):234-240.(in Chinese)
[10]徐路铮,郭思逸.基于Arc GIS Engine的空间信息项目一体化管理系统的研究[J].地矿测绘,2018,34(3):20-22.XU Luzheng,GUO Siyi.Research on integrated management system of spatial information project based on Arc GIS Engine[J].Surveying and Mapping of Geology and Mineral Resources,2018,34(3):20-22.(in Chinese)
[11]黄晶.基于Arc GIS的室内地图数据处理及交付系统的设计与实现[D].北京:北京邮电大学,2016.HUANG Jing.Design and implementation of indoor map data processing and delivery system based on Arc GIS[D].Beijing:Beijing University of Posts and Telecommunications,2016.(in Chinese)
[12]李国强,王猛,胡峰,等.基于GIS的夏玉米氮肥精确管理系统设计与实现[J].贵州农业科学,2016,44(2):186-189.LI Guoqiang,WANG Meng,HU Feng,et al.Design and implement of GIS-based precision nitrogen management system in summer maize[J].Guizhou Agricultural Sciences,2016,44(2):186-189.(in Chinese)
[13]李忠武,任平,王振兴,等.基于Arc GIS Engine的水稻生产潜力预测系统的设计与实现[J].湖南大学学报(自科版),2011,38(11):76-81.LI Zhongwu,REN Ping,WANG Zhenxing,et al.Design and realization of rice productivity potential forecast system based on Arc GIS Engine[J].Journal of Hunan University(Natural Sciences),2011,38(11):76-81.(in Chinese)
[14]姜小刚,王海阳,郝勇,等.基于Arc GIS和Vis-NIR脐橙园土壤养分含量分布图预测研究[J].光谱学与光谱分析,2016,36(增刊1):128-129.JANG Xiaogang,WANG Haiyang,HAO Yong,et al.Prediction of navel orange orchard soil nutrient distribution based on Arc GIS and Vis-NIR[J].Spectroscopy and Spectral Analysis,2016,36(Supp.1):128-129.(in Chinese)
[15]WERE K,BUI D T,DICKB,et al.A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape[J].Ecological Indicators,2015,52:394-403.
[16]MOUAZEN A M,KUANG B,DE BAERDEMAEKER J,et al.Comparison among principal component,partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy[J].Geoderma,2010,158(1-2):23-31.
[17]郑立华,李民赞,潘娈,等.基于近红外光谱技术的土壤参数BP神经网络预测[J].光谱学与光谱分析,2008,28(5):1160-1164.ZHENG Lihua,LI Minzan,PAN Luan,et al.Estimation of soil organic matter and soil total nitrogen based on NIR spectroscopy and BP neural work[J].Spectroscopy and Spectral Aanlysis,2008,28(5):1160-1164.(in Chinese)
[18]李燕,孙秀云,王俊德.人工神经网络法测定五组分红外光谱体系[J].光谱学与光谱分析,2000,20(6):773-776.LI Yan,SUN Xiuyun,WANG Junde.Determination of five compounent infrared spectra system with artificial neural network[J].Spectroscopy and Spectral Analysis,2000,20(6):773-776.(in Chinese)
[19]李民赞,韩东海,王秀.光谱分析技术及其应用[M].北京:科学出版社,2006.
[20]VOHLAND M,MICHEL K,LUDWING B.Use of near-infrared spectroscopy to distinguish carbon and nitrogen originating from char and forest-floor material in soils:usefulness of a genetic algorithm[J].Journal of Plant Nutrition and Soil Science,2011,174(5):695-701.
[21]王旭,刘仁杰,孙红,等.冬小麦叶绿素含量空间分布成图方法与精度分析[J].农业机械学报,2017,48(增刊):92-97.WANG Xu,LIU Renjie,SUN Hong,et al.Mapping method and accuracy analysis on spatial distribution of winter wheat chlorophyll content[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(Supp.):92-97.(in Chinese)
[22]臧辰龙.基于中国区域的气温和降水量的插值方法研究[D].泰安:山东农业大学,2014.ZANG Chenlong.Research temperature and precipitation interpolation methods of China[D].Taian:Shandong Agricultural University,2014.(in Chinese)