基于粒子群神经网络模型反演玉米、小麦叶面积指数
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摘要
基于高分1号遥感影像,分别采用粒子群神经网络模型、神经网络模型和植被指数回归模型3种方法,反演廊坊市玉米、小麦叶面积指数(LAI)。结果表明,粒子群神经网络模型反演玉米、小麦LAI的精度要高于其他方法,其模型的决定系数R~2均高于0.9,均方根误差均低于0.196,可满足反演精度的要求。本研究提出的基于高分1号影像的粒子群神经网络模型反演玉米和小麦LAI的方法具有一定的普适性。
In this paper, based on GF-1 remote sensing image, three methods, namely particle swarm optimization neural network model, artificial neural network model, vegetation index regression model,were adopted to invert leaf area index(LAI) of maize and wheat in Langfang City. It was shown that the accuracy of maize and wheat LAI inversion by particle swarm optimization neural network model was the highest. The calculated determination coefficient R~2 of this method was higher than 0.9, and its root mean square error was lower than 0.196, which could satisfy the requirement of inversion precision. To sum up, maize and wheat LAI inversion based on the proposed particle swarm optimization neural network model was feasible on GF-1 images, and possessed universality.
In this paper, based on GF-1 remote sensing image, three methods, namely particle swarm optimization neural network model, artificial neural network model, vegetation index regression model,were adopted to invert leaf area index(LAI) of maize and wheat in Langfang City. It was shown that the accuracy of maize and wheat LAI inversion by particle swarm optimization neural network model was the highest. The calculated determination coefficient R~2 of this method was higher than 0.9, and its root mean square error was lower than 0.196, which could satisfy the requirement of inversion precision. To sum up, maize and wheat LAI inversion based on the proposed particle swarm optimization neural network model was feasible on GF-1 images, and possessed universality.
引文
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[13] LI H,CHEN Z X,JIANG Z W,et al.Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat[J].Journal of Integrative Agriculture,2017,16(2):266-285.
[14] 蔡亮红,丁建丽,魏阳.基于地形校正TVDI的地下水位埋深反演[J].干旱地区农业研究,2018,36(3):244-250.CAI L H,DING J L,WEI Y.Estimation of groundwater level from TVDI after topographic correction[J].Agricultural Research in the Arid Areas,2018,36(3):244-250.(in Chinese with English abstract)
[15] 姜春雷,张树清,张策,等.基于SARIMA-BP神经网络组合方法的MODIS叶面积指数时间序列建模与预测[J].光谱学与光谱分析,2017,37(1):189-193.JIANG C L,ZHANG S Q,ZHANG C,et al.Modeling and predicting of MODIS leaf area index time series based on a hybrid SARIMA and BP neural network method[J].Spectroscopy and Spectral Analysis,2017,37(1):189-193.(in Chinese with English abstract)
[2] NJUGUNA C W,KAMIRI H W,OKALEBO J R,et al.Evaluating the effect of plant population densities and nitrogen application on the leaf area index of maize in a reclaimed wetland in Kenya[J].Acta Universitatis Sapientiae,Agriculture and Environment,2016,8(1):139-148.
[3] 贺佳,刘冰锋,李军.不同生育时期冬小麦叶面积指数高光谱遥感监测模型[J].农业工程学报,2014,30(24):141-150.HE J,LIU B F,LI J.Monitoring model of leaf area index of winter wheat based on hyperspectral reflectance at different growth stages[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(24):141-150.(in Chinese with English abstract)
[4] 赵虎,裴志远,马尚杰,等.利用HJ-1-A/BCCD2数据反演冬小麦叶面积指数[J].农业工程学报,2012,28(10):172-176.ZHAO H,PEI Z Y,MA S J,et al.Retrieving leaf area index of winter wheat using HJ-1-A/B CCD2 data[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(10):172-176.(in Chinese with English abstract)
[5] 李军玲,彭记永.不同生育时期冬小麦叶面积指数地面高光谱遥感模型研究[J].麦类作物学报,2018,38(8):979-987.LI J L,PENG J Y.Estimation of winter wheat LAI at different growth stages based on canopy hyperspectral remote sensing system[J].Journal of Triticeae Crops,2018,38(8):979-987.(in Chinese with English abstract)
[6] 刘洋,刘荣高,刘斯亮,等.基于物理模型训练神经网络的作物叶面积指数遥感反演研究[J].地球信息科学学报,2010,12(3):426-435.LIU Y,LIU R G,LIU S L,et al.Estimation of crop LAI based on the neural network trained from physical model[J].Journal of Geo-Information Science,2010,12(3):426-435.(in Chinese with English abstract)
[7] 刘振波,邹娴,葛云健,等.基于高分一号WFV影像的随机森林算法反演水稻LAI[J].遥感技术与应用,2018,33(3):458-464.LIU Z B,ZOU X,GE Y J,et al.Retrieval rice leaf area index using random forest algorithm based on GF-1 WFV remote sensing data[J].Remote Sensing Technology and Application,2018,33(3):458-464.(in Chinese with English abstract)
[8] 雷宇斌,朱善宽,郭云开,等.极限学习机辅助下路域植被叶面积指数的反演[J].测绘通报,2018(9):82-86.LEI Y B,ZHU S K,GUO Y K,et al.Inversion of leaf area index based on extreme learning machine regression in road vegetation[J].Bulletin of Surveying and Mapping,2018(9):82-86.(in Chinese with English abstract)
[9] HONG C Q,JIN X B,REN J,et al.Satellite data indicates multidimensional variation of agricultural production in land consolidation area[J].Science of the Total Environment,2019,653:735-747.
[10] FAN L,WIGNERON J P,XIAO Q,et al.Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region[J].Remote Sensing of Environment,2018,205:210-223.
[11] VEECK G,LI Z,YU F W,et al.Contemporary changes to herding systems in China and effects on pasture quality:a case study in Gansu Province,2000-2012[J].Environmental Monitoring and Assessment,2017,189(11):575.
[12] LEWIS R,FEETHAM C,GENTLES L,et al.Benzamil sensitive ion channels contribute to volume regulation in canine chondrocytes[J].British Journal of Pharmacology,2013,168(7):1584-1596.
[13] LI H,CHEN Z X,JIANG Z W,et al.Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat[J].Journal of Integrative Agriculture,2017,16(2):266-285.
[14] 蔡亮红,丁建丽,魏阳.基于地形校正TVDI的地下水位埋深反演[J].干旱地区农业研究,2018,36(3):244-250.CAI L H,DING J L,WEI Y.Estimation of groundwater level from TVDI after topographic correction[J].Agricultural Research in the Arid Areas,2018,36(3):244-250.(in Chinese with English abstract)
[15] 姜春雷,张树清,张策,等.基于SARIMA-BP神经网络组合方法的MODIS叶面积指数时间序列建模与预测[J].光谱学与光谱分析,2017,37(1):189-193.JIANG C L,ZHANG S Q,ZHANG C,et al.Modeling and predicting of MODIS leaf area index time series based on a hybrid SARIMA and BP neural network method[J].Spectroscopy and Spectral Analysis,2017,37(1):189-193.(in Chinese with English abstract)