河南农业科学 >

2019 , Vol. 48 >Issue 12: 157 - 163

DOI: https://doi.org/10.15933/j.cnki.1004-3268.2019.12.024

农业信息与工程·农产品加工

基于CASA-VPM模型的植被最大光能利用率研究

  • 刘剑锋 ,
  • 陈琳 ,
  • 孟琪 ,
  • 王璇 ,
  • 王远征 ,
  • 王来刚 ,
  • 张喜旺
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  • 1.黄河水利职业技术学院，河南开封 475004；2.河南省地质矿产勘查开发局 第三地质勘查院，河南 郑州 451464；3.黄河中下游数字地理技术教育部重点实验室，河南 开封 475004；4.河南省农业科学院 农业经济与信息研究所，河南郑州450002;5.河南大学 环境与规划学院，河南开封 475004；）
刘剑锋(1980-)，女，山东潍坊人，讲师，硕士，主要从事农业遥感、GIS应用等研究。E-mail:liu-jian-feng@163.com

收稿日期: 2019-05-23

  网络出版日期: 2019-12-15

基金资助

国家科技基础条件平台建设项目（2005DKA32300）；全球环境基金（GEF）水资源与水环境综合管理主流化项目（MWR-C-3-9）；河南省科技攻关项目（152102110047）；河南省重大科技专项（121100111300）

收起

Research on Maximum Light Use Efficiency Based on CASA-VPM Model

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  • (1.Yellow River Conservancy Technical Institute,Kaifeng 475001,China;2. No. 3 Geological Exploration Institute,Henan Bureau of Geo-Exploration & Mineral Development,Zhengzhou 451464,China;3. Key Laboratory of Geospatial Technology for Middle and Lower Reaches of the Yellow River Regions,Ministry of Education,Kaifeng 475004,China;4.Institute of Agricultural Economics and Information,Henan Academy of Agricultural Sciences,Zhengzhou 450002,China;5.College of Environment & Planning,Henan University,Kaifeng 475004,China)

Received date: 2019-05-23

  Online published: 2019-12-15

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摘要

通过综合利用CASA模型和VPM模型，在像元尺度上开展εmax值估算研究，从而提高εmax值的空间精细度，为指导农业生产，开展精准农业提供技术支持。以河南省为研究区，首先基于MODIS数据，结合CASA模型和关系模型计算NPP值，进一步结合VPM模型得到像元上的εmax值，最后根据土地利用数据对林地、草地、水田和旱田4种植被覆盖类型的εmax值进行时空分析，并探索相关环境因素对εmax值的影响。结果表明，2001—2015年，εmax值介于0.000~4.796 g/MJ，空间上呈西北、西南高，其他区域低的格局；月尺度变化上，不同植被覆盖类型εmax值均在6—8月份达到峰值，其中，旱田εmax值呈现明显的双峰分布，和河南省大部分地区一年两熟的作物种植制度相符。4种植被覆盖类型中，因为用于光合作用的叶面积较大，林地εmax值较高；环境影响因素中，化肥使用折纯量、二氧化碳含量、植被含水指数和有效灌溉面积与εmax值呈现明显的正相关，相关系数分别为0.66、0.61、0.56、0.53，说明εmax值的变化是自然、人为因素共同影响的结果。可见，在农作物生长的关键阶段，适当增加人为的影响可以提高光能利用率，从而增加作物生产潜力和产量。

关键词： CASA模型; VPM模型; 最大光能利用率; 净初级生产力; 总初级生产力; 光合作用; 时空分布; 植被覆盖类型

本文引用格式

刘剑锋 , 陈琳 , 孟琪 , 王璇 , 王远征 , 王来刚 , 张喜旺 . 基于CASA-VPM模型的植被最大光能利用率研究[J]. 河南农业科学, 2019 , 48(12) : 157 -163 . DOI: 10.15933/j.cnki.1004-3268.2019.12.024

Abstract

By comprehensively utilizing CASA and the VPM models,the estimation method of εmax value was studied on the pixel scale,to improve the spatial precision of εmax value,and provide technical support for guiding agricultural production and developing precision agriculture.Taking Henan Province as research area,NPP value was calculated by combining with CASA model and relational model using MODIS data.Further combined VPM model,εmax value was obtained.Finally,the land use data was used to carry out the temporal and spatial analysis of the εmax values for the four vegetation cover types,i.e.forest,grass,paddy field and dry land. And the influence of related environmental factors on εmax value was also explored.The results showed that,the value of εmax was between 0.000 and 4.796 g/MJ during 2001 and 2015.And it was spatially higher in the northwest and southwest,and in other regions was low.On the monthly scale change,all types of vegetation cover reached the peak value between June and August.The εmax value of dry land showed obvious bimodal distribution,which was consistent with the cropping system of yield two crops a year in Henan Province.Among the four vegetation cover types,the leaves of forest for photosynthesis were denser and larger,so εmax value was significantly higher.Among environmental impact factors,fertilizer application amount(convert into purification),carbon dioxide content,vegetation water content index and effective irrigation area showed a significant positive correlation with εmax value,and the correlation coefficients were 0.66,0.61,0.56 and 0.53,respectively.It can be seen that the change of εmax value was the result of the combined effects of natural and human factors.The results indicate that an appropriate increase in human influence can increase the utilization of light energy at the critical stage of crop growth,thereby increasing crop production potential and yield.

Key words： Carnegie-ames-stanford approach model; Vegetation photosynthesis model; Maximum light use efficiency; Net primary productivity; Gross primary productivity; Photosynthesis; Temporal and spatial distribution; Vegetation cover type

参考文献

［1］赵育民,牛树奎,王军邦,等.植被光能利用率研究进展［J］.生态学杂志,2007,26(9):1471-1477.

［2］陈迪.基于遥感技术的甘南州陆地生态系统NEP研究［D］.兰州:兰州大学,2016.

［3］谭昌伟,杜颖,童璐,等.基于开花期卫星遥感数据的大田小麦估产方法比较［J］.中国农业科学,2017,50(16):3101-3109.

［4］康婷婷,居为民,张春华.2001—2011年中国农田最大光能利用率参数时空变化特征［J］.遥感技术与应用,2016,31(4):663-671.

［5］张强,张黎,何洪林,等.基于涡度相关通量数据的植被最大光能利用率反演研究［J］.第四纪研究,2014,34(4):743-751.

［6］POTTER C S,RANDERSON J T,FIELD C B,et al.Terrestrial ecosystem production:A process model based on global satellite and surface data［J］.Global Biogeochemical Cycles,1993,7(4):811-841.

［7］FIELD C B,BEHRENFELD M J,RANDERSON J T,et al.Primary production of the biosphere:Integrating terrestrial and oceanic components［J］.Science,1998,281(5374):237-240.

［8］朱文泉,潘耀忠,何浩,等.中国典型植被最大光利用率模拟［J］.科学通报,2006,51(6):700-706.

［9］YAN H M,FU Y L,XIAO X M,et al.Modeling gross primary productivity for winter wheatmaize double cropping system using MODIS time series and CO2 eddy flux tower data［J］.Agriculture Ecosystems & Environment,2009,129(4):391-400.

［10］张美玲,蒋文兰,陈全功,等.基于改进的CASA模型模拟草原综合顺序分类体系各类的最大光能利用率［J］.草原与草坪,2012,32(4):60-66.

［11］王保林,王晶杰,杨勇,等.植被光合有效辐射吸收分量及最大光能利用率算法的改进［J］.草业学报,2013,22(5):220-228.

［12］包刚,辛晓平,包玉海,等.内蒙古草原植被最大光能利用率取值优化研究［J］.光谱学与光谱分析,2016,36(10):3280-3286.

［13］康婷婷,高苹,居为民,等.江苏省农作物最大光能利用率时空特征及影响因子［J］.生态学报,2014,34(2):410-420.

［14］赵文亮,贺振,贺俊平,等.基于MODIS-NDVI的河南省冬小麦产量遥感估测［J］.地理研究,2012,31(12):2310-2320.

［15］ZHANG X W,QIU F,QIN F.Identification and mapping of winter wheat by integrating temporal change information and kullbackleibler divergence［J］.Int J Appl Earth Obs Geoinformation,2019,76:26-39.

［16］张喜旺,秦耀辰,秦奋.综合季相节律和特征光谱的冬小麦种植面积遥感估算［J］.农业工程学报,2013,19(8):154-163.

［17］刘剑锋,张喜旺.基于光谱和时相特征的夏玉米遥感识别［J］.遥感技术与应用,2016,31(6):1131-1139.

［18］孟琪,王来刚,秦奋,等.河南省农作物最大光能利用率综合评估与时空特征分析［J］.河南农业科学,2018,47(7):149-156.

［19］XIAO X,HOLLINGER D,ABER J,et al.Satellitebased modeling of gross primary production in an evergreen needle leaf forest［J］.Remote Sensing of Environment,2004,89(4):519-534.

［20］ZHANG Y,YU G,YANG J,et al.Climatedriven global changes in carbon use efficiency［J］.Global Ecology and Biogeography,2014,23(2):144-155.

 [21］张喜旺,于宁,秦奋,等.基于遥感的植被吸收光合有效辐射估算［J］.生态科学,2013,32(5):604-608.
［22］刘真真,张喜旺,陈云生,等.基于CASA 模型的区域冬小麦生物量遥感估算［J］.农业工程学报,2017,33(4) :225-233.

［23］蒋蕊竹,李秀启,朱永安,等.基于MODIS黄河三角洲湿地NPP与NDVI相关性的时空变化特征［J］.生态学报,2011,31(22):6708-6716.

［24］孙成明,孙政国,刘涛,等.基于MODIS的中国草地NPP综合估算模型［J］.生态学报,2015,35(4):1079-1085.

［25］李卫民,张佳宝,朱安宁.空气温湿度对小麦光合作用的影响［J］.灌溉排水学报,2008,27(3):90-92.

［26］张黛静，陈倩青，马建辉，等.不同冬春性小麦品种在豫中、豫北地区光能利用率及生产潜力比较［J］.河南农业科学，2018，47(8):17-23.

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