[1]陆国政,杨贵军,赵晓庆,等.基于多载荷无人机遥感的大豆地上鲜生物量反演[J].大豆科学,2017,36(01):41-50.[doi:10.11861/j.issn.1000-9841.2017.01.0041]
　LU Guo-zheng,YANG Gui-jun,ZHAO Xiao-qing,et al.Inversion of Soybean Fresh Biomass Based on Multipayload Unmanned Aerial Vehicles (UAVs)[J].Soybean Science,2017,36(01):41-50.[doi:10.11861/j.issn.1000-9841.2017.01.0041]
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基于多载荷无人机遥感的大豆地上鲜生物量反演

《大豆科学》[ISSN:1000-9841/CN:23-1227/S] 卷: 第36卷 期数: 2017年01期 页码: 41-50 栏目: 出版日期: 2017-01-20
Title:
Inversion of Soybean Fresh Biomass Based on Multipayload Unmanned Aerial Vehicles (UAVs)
作者:
陆国政1; 2; 3; 杨贵军2; 赵晓庆2; 王艳杰1; 李长春1; 张小燕3
1.河南理工大学 测绘与国土信息工程学院， 河南 焦作 454000; 2.北京农业信息技术研究中心，北京 100097； 3.南京农业大学 国家大豆改良中心， 江苏 南京 210095
Author(s):
LU Guo-zheng1; 2; 3; YANG Gui-jun2;  ZHAO Xiao-qing2;  WANG Yan-jie1;  LI Chang-chun1;  ZHANG Xiao-yan3
1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China; 2. Beijing Agricultural Information Technology Research Center, Beijing 100097, China; 3.Nanjing Agricultural University, National Center for Soybean Improvement, Nanjing 210095, China
关键词:
无人机; 遥感; 载荷; 生物量; 植被指数; 大豆
Keywords:
Unmanned aerial vehicle;  Remote sensing;  Multi-payload;  Biomass;  The vegetation index;  Soybean
DOI:
10.11861/j.issn.1000-9841.2017.01.0041
文献标志码:
A
摘要:
以多载荷无人机获取数据和地面实测的数据为基础，将大豆生殖生长期分段建模，采用植被指数和光谱参数相结合再加上农学参数株高，通过最小二乘法建立多元线性回规模型的方法，来估算大豆开花期和结荚期的鲜生物量，采用高光谱植被指数法估算大豆鼓粒期和成熟期的鲜生物量。结果表明：在大豆开花期和结荚期内，采用混合法构建生物量反演模型利用交叉验证法，验证结果的R2和RMSE分别为0.714和0.393；在大豆鼓粒期和成熟期内，采用高光谱植被指数法构建生物量反演模型，利用交叉验证法，验证结果的R2和RMSE分别为0.697和0.386；大豆开花结荚期构建的模型和鼓粒成熟期构建的模型都有比较高的精度和可靠性，利用这两种模型完成了高光谱影像鲜生物量的遥感空间制图，能反映当地当时大豆的真实长势情况。
Abstract:
In this study, based on the UAV payload data and ground measured data, different soybean productive growth periods were modeled, vegetation index and spectral parameters combine agronomic parameters plus the height multiple linear scale back by least squares method were used to estimate the biomass of fresh soybean flowering and fruiting period, and hyperspectral vegetation index were use to estimate soybean drum fresh biomass and maturity of grain. The results showed that, during soybean flowering and fruiting period, verification results of R2and RMSE were 0.714 and 0.393, respectively, by using of biomass mixing build inversion model using cross-validation. During soybean seed filling stage and mature period, verification results of R2and RMSE were 0.697 and 0.386, respectively, by using of hyperspectral vegetation index build biomass inversion model using cross-validation. Models of soybean podding and seed filling to maturity periods both had a relatively high accuracy and reliability, using these two models to complete the hyperspectral remote sensing image of fresh biomass mapping could reflect the real growth situation of soybeans.

参考文献/References:

［1］王登伟, 黄春燕, 马勤建, 等. 棉花高光谱植被指数与LAI和地上鲜生物量的相关关系研究［J］. 新疆农业科学, 2008,45(5):426-429.（Wang D W, Huang C H, Ma Q J, et al. Relationships between Hyperspectral Vegetative index, LAI and aboveground fresh biomass of cotton［J］. Xinjiang Agricultural Sciences, 2008,45(5):426-429.）

［2］黄春燕, 王登伟, 曹连莆, 等. 棉花地上鲜生物量的高光谱估算模型研究［J］. 农业工程学报, 2007,23(3):131-135.（Huang C Y, Wang D W, Cao L P, et al. Models for estimating cotton aboveground fresh biomass using hyperspectral data［J］. Transactions of the Chinese Society of Agricultural, 2007,23(3):131-135.）
［3］王建步, 张杰, 马毅, 等. 基于高分一号 WFV 卫星影像的黄河口湿地草本植被生物量估算模型研究［J］. 激光生物学报, 2014(6):604-608.（Wang J B, Zhang J, Ma Y, et al. Study on the above ground vegetation biomass estimation model based on GF-1 WFV satellite image in the yellow river estuary wetland［J］. Journal of Laser Biological, 2014(6):604-608.）
［4］高明亮, 赵文吉, 宫兆宁, 等. 基于环境卫星数据的黄河湿地植被生物量反演研究［J］. 生态学报, 2013,33(2):542-553.（Gao M L, Zhao W J, Gong Z N, et al. The study of vegetation biomass inversion based on the HJ satellite data in Yellow River wetland［J］. Acta Ecologica Sinica, 2013,33(2):542-553.）
［5］刘芳, 冯仲科, 赵芳, 等. 资源三号遥感卫星影像的生物量反演研究［J］. 西北林学院学报, 2015,30(3):175-181.（Liu F, Feng Z K, Zhao F, et al. Biomass inversion study of ZY-3 remote sensing satellite imagery［J］. Journal of Northwest Forestry University, 2015,30(3):175-181.）
［6］崔霞, 梁天刚, 刘勇. 基于MOD09GA产品的草地生物量遥感估算模型［J］. 兰州大学学报:自然科学版, 2009,45(5):79-87.（Cui X, Liang T G, Liu Y. Modeling of aboveground biomass of grassland using remotely sensed MOD09GA data［J］. Journal of Lanzhou University（Natural Science Edition）, 2009,45(5):79-87.）
［7］李继宇, 张铁民, 彭孝东, 等. 小型无人机在农田信息监测系统中的应用［J］. 农机化研究, 2010,32(5):183-186.（Li J Y, Zhang T M, Peng X D, et al. The application of small UAV(SUAV) in farm and information monitoring system［J］. Journal of Agricultural Mechanization Research, 2010,32(5):183-186.）
［8］杨贵军, 李长春, 于海洋, 等. 农用无人机多传感器遥感辅助小麦育种信息获取［J］. 农业工程学报, 2015(21):184-190.（Yang G J, Li C C, Yu H Y, et al. UAV based multi-load remote sensing technologies for wheat breeding information acquirement［J］. Journal of Agricultural Engineering, 2015(21): 184-190.）
［9］高林, 杨贵军, 王宝山, 等. 基于无人机遥感影像的大豆叶面积指数反演研究［J］. 中国生态农业学报, 2015(7):868-876.（Gao L, Yang G J, Wang B S, et al. Soybean leaf area index retrieval with UAV remote sensing imagery［J］. Chinese Journal of Eco-Agriculture, 2015(7): 868-876.）
［10］Hame T, Salli A, Andersson K, et al. A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data［J］. International Journal of Remote Sensing, 1997,18(18):3211-3243.
［11］李素英, 李晓兵, 莺歌, 等. 基于植被指数的典型草原区生物量模型-以内蒙古锡林浩特市为例［J］. 植物生态学报, 2007,31(1):23-31.（Li S Y, Li X B, Ying G, et al. Vegetation indexes-biomass models for typical semi-arid steppe-A case study for xilinhot in northern china［J］. Journal of Plant Ecology, 2007,31(1):23-31.）
［12］侯学会, 牛铮, 黄妮, 等. 小麦生物量和真实叶面积指数的高光谱遥感估算模型［J］. 国土资源遥感, 2012(4):30-35.（Hou X H, Niu Z, Huang N, et al. The hyperspectral remote sensing estimation models of total biomass and true LAI of wheat［J］. Remote Sensing of Land and Resources, 2012(4):30-35.）
［13］Bareth G, Aasen H, Bendig J, et al. Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: Spectral comparison with portable spectroradiometer measurements［J］. Photogrammetrie-Fernerkundung-Geoinformation,2015(1):69-79.
［14］Aasen H, Bendig J, Bolten A, et al. Introduction and preliminary results of a calibration for full-frame hyperspectral cameras to monitor agricultural crops with UAVs［J］. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014,7(7):1-8.

备注/Memo

基金项目：北京市自然科学基金（4141001）；北京市农林科学院科技创新能力建设项目（KJCX20140417）；国家自然科学基金（41271345，41471351）。

第一作者简介：陆国政（1991-），男，硕士，主要从事农业遥感研究。E-mail：1450018989@qq.com。
通讯作者：杨贵军（1975-），男，研究员，主要从事农业定量遥感机理及精准农业应用研究。E-mail:guijun.yan@163.com。

更新日期/Last Update: 2017-03-14