|Table of Contents|

Inversion of Soybean Fresh Biomass Based on Multipayload Unmanned Aerial Vehicles (UAVs)(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2017年01期
Page:
41-50
Research Field:
Publishing date:

Info

Title:
Inversion of Soybean Fresh Biomass Based on Multipayload Unmanned Aerial Vehicles (UAVs)
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
Keywords:
Unmanned aerial vehicle;  Remote sensing;  Multi-payload;  Biomass;  The vegetation index;  Soybean
PACS:
-
DOI:
10.11861/j.issn.1000-9841.2017.01.0041
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.

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.

Memo

Memo:
-
Last Update: 2017-03-14