您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Winter Wheat Growth Spatial Variation Monitoring Through Hyperspectral Remote Sensing Image

文献类型： 外文期刊

作者： Song Xiaoyu 1 ; Li Ting 2 ; Wang Jihua 3 ; Gu Xiaohe 1 ; Xu Xingang 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.HaiNan Normal Univ, Coll Geog & Tourism, Haikou 571158, Peoples R China

3.Beijing Res Ctr Agrifood Testing & Farmland Monit, Beijing 100097, Peoples R China

关键词： Winter Wheat;Spatial Variation;Operational Modular Imaging Spectrometer (OMIS);Sill;Range;Nugget

期刊名称：REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XVII

ISSN： 0277-786X

年卷期： 2015 年 9637 卷

页码：

收录情况： SCI

摘要： This work aims at quantifying the winter wheat growth spatial heterogeneity captured by hyperspectral airborne images. The field experiment was conducted in 2001 and 2002 and airborne hyperspectral remote-sensing data was acquired at noon on 11 April 2001 using an operational modular imaging spectrometer (OMIS). Totally 12 winter fields which covered by both dense and sparse winter wheat canopies were selected to analysis the winter wheat growth heterogeneity. The experimental semi-variograms for bands covered from invisible to mid-infrared were computed for each field then the theoretical models were be fitted with least squares algorithm for spherical model, exponential model. The optimization model was selected after evaluated by R-square. Three key terms in each model, the sill, the range, and nugget variance were then calculated from the models. The study results show that the sill, range and nugget for same field wheat were varied with the wavelength from blue to mid infrared bands. Although wheat growth in different fields showed different spatial heterogeneity, they all showed an obvious sill pattern. The minimum of mean range value was 7.52 m for mid-infrared bands while the maximum value was 91.71 m for visible bands. The minimum of mean sill value ranged from 1.46 for visible bands to 39.76 for NIR bands, the minimum of mean nugget value ranged from 0.06 for visible bands to5.45 for mid-infrared bands. This study indicate that remote sensing image is important for crop growth spatial heterogeneity study. But it is necessary to explore the effect of different wavelength of image data on crop growth semi-variogram estimation and find out which band data could be used to estimate crop semi-variogram reliably.

  • 相关文献

[1]Spatial variation of attainable yield and fertilizer requirements for maize at the regional scale in China. Xu, Xinpeng,Xu, Xinpeng,He, Ping,Zhang, Jiajia,Zhou, Wei,He, Ping,Pampolino, Mirasol F.,Johnston, Adrian M..

[2]Simulation of Winter Wheat Phenology in Beijing Area with DSSAT-CERES Model. Haikuan Feng,Zhenhai Li,Peng He,Xiuliang Jin,Guijun Yang,Haiyang Yu,Fuqin Yang. 2016

[3]CHARACTERIZATION OF POWDERY MILDEW IN WINTER WHEAT USING MULTI-ANGULAR HYPERSPECTRAL MEASUREMENTS. Jinling Zhao,Lin Yuan,Linsheng Huang,Dongyan Zhang,Jingcheng Zhang,Xiaohe Gu. 2013

[4]Retrieval of LAI and leaf chlorophyll content from remote sensing data by agronomy mechanism knowledge to solve the ill-posed inverse problem. Zhenhai Li,Chenwei Nie,Guijun Yang,Xingang Xu,Xiuliang Jin,Xiaohe Gu. 2014

[5]Monitoring the ratio of leaf carbon to nitrogen in winter wheat with hyperspectral measurements. Xin-gang Xu,Xiao-dong Yang,Xiao-he Gu,Hao Yang,Hai-kuan Feng,Gui-jun Yang,Xiao-yu,Song. 2015

[6]Study the Spatial-Temporal Variation of Wheat Growth Under Different Site-Specific Nitrogen Fertilization Approaches. Bei Cui,Wenjiang Huang,Xiaoyu Song,Huichun Ye,Yingying Dong. 2019

[7]EVALUATION OF ARABLE LAND YIELD POTENTIAL THROUGH REMOTE SENSING MONITORING. Song Xiaoyu,Gu Xiaohe,Wang Jihua,Chang Hong. 2014

[8]SPATIAL VARIABILITY OF WINTER WHEAT GROWTH BASED ON THE INDIVIDUAL INDEX AND THE POPULATION INDEX. Bei Cui,Xiaoyu Song,Wude Yang,Meichen Feng,Jihua Wang. 2014

[9]Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices. Xie, Qiaoyun,Huang, Wenjiang,Zhang, Bing,Dong, Yingying,Xie, Qiaoyun,Chen, Pengfei,Song, Xiaoyu,Pascucci, Simone,Pignatti, Stefano,Laneve, Giovanni. 2016

[10]Winter wheat biomass estimation based on canopy spectra. Zheng Ling,Zhu Dazhou,Zhang Baohua,Wang Cheng,Zhao Chunjiang,Zheng Ling,Liang Dong. 2015

[11]MONITORING WINTER WHEAT MATURITY BY HYPERSPECTRAL VEGETATION INDICES. Wang, Qian,Huang, Yuanfang,Wang, Qian,Li, Cunjun,Wang, Jihua,Song, Xiaoyu,Huang, Wenjiang. 2012

[12]Discrimination of yellow rust and powdery mildew in wheat at leaf level using spectral signatures. Yuan, Lin,Zhang, Jingcheng,Zhao, Jinling,Du, Shizhou,Huang, Wenjiang,Wang, Jihua. 2012

[13]SELECTION OF SPECTRAL CHANNELS FOR SATELLITE SENSORS IN MONITORING YELLOW RUST DISEASE OF WINTER WHEAT. Yuan, Lin,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Wang, Jihua. 2013

[14]Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image. Yuan, Lin,Zhang, Jingcheng,Nie, Chenwei,Wei, Liguang,Wang, Jihua,Zhang, Jingcheng,Wang, Jihua,Zhang, Jingcheng,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Wang, Jihua,Shi, Yeyin. 2014

[15]Monitoring and Forecasting Winter Wheat Freeze Injury and Yield from Multi-Temporal Remotely Sensed Data. Wang, Huifang,Huo, Zhiguo,Zhou, Guangsheng,Wu, Li,Wang, Huifang,Feng, Haikuan. 2016

[16]Hyperspectral Estimation of Leaf Water Content for Winter Wheat Based on Grey Relational Analysis(GRA). Jin Xiu-liang,Wang Yan,Tan Chang-wei,Zhu Xin-kai,Guo Wen-shan,Xu Xin-gang,Wang Ji-hua,Li Xin-chuan. 2012

[17]Using new hyperspectral index to estimate leaf chlorophyll content in winter wheat. Xu, Xingang,Song, Xiaoyu,Li, Cunjun,Wang, Jihua. 2012

[18]Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat. Xie, Qiaoyun,Huang, Wenjiang,Liang, Dong,Huang, Linsheng,Zhang, Dongyan,Chen, Pengfei,Wu, Chaoyang,Yang, Guijun,Zhang, Jingcheng. 2014

[19]Winter wheat field transformation monitoring through remote sensing in Beijing suburb. Song, Xiaoyu,Cui, Fangning,Gu, Xiaohe,Xu, Xingang,Cui, Fangning,Wang Jihua. 2014

[20]EVALUATION OF ARABLE LAND YIELD POTENTIAL THROUGH REMOTE SENSING MONITORING. Song Xiaoyu,Gu Xiaohe,Chang Hong. 2014

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台