北京市农林科学院机构知识库

Monitoring and Forecasting Winter Wheat Freeze Injury and Yield from Multi-Temporal Remotely Sensed Data

收藏
分享
全文链接

文献类型：外文期刊

作者： Wang, Huifang ¹ ; Huo, Zhiguo ¹ ; Zhou, Guangsheng ¹ ; Wu, Li ¹ ; Feng, Haikuan ² ;

作者机构： 1.Chinese Acad Meteorol Sci, Beijing 100081, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Winter wheat;freeze injury levels;change detection;yield assessment

期刊名称：INTELLIGENT AUTOMATION AND SOFT COMPUTING （影响因子：1.647；五年影响因子：1.469 ）

ISSN： 1079-8587

年卷期： 2016 年 22 卷 2 期

页码：

收录情况： SCI

摘要： Remote-sensing techniques provide crop growth information economically, rapidly, and objectively on a large scale. Remote sensing has been widely used to monitor crop growth and forecast yield. In this study, three Huanjing satellite ( HJ) charge-coupled device ( CCD) images of winter wheat at growth-wintering ( December 2, 2009; pre-freeze injury), regreening ( April 2, 2010; post-freeze injury), and jointing stages ( April 23, 2010) were acquired for the Gaocheng area in Hebei Province. According to the change characteristics of the normalized difference vegetation index ( NDVI) of field samplings of post-freeze injury, we built a multi-line-progress model between the NDVI difference (Delta NDVI) and field samplings, which correspond with field investigation data. The damage levels ( uninjured, mild, moderate, and serious) and the growth levels ( better, good, bad, and worse) were also specified in the model. As a result, the coefficient of determination ( R-2) of this model reached 0.6001; 20 sampling points were used to validate the model and R-2 reached 0.5255. This study demonstrates the feasibility of using early growth stage model to predict yield and provides a tentative prediction of the yield in the Hebei area using HJ-CCD images of China.

相关文献

[1]Characterizing spatiotemporal dynamics of land cover with multi-temporal remotely sensed imagery in Beijing during 1978-2010. Zhao, Jinling,Huang, Linsheng,Zhang, Dongyan,Guo, Wei,Huang, Wenjiang,Yang, Hao,Yuan, Lin. 2014

[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]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

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

[16]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

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

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

[19]New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases. Huang, Wenjiang,Guan, Qingsong,Guan, Qingsong,Zhao, Jinling,Liang, Dong,Huang, Linsheng,Zhang, Dongyan,Luo, Juhua,Zhang, Jingcheng. 2014

[20]Monitoring of Winter Wheat Aboveground Fresh Biomass Based on Multi-Information Fusion Technology. Zheng Ling,Dong Da-ming,Zhang Bao-hua,Wang Cheng,Zhao Chun-jiang,Zheng Ling,Zhu Da-zhou. 2016

作者其他论文更多>>

Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering

作者：Xu, Bo;Zhao, Chunjiang;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao

关键词：tassel; 3D phenotyping; TreeQSM; genotyping; clustering
Sensitivity Analysis of AquaCrop Model Parameters for Winter Wheat under Different Meteorological Conditions Based on the EFAST Method

作者：Xing, Huimin;Sun, Qi;Li, Zhiguo;Wang, Zhen;Xing, Huimin;Wang, Zhen;Xing, Huimin;Sun, Qi;Wang, Zhen;Li, Zhiguo;Feng, Haikuan

关键词：winter wheat; biomass; sensitivity analysis; AquaCrop model
Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning

作者：Chen, Riqiang;Feng, Haikuan;Hu, Haitang;Chen, Riqiang;Ren, Lipeng;Yang, Guijun;Cheng, Zhida;Zhao, Dan;Zhang, Chengjian;Feng, Haikuan;Hu, Haitang;Yang, Hao;Chen, Riqiang;Zhang, Chengjian;Ren, Lipeng;Feng, Haikuan

关键词：maize; chlorophyll; radiative transfer model; feature selection; transfer learning
Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (Annona squamosa L.)

作者：Jiang, Xiangtai;Xu, Xingang;Wu, Wenbiao;Yang, Guijun;Meng, Yang;Feng, Haikuan;Li, Yafeng;Xue, Hanyu;Chen, Tianen;Jiang, Xiangtai;Xu, Xingang;Gao, Lutao

关键词：canopy nitrogen content; UAV remote sensing; ensemble learning; Lasso model
Retrieving the chlorophyll content of individual apple trees by reducing canopy shadow impact via a 3D radiative transfer model and UAV multispectral imagery

作者：Zhang, Chengjian;Chen, Zhibo;Chen, Riqiang;Zhang, Wenjie;Zhang, Chengjian;Chen, Riqiang;Zhang, Wenjie;Zhao, Dan;Yang, Guijun;Xu, Bo;Feng, Haikuan;Yang, Hao

关键词：Chlorophyll content; Shadows; Vegetation index (VI); Radiative transfer models (RTMs); Hybrid inversion model; Individual apple tree crown
Enhancing potato leaf protein content, carbon-based constituents, and leaf area index monitoring using radiative transfer model and deep learning

作者：Feng, Haikuan;Fan, Yiguang;Ma, Yanpeng;Liu, Yang;Chen, Riqiang;Bian, Mingbo;Fan, Jiejie;Yang, Guijun;Zhao, Chunjiang;Feng, Haikuan;Zhao, Chunjiang;Yue, Jibo;Fu, Yuanyuan;Leng, Mengdie;Jin, Xiuliang;Zhao, Yu

关键词：Potato; Deep learning; Radiative transfer model; Transfer learning; Leaf protein content
Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing

作者：Zhang, Shanxin;Yue, Jibo;Shu, Meiyan;Zhang, Shanxin;Wang, Xiaoyan;Feng, Haikuan;Feng, Haikuan;Liu, Yang

关键词：segmentation; digital camera; corn; deep learning

Monitoring and Forecasting Winter Wheat Freeze Injury and Yield from Multi-Temporal Remotely Sensed Data

作者其他论文 更多>>

Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering

Sensitivity Analysis of AquaCrop Model Parameters for Winter Wheat under Different Meteorological Conditions Based on the EFAST Method

Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning

Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (Annona squamosa L.)

Retrieving the chlorophyll content of individual apple trees by reducing canopy shadow impact via a 3D radiative transfer model and UAV multispectral imagery

Enhancing potato leaf protein content, carbon-based constituents, and leaf area index monitoring using radiative transfer model and deep learning

Segmentation and Fractional Coverage Estimation of Soil, Illuminated Vegetation, and Shaded Vegetation in Corn Canopy Images Using CCSNet and UAV Remote Sensing

意 见 箱

作者其他论文更多>>

意见箱