数字农科院2.0

Effects of meteorological factors on different grades of winter wheat growth in the Huang-Huai-Hai Plain, China

文献类型： 外文期刊

作者： Huang Qing;Wang Li-min;Chen Zhong-xin;Liu Hang

作者机构：

关键词： growth condition;meteorological factors;remote sensing;spatiotemporal correlation;winter wheat;Huang-Huai-Hai (HHH) Plain region;China

期刊名称： JOURNAL OF INTEGRATIVE AGRICULTURE

ISSN： 2095-3119

年卷期： 2016 年 15 卷 11 期

页码：

收录情况： SCI

摘要： The sown area of winter wheat in the Huang-Huai-Hai (HHH) Plain accounts for over 65% of the total sown area of winter wheat in China. Thus, it is important to monitor the winter wheat growth condition and reveal the main factors that influence its dynamics. This study assessed the winter wheat growth condition based on remote sensing data, and investigated the correlations between different grades of winter wheat growth and major meteorological factors corresponding. First, winter wheat growth condition from sowing until maturity stage during 2011-2012 were assessed based on moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series dataset. Next, correlation analysis and geographical information system (GIS) spatial analysis methods were used to analyze the lag correlations between different grades of winter wheat growth in each phenophase and the meteorological factors that corresponded to the phenophases. The results showed that the winter wheat growth conditions varied over time and space in the study area. Irrespective of the grades of winter wheat growth, the correlation coefficients between the winter wheat growth condition and the cumulative precipitation were higher than zero lag (synchronous precipitation) and one lag (pre-phenophase precipitation) based on the average values of seven phenophases. This showed that the cumulative precipitation during the entire growing season had a greater effect on winter wheat growth than the synchronous precipitation and the pre-phenophase precipitation. The effects of temperature on winter wheat growth varied according to different grades of winter wheat growth based on the average values of seven phenophases. Winter wheat with a better-than-average growth condition had a stronger correlation with synchronous temperature, winter wheat with a normal growth condition had a stronger correlation with the cumulative temperature, and winter wheat with a worse-than-average growth condition had a stronger correlation with the pre-phenophase temperature. This study may facilitate a better understanding of the quantitative correlations between different grades of crop growth and meteorological factors, and the adjustment of field management measures to ensure a high crop yield.

分类号：

  • 相关文献

[1]Remote-Sensing Based Winter Wheat Growth Dynamic Changes and the Spatial-Temporal Relationship with Meteorological Factor. Huang Qing,Zhou Qingbo,Wu Wenbin,Li Dandan. 2014

[2]Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Tang, Huajun,Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Tang, Huajun. 2008

[3]Application of EOS/MODIS-NDVI at Different Time Sequences on Monitoring Winter Wheat Acreage in Henan Province. Cheng Deng-fa. 2009

[4]EXTRACTING SPATIAL INFORMATION OF HARVEST INDEX FOR WINTER WHEAT BASED ON MODIS NDVI IN NORTH CHINA. Ren, Jianqiang,Chen, Zhongxin,Tang, Huajun,Ren, Jianqiang,Chen, Zhongxin,Tang, Huajun,Liu, Xingren. 2010

[5]Regional yield prediction for winter wheat based on crop biomass estimation using multi-source data. Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Tang, Huajun,Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Tang, Huajun,Li, Su. 2007

[6]Extracting Winter Wheat Planting Area Based on Cropping System with Remote Sensing. Li, Shaokun,Zhu, Zhenlin,Sui, Xueyan,Zhang, Xiaodong,Zhu, Zhenlin,Sun, Xiaoqing,Li, Shaokun. 2011

[7]Estimating the winter wheat harvest index with canopy hyperspectral remote sensing data based on the dynamic fraction of post-anthesis phase biomass accumulation. Zhang, Ningdan,Liu, Xingren,Ren, Jianqiang,Wu, Shangrong,Li, Fangjie. 2022

[8]Monitoring and Mapping Winter Wheat Spring Frost Damage with MODIS Data and Statistical Data. Di Chen,Buchun Liu,Tianjie Lei,Xiaojuan Yang,Yuan Liu,Wei Bai,Rui Han,Huiqing Bai,Naijie Chang. 2023

[9]Remote sensing-based analysis of yield and water-fertilizer use efficiency in winter wheat management. Weiguang Zhai,Qian Cheng,Fuyi Duan,Xiuqiao Huang,Zhen Chen. 2025

[10]Long-Term Inorganic Plus Organic Fertilization I.ncreases Yield And Yield S tability Of Winter Wheat. Chen, H, Deng, AX, Zhang, WJ, Li, W, Qi, YQ, Yang, TM, Zheng, CY, Cao, CF, Chen, F. 2018

[11]Characterisation of high- and low-molecular-weight glutenin subunit genes in Chinese winter wheat cultivars and advanced lines using allele-specific markers and SDS-PAGE. Yang, F. P.,Wang, L. H.,Wang, J. W.,He, X. Y.,Xia, X. C.,He, Z. H.,Yang, F. P.,Yang, W. X.,Wang, J. W.,Zhang, X. K.,Shang, X. W.,He, Z. H..

[12]Effect of alternative tillage and residue cover on yield and water use efficiency in annual double cropping system in North China Plain. He Jin,Wang Qingjie,Li Hongwen,Gao Huanwen,Liu Lijin.

[13]Geographical detector-based wheat quality attribution under genotype, environment, and crop management frameworks. Zhang X.,Ma X.,Li Y.,Ju H.. 2022

[14]Assessment of grassland degradation in Guinan county, Qinghai Province, China, in the past 30 years. Feng, Y.,Lu, Q.,Wang, X.,Tokola, T.,Liu, H..

[15]Identification of potential areas for biomass production in China: Discussion of a recent approach and future challenges. Schweers, Wilko,Zhihao, Qin,Cai, Dianxiong,Jin, Yunxiang,Bai, Zhanguo,Campbell, Elliott,Hennenberg, Klaus,Fritsche, Uwe,Mang, Heinz-Peter,Lucas, Mario,Li, Zifu,Scanlon, Andrew,Chen, Haoran,Zhihao, Qin,Zhang, Jun,Tu, Lili,Gemmer, Marco,Jiang, Tong,Zhang, Nannan. 2011

[16]Valuation of rangeland ecosystem degradation with remote sensing technology in China. Wang, Ruijie,Qin, Zhihao,Jiang, Lipeng,Ye, Ke,Qin, Zhihao. 2006

[17]A Process-Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services. Zhongen Niu,Honglin He,Shushi Peng,Xiaoli Ren,Li Zhang,Fengxue Gu,Gaofeng Zhu,Changhui Peng,Pan Li,Junbang Wang,Rong Ge,Na Zeng,Xiaobo Zhu,Yan Lv,Qingqing Chang,Qian Xu,Mengyu Zhang,Weihua Liu. 2021

[18]A hybrid CNN-LSTM model for predicting PM2.5 in Beijing based on spatiotemporal correlation. Chen Ding,Guizhi Wang,Xinyue Zhang,Qi Liu,Xiaodong Liu. 2021

[19]Integrated effects of meteorological factors, edaphic moisture, evapotranspiration, and leaf area index on the net primary productivity of Winter wheat − Summer maize rotation system. Yang Han,Hongfei Lu,Dongmei Qiao. 2023

[20]Ensemble learning prediction of soybean yields in China based on meteorological data. Qian chuan LI,Shi wei XU,Jia yu ZHUANG,Jia jia LIU,Yi ZHOU,Ze xi ZHANG. 2023

作者其他论文 更多>>