Knowledge Resource Center for Ecological Environment in Arid Area
Study on Yield Estimation of Spring Wheat basing on Hyperspectral data under Different Meteorological Condition in Semi-Arid Rain Fed Region | |
Sha Sha1; Wang Xiaoping1; Li Qiaozhen2; Li Weidong3 | |
通讯作者 | Sha Sha |
会议名称 | 5th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) |
会议日期 | JUL 18-20, 2016 |
会议地点 | Tianjin, PEOPLES R CHINA |
英文摘要 | Statistically relating vegetation index to yield is a common wheat yield estimation method. In this paper, we investigate the relationship between a variety of spectral vegetation indices and yield factors for spring wheat in a semiarid, rain-fed, agricultural region under different meteorological conditions on the basis of relevant ground observations. We also analyze the yield estimation factor of remote sensing for spring wheat by regression method under different meteorological conditions. Results are as follows. 1) The theoretical yield per unit area, thousand-kernel weight, grains per ear, and number of productive tillers per square meters at the milk stage of maturity are relatively small. These data exhibit flat variation trends with spectral vegetation indices under drought conditions. By contrast, the trends under non-drought conditions are significantly changing. 2) Meanwhile, the spectral vegetation indices under drought and non-drought conditions are appreciably associated with the theoretical yields at the booting (0.01) and heading stages (0.001). 3) In the meteorological droughts, the aggregate value of the semi-arid water index at the booting and heading stages is suitable for use as the yield estimation factor for spring wheat. However, under the meteorological non-droughts, the RVI(p 780/p1750) at the booting stage is used as the yield estimation factor for spring wheat. The mean absolute percentage errors of the yield estimations in the two cases are 70.9% and 84.2%, respectively. |
英文关键词 | Meteorological Condition Spring Wheat Dingxi |
来源出版物 | 2016 FIFTH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS) |
ISSN | 2334-3168 |
出版年 | 2016 |
页码 | 242-247 |
EISBN | ***************** |
出版者 | IEEE |
类型 | Proceedings Paper |
语种 | 英语 |
国家 | Peoples R China |
收录类别 | CPCI-S |
WOS记录号 | WOS:000391252300048 |
WOS关键词 | MODIS ; NDVI |
WOS类目 | Agriculture, Multidisciplinary ; Computer Science, Information Systems |
WOS研究方向 | Agriculture ; Computer Science |
资源类型 | 会议论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/305329 |
作者单位 | 1.CMA, Inst Arid Meteorol, Key Lab Arid Climate Change & Reducing Disaster G, Key Open Lab Arid Climate Change & Disaster Reduc, Lanzhou, Peoples R China; 2.Meteorol Bur Dingxi Gansu Prov, Dingxi, Peoples R China; 3.Gansu Prov Meteorol Bur, Lanzhou, Peoples R China |
推荐引用方式 GB/T 7714 | Sha Sha,Wang Xiaoping,Li Qiaozhen,et al. Study on Yield Estimation of Spring Wheat basing on Hyperspectral data under Different Meteorological Condition in Semi-Arid Rain Fed Region[C]:IEEE,2016:242-247. |
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