| 基于MODIS数据的黄淮海夏玉米高温风险空间分布 |
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| 引用本文： | 刘哲,汪雪滢,刘帝佑,昝糈莉,赵祖亮,李绍明,张晓东.基于MODIS数据的黄淮海夏玉米高温风险空间分布[J].农业工程学报,2018,34(9):175-181. |
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| 作者姓名： | 刘哲 汪雪滢 刘帝佑 昝糈莉 赵祖亮 李绍明 张晓东 |
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| 作者单位： | 中国农业大学信息与电气工程学院 |
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| 基金项目： | 国家自然科学基金资助项目（41771104）；北京市科委项目（D171100002317002） |
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| 摘 要： | 近年来中国高温灾害频发,对黄淮海地区的玉米生产造成较大影响。目前已有的高温风险研究多用的是气象站点的点源数据,针对气象站点数据对大范围区域代表性较差的问题,该文使用搭载在Aqua卫星上的MODIS陆地表面温度产品(MYD11A1),在研究其与气象日最高温度间具有显著相关性的基础上,使用遥感温度数据获取黄淮海夏玉米花期的高温风险空间分布,并结合高程、水体等地理环境因素分析高温风险的成因。结果表明:气象日高温数据与遥感温度数据间的决定系数R2为0.51,P0.001,存在显著的正相关性。通过遥感温度计算发现近年高温风险主要分布在秦岭山区北部以及城镇、村庄的周边地区,与实际情况相符。该研究对于大范围高温风险研究与玉米生产管理具有参考作用。
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| 关 键 词： | 遥感 农作物 夏播玉米花期 遥感温度数据 气象日最高温 高温风险 |
| 收稿时间： | 2017/12/26 0:00:00 |
| 修稿时间： | 2018/4/6 0:00:00 |
Spatial distribution of high temperature risk on summer maize in Huang-huai-hai Plain based on MODIS data |
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| Liu Zhe,Wang Xueying,Liu Diyou,Zan Xuli,Zhao Zuliang,Li Shaoming and Zhang Xiaodong.Spatial distribution of high temperature risk on summer maize in Huang-huai-hai Plain based on MODIS data[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(9):175-181. |
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| Authors: | Liu Zhe Wang Xueying Liu Diyou Zan Xuli Zhao Zuliang Li Shaoming and Zhang Xiaodong |
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| Institution: | College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China,College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China,College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China,College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China,College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China,College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China and College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China |
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| Abstract: | Abstract: Maize is one of the major crops and widely cultivated in china. Because maize is thermophilic crop, temperature has a huge influence during the maize growth, and become a significant meteorological factor in agriculture. High temperature will inhibit the growth of maize. In recent years, high temperature disasters occurred frequently in China, which has caused serious impact on maize production in the Huang-Huai-Hai Plain. The detection and monitoring of maize high temperature damage has become an important part of agricultural production management. At present, most of the high temperature risk studies use point source data from weather stations. The distribution of meteorological sites is limited due to the complexity of the terrain. Moreover, the temperature obtained by meteorological site is the temperature in the shade box at a height of 1.5 m above the ground. Therefore, the temperature of the weather station cannot represent the temperature of a wide area. In order to obtain those temperature data in the continuous regions, interpolation algorithm is usually used. But, the accuracy of interpolation algorithm is low. Remote sensing temperature measurement technology can obtain the surface temperature, and the precision can reach the pixel level. This technique can explore the spatial and temporal distribution of high temperature risk with land parcel accuracy and better express the temperature response of the plant canopy. The previous experimental data show that there is a close correlation between the temperature of the meteorological station and the temperature of MODIS LST inversion. In addition, the mobile window algorithm was used to obtain the spatial distribution of high temperature risk in the summer maize growing area in the Huang-Huai-Hai Plain, and combined with the geographical and environmental factors such as elevation and water body distribution to analyze the reasons for the formation of high temperature risk. Data from July to August during 2011-2014 have been analyzed, which is at the flowering stage of summer maize and is the key growth period of maize. The analysis of remote sensing image data shows that it can accurately obtain the spatial distribution of high temperature risk and provide support for agricultural high temperature risk assessment. In this study, we used the meteorological highest temperature as a benchmark to perform correlation analysis on the MYD11A1 remote sensing temperature data, and we analyzed the degree of correlation between the 2 kinds of data by decisive factor and root mean square error. By significance test, the correlation between the meteorological highest temperature and the remote sensing temperature data is significant, and P value is less than 0.001. Through remote sensing temperature calculation, it can be found that the high temperature risk area in recent years is mainly distributed in the northern part of the Qinling Mountains and other parts of cities and villages, consistent with the actual situation. Among them, mountains and inhabitant communities are the main reasons to the formation of high temperature anomalies. The reason is that the water has played a role in regulating the temperature of surrounding environment. The study can provide a reference for large-scale high temperature risk research and corn production management. |
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| Keywords: | remote sensing crops florescence of summer maize remote sensing temperature data meteorological highest temperature high temperature risk |
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