基于MODIS数据的黄淮海夏玉米高温风险空间分布
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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,P<0.001,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。
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 MYD11 A1 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.
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 MYD11 A1 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.
引文
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[8]陆伟婷,于欢,曹胜男,等.近20年黄淮海地区气候变暖对夏玉米生育进程及产量的影响[J].中国农业科学,2015,48(16):3132-3145.Lu Weiting,Yu Huan,Cao Shengnan,et al.Effects of climate warming on growth process and yield of summer maize in Huang-Huai-Hai plain in last 20 years[J].Scientia Agricultura Sinica,2015,48(16):3132-3145.(in Chinese with English abstract)
[9]徐玲玲,吕厚荃,方利.气候变化对黄淮海地区夏玉米气候适宜度的影响[J].资源科学,2014,36(4):782-787.Xu Lingling,Lv Houquan,Fang Li.Effects of climate change on the climate suitability of summer maize on the HuangHuai-Hai plain[J].Resources Science,2014,36(4):782-787.(in Chinese with English abstract)
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