Spatial distribution of high temperature risk on summer maize in Huang-huai-hai Plain based on MODIS data

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.