Yang Lei, Han Lijuan, Song Jinling, et al. Monitoring and evaluation of high temperature and heat damage of summer maize based on remote sensing data. J Appl Meteor Sci, 2020, 31(6): 749-758. DOI:  10.11898/1001-7313.20200610.
Citation: Yang Lei, Han Lijuan, Song Jinling, et al. Monitoring and evaluation of high temperature and heat damage of summer maize based on remote sensing data. J Appl Meteor Sci, 2020, 31(6): 749-758. DOI:  10.11898/1001-7313.20200610.

Monitoring and Evaluation of High Temperature and Heat Damage of Summer Maize Based on Remote Sensing Data

DOI: 10.11898/1001-7313.20200610
  • Received Date: 2020-03-17
  • Rev Recd Date: 2020-06-25
  • Publish Date: 2020-10-27
  • In the context of global warming, the high temperature heat damage of summer maize occurs frequently in recent years, which seriously affects the yield and quality of corn. As an important food crop, output of summer maize has a crucial impact on national food security. Most studies on the high temperature heat damage of summer maize are based on data of discrete weather stations, which are less representative for large areas; and there are few published works or studies using remote sensing data to monitor and evaluate the high temperature heat damage of summer maize.MOD09A1 land surface reflectance products are used to extract the Huang-Huai-Hai summer maize planting area. Combined with MOD/MYD11A1 land surface temperature products and ground measured temperature data, based on linear correlation between land surface temperature and air temperature, a method of combining multiple stepwise regression and principal component analysis is used to construct a high-temperature damage evaluation model for the Huang-Huai-Hai summer maize production area. Results show that the determination coefficient of daily average air temperature in the plain area is above 0.8, and the determination coefficient of daily maximum temperature is above 0.7, and passing the test of 0.001 level. The accuracy in mountain area is slightly lower, the determination coefficient of daily average temperature is above 0.7, and the determination coefficient of daily maximum temperature is above 0.6, passing the test of 0.001 level. The root mean square error of simulation results of daily average temperature and daily maximum temperature fluctuates within a small range of about 2℃, and the inversion accuracy of daily average temperature is higher than that of daily maximum temperature. Using this model to evaluate the high-temperature heat damage in the main summer maize production area of the Huang-Huai-Hai from 2008 to 2018, it is found that the high-temperature heat damage area increases, and the spatial distribution are similar. Affected areas are mainly in the southern part of Beijing-Tianjin-Hebei region, the northern part of Henan Province, and the western part of Shandong Province. The main summer maize production areas in 2017 and 2018 are severely affected by high temperature heat damage. The affected areas are mainly distributed in the southeast of Hebei Province, most of Henan Province and western Shandong Province. This study has an important reference role for the development of a large-scale summer corn high temperature monitoring and evaluation work.
  • Fig. 1  Summer maize distribution in the study area from 2008 to 2018

    Fig. 2  Comparison of MODIS and Landsat extraction results

    Fig. 3  Scattered distribution of simulated temperature and measured temperature in plain and mountain areas

    Fig. 4  Distribution of disaster levels in the study area in 2017 and 2018

    Table  1  High temperature heat damage grade index for summer maize

    高温热害等级 日平均气温/℃ 日最高气温/℃ 持续日数/d
    1 ≥30 ≥35 3≤D < 5
    2 ≥30 ≥35 5≤D < 8
    ≥32 ≥37 3≤D < 5
    3 ≥30 ≥35 D≥8
    ≥32 ≥37 5≤D < 8
    4 ≥32 ≥37 D≥8
    DownLoad: Download CSV

    Table  2  The temperature fitting model

    区域 气温类型 模型 决定系数 均方根误差/℃
    平原 山区 平原 山区
    京津冀 日平均气温 Yave=0.085TOD+0.444TON+0.176TYD+
    0.323TYN+0.299
    0.8202 0.8109 1.28 2.32
    日最高气温 Ymax=0.177TOD+0.152TON+0.223TYD+
    0.281TYN+8.380
    0.7021 0.6848 1.94 2.48
    河南省 日平均气温 Yave=0.310TOD+0.138TON+0.394TYD+
    0.046TYN+0.876
    0.8112 0.7356 2.03 2.36
    日最高气温 Ymax=0.102TOD+0.301TON+0.194TYD+
    0.284TYN+4.307
    0.7126 0.7027 1.73 1.97
    山东省 日平均气温 Yave=0.292TOD+0.168TON+0.440TYD+
    0.058TYN+1.973
    0.8605 0.8143 2.33 2.51
    日最高气温 Ymax=0.076TOD+0.270TON+0.288TYD+
    0.260TYN+5.290
    0.7893 0.6694 1.20 3.02
    DownLoad: Download CSV

    Table  3  Area affected by high temperature from 2008 to 2018

    区域 年份 总受灾面积/hm2 一级受灾面积/hm2 二级受灾面积/hm2 三级受灾面积/hm2
    京津冀 2008年 0 0 0 0
    2009年 4.15×105 4.02×105 1.33×104 0
    2010年 2.00×103 1.90×103 1.00×102 0
    2013年 6.66×105 6.66×105 3.00×102 0
    2014年 7.86×105 7.86×105 0 0
    2015年 9.25×104 9.07×104 1.70×103 1.00×102
    2016年 4.85×104 4.85×104 0 0
    2017年 2.05×106 1.99×106 6.05×104 0
    2018年 3.42×106 1.81×106 1.50×106 1.16×105
    河南省 2008年 4.23×104 4.23×104 0 0
    2009年 6.29×105 6.04×105 2.51×104 0
    2010年 1.17×104 9.30×103 2.40×103 0
    2013年 1.14×106 1.12×106 1.19×104 0
    2014年 6.73×105 6.61×105 1.78×104 0
    2015年 2.55×105 2.32×105 2.35×104 0
    2016年 1.47×105 1.46×105 1.20×103 0
    2017年 3.41×106 1.03×106 2.19×106 1.86×105
    2018年 3.40×106 1.00×106 2.19×106 2.09×105
    山东省 2008年 0 0 0 0
    2009年 1.83×104 1.83×104 0 0
    2010年 0 0 0 0
    2013年 2.04×105 2.04×105 0 0
    2014年 4.72×104 4.72×104 1.21×104 0
    2015年 9.36×104 7.63×104 1.62×104 1.10×103
    2016年 3.24×104 3.24×104 0 0
    2017年 1.22×106 1.22×106 0 0
    2018年 3.23×106 1.27×106 1.39×106 5.73×105
    DownLoad: Download CSV
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    • Received : 2020-03-17
    • Accepted : 2020-06-25
    • Published : 2020-10-27

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