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
  • [1]
    刘聪.全球气候变化背景下应用温度三区间理论对郑州地区夏玉米高温热害规律研究.现代农业研究, 2018(9):20-22;28. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ncsykjxx201809008
    [2]
    杨萍, 刘伟东, 王启光, 等.近40年我国极端温度变化趋势和季节特征.应用气象学报, 2010, 21(1):29-36. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb201001004
    [3]
    杨彬云, 吴荣军, 杨保东, 等.近40年河北省地表干燥度的时空变化.应用气象学报, 2009, 20(6):745-752. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb200906013
    [4]
    刘笑.农业气象灾害和气温降水对华北平原粮食产量的影响.北京:中国农业科学院, 2018.
    [5]
    郭建茂, 王锦杰, 吴越, 等.基于卫星遥感与气象站数据的水稻高温热害监测和评估模型研究——以江苏、安徽为例.农业现代化研究, 2017, 38(2):298-306. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=671673166
    [6]
    郭建茂, 王锦杰, 吴越, 等.基于卫星遥感与气象站点数据的水稻高温热害监测和评估模型的改进.自然灾害学报, 2018, 27(1):163-174. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zrzhxb201801020
    [7]
    闵文彬, 李跃清.MODIS反演地表温度与地面同步气温、地温的相关分析.中国气象学会年会卫星遥感应用技术与处理方法分会场, 2008.
    [8]
    刘梅.MODIS影像中云覆盖像元地表温度的估算研究.南京: 南京大学, 2012.
    [9]
    李天祺, 朱秀芳, 潘耀忠, 等.MODIS陆地表面温度数据重构方法研究.北京师范大学学报(自然科学版), 2015, 51(增刊I):70-76. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bjsfdxxb2015z1011
    [10]
    刘哲, 汪雪滢, 刘帝佑, 等.基于MODIS数据的黄淮海夏玉米高温风险空间分布.农业工程学报, 2018, 34(9):175-181. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nygcxb201809021
    [11]
    刘哲, 乔红兴, 赵祖亮, 等.黄淮海夏播玉米花期高温热害空间分布规律研究.农业机械学报, 2015, 46(7):272-279. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nyjxxb201507039
    [12]
    霍治国, 尚莹, 邬定荣, 等.中国小麦干热风灾害研究进展.应用气象学报, 2019, 30(2):129-141. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb201902001
    [13]
    张佳华, 姚凤梅, 李秉柏, 等.星-地光学遥感信息监测水稻高温热害研究进展.中国科学(地球科学), 2011, 41(10):1396-1406. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgkx-cd201110002
    [14]
    宫丽娟, 李秀芬, 田宝星, 等.黑龙江省大豆不同生育阶段干旱时空特征.应用气象学报, 2020, 31(1):95-104. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb202001009
    [15]
    杨建莹, 霍治国, 王培娟, 等.江西早稻高温热害发生时间分布特征.应用气象学报, 2020, 31(1):42-51. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb202001004
    [16]
    豆玉洁.水稻高温热害遥感监测方法研究.杭州: 浙江大学, 2019.
    [17]
    骆宗强, 石春林, 江敏.水稻高温热害预警监测与定量评估研究进展.江苏农业科学, 2016, 44(4):12-15. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsnykx201604004
    [18]
    Zhu W B, Lu A F, Jia S F.Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products.Remote Sens Environ, 2013, 130:62-73. http://www.sciencedirect.com/science/article/pii/S0034425712004221
    [19]
    陈丽娟, 顾伟宗, 伯忠凯, 等.黄淮地区夏季降水的统计降尺度预测.应用气象学报, 2017, 28(2):129-141. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb201702001
    [20]
    陈怀亮, 刘玉洁, 杜子璇, 等.黄淮海地区植被生长季变化及其气候变化响应.应用气象学报, 2011, 22(4):437-444. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb201104006
    [21]
    Wan Z, Dozier J.A generalized split-window algorithm for retrieving land-surface temperature from space.IEEE Trans Geosci Remote Sens, 1996, 34(4):892-905. doi:  10.1109/36.508406
    [22]
    Wan Z M.Collection-5 MODIS Land-Surface Temperature Products Users'Guide.Institute for Computational Earth System Science, 2006-09-15[2005-01-03].http://www.icess.ucsb.edu/modis/LstUsrGuide/MODIS_LST_products_Users_guide_C5.pdf.
    [23]
    任义方, 赵艳霞, 王春乙.河南省冬小麦干旱保险风险评估与区划.应用气象学报, 2011, 22(5):537-548. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb201105003
    [24]
    Goetz S J.Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site.Int J Remote Sens, 1997, 18(1):71-94. doi:  10.1080/014311697219286
    [25]
    Price J C.Using spatial context in satellite data to infer regional scale evapotranspiration.IEEE Trans Geosc Remote Sens, 1990, 28(5):940-948. doi:  10.1109/36.58983
    [26]
    陈晓停, 曹兰杰, 汪金花.基于决策树的县域冬小麦种植面积提取.地理空间信息, 2018, 16(9):85-86;98;12. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlkjxx201809026
    [27]
    郭文茜.遥感和统计数据融合的冬小麦分布提取及其时空变化分析.北京: 中国农业科学院, 2018.
    [28]
    刘红超, 梁燕, 张喜旺.多时相影像的冬小麦种植面积提取及估产.遥感信息, 2017, 32(5):87-92. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ygxx201705014
    [29]
    郭昱杉, 刘庆生, 刘高焕, 等.基于MODIS时序NDVI主要农作物种植信息提取研究.自然资源学报, 2017, 32(10):1808-1818. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zrzyxb201710014
    [30]
    Jin M, Dickinson R E.Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle:1.Without clouds.J Geophys Res Atmos, 1999, 104(D2):2105-2116. doi:  10.1029/1998JD200005
    [31]
    Lu L, Venus V, Skidmore A, et al.Estimating land-surface temperature under clouds using MSG/SEVIRI observations.Int J Appl Earth Obs, 2011, 13(2):265-276. doi:  10.1016/j.jag.2010.12.007
    [32]
    李德, 孙义, 孙有丰.淮北平原夏玉米花期高温热害综合气候指标研究.中国生态农业学报, 2015, 23(8):1035-1044. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=stnyyj201508014
    [33]
    吴玮, 景元书, 马玉平, 等.干旱环境下夏玉米各生育时期光响应特征.应用气象学报, 2013, 24(6):723-730. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb201306009
    [34]
    宋艳玲, 王建林, 田靳峰, 等.气象干旱指数在东北春玉米干旱监测中的改进.应用气象学报, 2019, 30(1):25-34. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb201901003
    [35]
    彭艳勤, 张天俊.淮阳县2016年夏玉米高温热害情况探析.农民致富之友, 2016(20):87. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nmzfzy201620079
    [36]
    王丽君.黄淮海平原夏玉米季干旱、高温的发生特征及对产量的影响.北京: 中国农业大学, 2018.
    [37]
    尚莹, 霍治国, 张蕾, 等.土壤相对湿度对冬小麦干热风灾害发生的影响.应用气象学报, 2019, 30(5):598-607. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb201905008
    [38]
    赵晓丹, 孔箐锌, 沈娟, 等.浅析温度对玉米产量与质量的影响.农业工程技术, 2018, 38(29):72;74. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=nongygcjs201829050
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    • Received : 2020-03-17
    • Accepted : 2020-06-25
    • Published : 2020-10-27

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