三江平原水旱田分布对遥感反演局地地表温度的影响
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摘要
土地利用剧烈变化对局地气候的影响是当前全球变化与可持续研究关注的焦点问题。该文基于2017年6月16日Landsat 8卫星遥感影像反演耕地利用剧烈变化的三江平原北部地区地表温度数据,刻画了研究时点水旱田分布对地表温度影响的空间特征。结果表明,研究区水田旱地面积占耕地总面积比例分别为58.12%、41.88%,水田平均地表温度比旱地低9.87℃;在村域尺度上,水田面积占比与地表温度均值在0.01水平(双侧)上呈显著负相关关系(P<0.01),该研究时点上水田对地表热环境存在较为明显的降温作用。水旱田不同下垫面因含水量及蒸散量不同造成的能量传递过程差异,使得其分布格局成为地表温度值分异的主要原因。
The impact of rapid land-use conversion on the local climate is the focus of global change and sustainable research. Large-scale upland conversion to paddy fields and its influence on the local climate in the Sanjiang Plain area should be the important content of current climate impact research on human land use activities. In order to explore its influence on the land surface temperature, we used the atmospheric correction temperature inverse method based on the Landsat 8 TIRS remote sensing image data on June 16, 2017. The basic principle of this method is to remove the atmospheric influence and use the atmospheric radiation transfer equation; surface heat radiation intensity observed by the satellite is converted to the corresponding surface temperature. The parameters involved are mainly atmospheric profile parameters and surface specific emissivity. Through the temperature inversion, the surface temperature data of the northern part of the Sanjiang Plain was obtained. Then, the mean-standard deviation method was used to divide the overall temperature data of the cultivated land in the study area into five different grades, which visually showed the overall distribution characteristics of the land surface temperature values of the cultivated land in the study area. The land-use data was updated and obtained from the national rural land survey data, which scale was 1∶10000. On this basis, we analyze the correlation between the paddy field planting area and the average land surface temperature in the different paddy field planting level divisions, and characterized the space effect of different patterns of dry field and paddy fields on the surface temperature feature. Through this series of data statistics and analysis process, the following results can be obtained: the research area we selected is the cultivated land concentration area, and the cultivated land area within the scope is 73.63%. Cultivated land types in the study area are mainly paddy fields and dry land. The paddy field planting area is 7.2×105 hm2, accounting for 58.12% of the total area of cultivated land, while the dry field planting area is slightly lower than that of paddy field with area 5.2×105 hm2, accounting for 41.88%. According to the zoning statistics, we know that in the paddy field planting area average surface temperature in the interior is 27.73 ℃, while the average surface temperature in the dry field is 37.60 ℃, which is 9.87 ℃ higher than that in the paddy field. In addition, from the aspect of spatial distribution, the surface temperature is lower in the northwest and the north which is higher in the southwestern part and the eastern part of the study area. After correlation analysis, we find that the paddy field area ratio and the surface temperature mean value are significantly negative at 0.01(two sides)(P<0.01). These findings reveal that different underlying surface types in the arable area are the necessary factors of surface temperature differentiation. During the study period, the paddy field has a significant cooling effect on the land surface thermal environment. The variation in energy transfer process caused by different water content and evapotranspiration in different underlying surfaces of dry field and paddy fields makes its distribution pattern the main cause of land surface temperature value differentiation.
The impact of rapid land-use conversion on the local climate is the focus of global change and sustainable research. Large-scale upland conversion to paddy fields and its influence on the local climate in the Sanjiang Plain area should be the important content of current climate impact research on human land use activities. In order to explore its influence on the land surface temperature, we used the atmospheric correction temperature inverse method based on the Landsat 8 TIRS remote sensing image data on June 16, 2017. The basic principle of this method is to remove the atmospheric influence and use the atmospheric radiation transfer equation; surface heat radiation intensity observed by the satellite is converted to the corresponding surface temperature. The parameters involved are mainly atmospheric profile parameters and surface specific emissivity. Through the temperature inversion, the surface temperature data of the northern part of the Sanjiang Plain was obtained. Then, the mean-standard deviation method was used to divide the overall temperature data of the cultivated land in the study area into five different grades, which visually showed the overall distribution characteristics of the land surface temperature values of the cultivated land in the study area. The land-use data was updated and obtained from the national rural land survey data, which scale was 1∶10000. On this basis, we analyze the correlation between the paddy field planting area and the average land surface temperature in the different paddy field planting level divisions, and characterized the space effect of different patterns of dry field and paddy fields on the surface temperature feature. Through this series of data statistics and analysis process, the following results can be obtained: the research area we selected is the cultivated land concentration area, and the cultivated land area within the scope is 73.63%. Cultivated land types in the study area are mainly paddy fields and dry land. The paddy field planting area is 7.2×105 hm2, accounting for 58.12% of the total area of cultivated land, while the dry field planting area is slightly lower than that of paddy field with area 5.2×105 hm2, accounting for 41.88%. According to the zoning statistics, we know that in the paddy field planting area average surface temperature in the interior is 27.73 ℃, while the average surface temperature in the dry field is 37.60 ℃, which is 9.87 ℃ higher than that in the paddy field. In addition, from the aspect of spatial distribution, the surface temperature is lower in the northwest and the north which is higher in the southwestern part and the eastern part of the study area. After correlation analysis, we find that the paddy field area ratio and the surface temperature mean value are significantly negative at 0.01(two sides)(P<0.01). These findings reveal that different underlying surface types in the arable area are the necessary factors of surface temperature differentiation. During the study period, the paddy field has a significant cooling effect on the land surface thermal environment. The variation in energy transfer process caused by different water content and evapotranspiration in different underlying surfaces of dry field and paddy fields makes its distribution pattern the main cause of land surface temperature value differentiation.
引文
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[2]孙华,何茂萍,胡明成.全球变化背景下气候变暖对中国农业生产的影响[J].中国农业资源与区划,2015,36(7):51-57.Sun Hua,He Maoping,Hu Mingcheng.Impact of global climatic warming on agricultural production in China[J].Chinese Journal of Agricultural Resources and Regional Planning,2015,36(7):51-57.(in Chinese with English abstract)
[3]杜国明,潘涛,尹哲睿,等.水田化进程中的富锦市耕地景观格局演化规律[J].应用生态学报,2015,26(1):207-214.Du Guoming,Pan Tao,Yin Zherui,et al.Cultivated landscape pattern change due to the rice paddy expansion in Northeast China:A case study in Fujin[J].Chinese Journal of Applied Ecology,2015,26(1):207-214.(in Chinese with English abstract)
[4]杜国明,马敬盼,春香.现代化农区耕地利用形态转型研究[J].中国农业资源与区划,2018,39(3):185-192.Du Guoming,Ma Jingpan,Chun Xiang.Study on the transformation of cultivated land use in modern agriculture region[J].Chinese Journal of Agricultural Resources and Regional Planning,2018,39(3):185-192.(in Chinese with English abstract)
[5]Stocker T F,Qin D,Platiner G K,et al.Climate Change 2013:The Physical Science Basis[R].Intergovernmental Panel on Climate Change,2013.
[6]Dong J,Xiao X,Zhang G,et al.Northward expansion of paddy rice in northeastern Asia during 2000-2014[J].Geophysical Research Letters,2016,8(43):3754-3761.
[7]刘纪远,宁佳,匡文慧,等.2010-2015年中国土地利用变化的时空格局与新特征[J].地理学报,2018,73(5):789-802.Liu Jiyuan,Ning Jia,Kuang Wenhui,et al.Spatio-temporal patterns and characteristics of land-use change in China during 2010-2015[J].Acta Geographica Sinica,2018,73(5):789-802.(in Chinese with English abstract)
[8]Georgescu M,Lobell D B,Field C B.Potential impact of USbiofuels on regional climate[J].Geophysical Research Letters,2009,36(21):147-148.
[9]Galford G L,Melillo J M,Kicklighter D W,et al.Greenhouse gas emissions from alternative futures of deforestation and agricultural management in the southern Amazon[J].Proceedings of the National Academy of Sciences,2010,107(46):19649-19654.
[10]杜国明,李昀,于凤荣,等.基于遥感的2000-2009年三江平原北部耕地变化特征分析[J].农业工程学报,2012,28(1):225-229.Du Guoming,Li Yun,Yu Fengrong,et al.Change characteristics analysis of farmland in Northern Sanjiang Plain in 2000-2009 based on remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(1):225-229.(in Chinese with English abstract)
[11]栾兆擎,章光新,邓伟,等.三江平原50年来气温及降水变化研究[J].干旱区资源与环境,2007,21(11):39-43.Luan Zhaoqing,Zhang Guangxin,Deng Wei,et al.Study on the changes of air temperature and precipitation in the last 50years in the Sanjiang Plain[J].Journal of Arid Land Resources and Environment,2007,21(11):39-43.(in Chinese with English abstract)
[12]刘焕军,杨昊轩,徐梦园,等.基于裸土期多时相遥感影像特征及最大似然法的土壤分类[J].农业工程学报,2018,34(14):132-139.Liu Huanjun,Yang Haoxuan,Xu Mengyuan,et al.Soil classification based on maximum likelihood method and features of multi-temporal remote sensing images in bare soil period[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(14):132-139.(in Chinese with English abstract)
[13]姚远,陈曦,钱静.城市地表热环境研究进展[J].生态学报,2018,38(3):1134-1147.Yao Yuan,Chen Xi,Qian Jing.Research progress on the thermal environment of the urban surfaces[J].Acta Ecologica Sinica,2018,38(3):1134-1147.(in Chinese with English abstract)
[14]戚鹏程,赵传燕,冯兆东,等.干旱荒漠区土地利用变化对地表温度的影响[J].农业工程学报,2010,26(11):310-316.Qi Pengcheng,Zhao Chuanyan,Feng Zhaodong,et al.Influence of land use change on land surface temperature in arid desert region[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(11):310-316.(in Chinese with English abstract)
[15]宋挺,段峥,刘军志,等.Landsat 8数据地表温度反演算法对比[J].遥感学报,2015,19(3):451-464.Song Ting,Duan Zheng,Liu Junzhi,et al.Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite[J].Journal of Remote Sensing,2015,19(3):451-464.(in Chinese with English abstract)
[16]蒋大林,匡鸿海,曹晓峰,等.基于Landsat8的地表温度反演算法研究--以滇池流域为例[J].遥感技术与应用,2015,30(3):448-454.Jiang Dalin,Kuang Honghai,Cao Xiaofeng,et al.Study of land surface temperature retrieval based on Landsat 8:With the sample of Dianchi Lake Basin[J].Remote Sensing Technology and Application,2015,30(3):448-454.(in Chinese with English abstract)
[17]胡德勇,乔琨,王兴玲,等.单窗算法结合Landsat8热红外数据反演地表温度[J].遥感学报,2015,19(6):964-976.Hu Deyong,Qiao Kun,Wang Xingling,et al.Land surface temperature retrieval from Landsat 8 thermal infrared data using mono-window algorithm[J].Journal of Remote Sensing,2015,19(6):964-976.(in Chinese with English abstract)
[18]徐涵秋.Landsat 8热红外数据定标参数的变化及其对地表温度反演的影响[J].遥感学报,2016,20(2):229-235.Xu Hanqiu.Change of Landsat 8 TIRS calibration parameters and its effect on land surface temperature retrieval[J].Journal of Remote Sensing,2016,20(2):229-235.(in Chinese with English abstract)
[19]李召良,段四波,唐伯惠,等.热红外地表温度遥感反演方法研究进展[J].遥感学报,2016,20(5):899-920.Li Zhaoliang,Duan Sibo,Tang Bohui,et al.Review of methods for land surface temperature derived from thermal infrared remotely sensed data[J].Journal of Remote Sensing,2016,20(5):899-920.(in Chinese with English abstract)
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