地表温度合成方式对TVDI预测精度影响
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摘要
针对目前干旱指数(temperature vegetation dryness index,TVDI)构建过程中地表温度(land surface temperature,LST)合成方式的不确定性问题,探讨了LST合成方式对TVDI预测精度的影响,提出了预测效果较好的LST合成方式。以巢湖流域为研究区,利用2013年6月的MODIS LST数据和归一化植被指数数据,构建TVDI预测模型,并结合降水数据对该模型预测结果进行定量验证。从不同时间尺度上(旬、月),探讨LST合成方式对于TVDI预测精度的影响。研究结果表明:(1)LST平均值合成构建的干旱指数TVDI与降水距指数(precipitation anomaly,PA)相关性在不同时间尺度上表现出现较大差异,上旬和中旬TVDI与PA均不存在相关性,下旬和全月TVDI与PA存在显著负相关性(p<0.01),相关系数分别为-0.31和-0.34;(2)LST最大值合成构建的干旱指数TVDI与PA在不同时间尺度上均存在显著负相关性(p<0.01或p<0.05),上旬、中旬、下旬及全月相关系数分别为-0.29、-0.25、-0.31、-0.41。综合分析发现,在月尺度上采用LST最大值合成方式构建TVDI指数对干旱预测效果更好。
In view of the uncertainty of the land surface temperature(LST)synthesis methods in the temperature vegetation dryness index(TVDI)construction process,this paper discusses the influence of LST synthesis methods on TVDI prediction accuracy,and proposes a better LST synthesis method.Taking Chaohu Lake Basin as the research area,the TVDI prediction model is constructed based on MODIS LST data and NDVI data from June 2013,and the model accuracy is quantitatively verified with precipitation data.The effect of LST synthesis methods on the accuracy of TVDI in the drought prediction is investigated on different time scales(10 days and the whole month).The results show that the correlation between TVDI constructed by LST mean value synthesis and precipitation anomaly(PA)is significantly different on different time scales.There is no correlation between TVDI and PA in the first 10 days and the middle 10 days,there is a significant negative correlation between TVDI and PA in late 10 days and the whole month(p<0.01),and the correlation coefficients are-0.31 and-0.34,respectively.There is a significant negative correlation between TVDI constructed by LST maximum value synthesis and PA on different time scales(p<0.01 or p<0.05),and the correlation coefficients of the first 10 days,the middle 10 days,the late10 days and the whole months are-0.29,-0.25,-0.31 and-0.41,respectively.The comprehensive analysis shows that the TVDI constructed by LST maximum value synthesis on the monthly scale is better for drought prediction.
In view of the uncertainty of the land surface temperature(LST)synthesis methods in the temperature vegetation dryness index(TVDI)construction process,this paper discusses the influence of LST synthesis methods on TVDI prediction accuracy,and proposes a better LST synthesis method.Taking Chaohu Lake Basin as the research area,the TVDI prediction model is constructed based on MODIS LST data and NDVI data from June 2013,and the model accuracy is quantitatively verified with precipitation data.The effect of LST synthesis methods on the accuracy of TVDI in the drought prediction is investigated on different time scales(10 days and the whole month).The results show that the correlation between TVDI constructed by LST mean value synthesis and precipitation anomaly(PA)is significantly different on different time scales.There is no correlation between TVDI and PA in the first 10 days and the middle 10 days,there is a significant negative correlation between TVDI and PA in late 10 days and the whole month(p<0.01),and the correlation coefficients are-0.31 and-0.34,respectively.There is a significant negative correlation between TVDI constructed by LST maximum value synthesis and PA on different time scales(p<0.01 or p<0.05),and the correlation coefficients of the first 10 days,the middle 10 days,the late10 days and the whole months are-0.29,-0.25,-0.31 and-0.41,respectively.The comprehensive analysis shows that the TVDI constructed by LST maximum value synthesis on the monthly scale is better for drought prediction.
引文
[1]李强子,闫娜娜,张飞飞,等.2010年春季西南地区干旱遥感监测及其影响评估[J].地理学报,2010,65(7):771-780.
[2]ZHANG J,MU Q,HUANG J.Assessing the remotely sensed drought severity index for agricultural drought monitoring and impact analysis in North China[J].Ecological Indicators,2016,63:296-309.
[3]KOGAN F N.Application of vegetation index and brightness temperature for drought detection[J].Advances in Space Research,1995,15(11):91-100.
[4]赵福年,张虹,陈家宙,等.作物水分胁迫指数监测红壤农田短期干旱的分析[J].土壤通报,2013,44(2):314-320.
[5]赵福年,王瑞君,张虹,等.基于冠气温差的作物水分胁迫指数经验模型研究进展[J].干旱气象,2012,30(4):522-528.
[6]QUIRING S M,GANESH S.Evaluating the utility of the Vegetation Condition Index(VCI)for monitoring meteorological drought in Texas[J].Agricultural and Forest Meteorology,2010,150(3):330-339.
[7]白力改,燕琴,张丽,等.MODIS干旱指数对华北干旱的敏感性分析[J].干旱区地理,2012,35(5):708-716.
[8]SANDHOLT I,RASMUSSEN K,ANDERSEN J.A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J].Remote Sensing of Environment,2002,79(2):213-224.
[9]ZHENG Z,ZENG Y,LI S,et al.A new burn severity index based on land surface temperature and enhanced vegetation index[J].International Journal of Applied Earth Observation and Geoinformation,2016,45:84-94.
[10]LIANG L,ZHAO S H,QIN Z H,et al.Drought change trend using MODIS TVDI and its relationship with climate factors in China from 2001to 2010[J].Journal of Integrative Agriculture,2014,13(7):1501-1508.
[11]赵杰鹏,张显峰,廖春华,等.基于TVDI的大范围干旱区土壤水分遥感反演模型研究[J].遥感技术与应用,2011,26(6):742-750.
[12]刘立文,张吴平,段永红,等.TVDI模型的农业旱情时空变化遥感应用[J].生态学报,2014,34(13):3704-3711.
[13]姜亚珍,张瑜洁,孙琛,等.基于TVDI河北省干热风同期土壤湿度监测研究[J].遥感技术与应用,2014,29(3):442-450.
[14]CARLSON T N,GILLIES R R,PERRY E M.A method to make use of thermal infrared temperature and NDVI measurement to infer surface soil water content and fractional vegetation cover[J].Remote Sensing Reviews,1994,9(1):161-173.
[15]MORAN M S,CLARKE T R,INOUE Y,et al.Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index.[J].Remote Sensing of Environment,1994,49(3):246-263.
[16]孙威,王鹏新,韩丽娟,等.条件植被温度指数干旱监测方法的完善[J].农业工程学报,2006,22(2):22-26.
[17]薄燕飞,白建军,刘永林.基于TVDI及气象干旱指数的河北省春旱时空变化特征[J].兰州大学学报(自然科学版),2016,52(2):188-195.
[18]孙丽,吴全,裴志远,等.温度植被干旱指数(TVDI)与多因子关系研究[J].地理与地理信息科学,2010,26(2):31-34.
[19]韦开,王全九,周蓓蓓,等.基于降水距平百分率的陕西省干旱时空分布特征[J].水土保持学报,2017,31(1):318-322.
[20]杨世刚,杨德保,赵桂香,等.三种干旱指数在山西省干旱分析中的比较[J].高原气象,2011,30(3):1406-1414.
[21]MISHRA A K,DESAI V R.Drought forecasting using stochastic models[J].Stochastic Environmental Research and Risk Assessment,2005,19(5):326-339.
[2]ZHANG J,MU Q,HUANG J.Assessing the remotely sensed drought severity index for agricultural drought monitoring and impact analysis in North China[J].Ecological Indicators,2016,63:296-309.
[3]KOGAN F N.Application of vegetation index and brightness temperature for drought detection[J].Advances in Space Research,1995,15(11):91-100.
[4]赵福年,张虹,陈家宙,等.作物水分胁迫指数监测红壤农田短期干旱的分析[J].土壤通报,2013,44(2):314-320.
[5]赵福年,王瑞君,张虹,等.基于冠气温差的作物水分胁迫指数经验模型研究进展[J].干旱气象,2012,30(4):522-528.
[6]QUIRING S M,GANESH S.Evaluating the utility of the Vegetation Condition Index(VCI)for monitoring meteorological drought in Texas[J].Agricultural and Forest Meteorology,2010,150(3):330-339.
[7]白力改,燕琴,张丽,等.MODIS干旱指数对华北干旱的敏感性分析[J].干旱区地理,2012,35(5):708-716.
[8]SANDHOLT I,RASMUSSEN K,ANDERSEN J.A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J].Remote Sensing of Environment,2002,79(2):213-224.
[9]ZHENG Z,ZENG Y,LI S,et al.A new burn severity index based on land surface temperature and enhanced vegetation index[J].International Journal of Applied Earth Observation and Geoinformation,2016,45:84-94.
[10]LIANG L,ZHAO S H,QIN Z H,et al.Drought change trend using MODIS TVDI and its relationship with climate factors in China from 2001to 2010[J].Journal of Integrative Agriculture,2014,13(7):1501-1508.
[11]赵杰鹏,张显峰,廖春华,等.基于TVDI的大范围干旱区土壤水分遥感反演模型研究[J].遥感技术与应用,2011,26(6):742-750.
[12]刘立文,张吴平,段永红,等.TVDI模型的农业旱情时空变化遥感应用[J].生态学报,2014,34(13):3704-3711.
[13]姜亚珍,张瑜洁,孙琛,等.基于TVDI河北省干热风同期土壤湿度监测研究[J].遥感技术与应用,2014,29(3):442-450.
[14]CARLSON T N,GILLIES R R,PERRY E M.A method to make use of thermal infrared temperature and NDVI measurement to infer surface soil water content and fractional vegetation cover[J].Remote Sensing Reviews,1994,9(1):161-173.
[15]MORAN M S,CLARKE T R,INOUE Y,et al.Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index.[J].Remote Sensing of Environment,1994,49(3):246-263.
[16]孙威,王鹏新,韩丽娟,等.条件植被温度指数干旱监测方法的完善[J].农业工程学报,2006,22(2):22-26.
[17]薄燕飞,白建军,刘永林.基于TVDI及气象干旱指数的河北省春旱时空变化特征[J].兰州大学学报(自然科学版),2016,52(2):188-195.
[18]孙丽,吴全,裴志远,等.温度植被干旱指数(TVDI)与多因子关系研究[J].地理与地理信息科学,2010,26(2):31-34.
[19]韦开,王全九,周蓓蓓,等.基于降水距平百分率的陕西省干旱时空分布特征[J].水土保持学报,2017,31(1):318-322.
[20]杨世刚,杨德保,赵桂香,等.三种干旱指数在山西省干旱分析中的比较[J].高原气象,2011,30(3):1406-1414.
[21]MISHRA A K,DESAI V R.Drought forecasting using stochastic models[J].Stochastic Environmental Research and Risk Assessment,2005,19(5):326-339.