基于MODIS数据的山东省2014—2016年干旱监测分析
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摘要
针对近年频发的干旱情况不能准确及时监测评估的问题,以山东省为研究区,基于温度植被干旱指数方法(TVDI),利用S-G加权滤波对MODIS地表温度产品MOD11A2和植被指数产品MOD13A2数据进行了重建,根据重建后的数据计算2014—2016年山东省的温度植被干旱指数,在比较NDVI-LST与EVI-LST构建的温度植被指数干旱模型(TVDI)的基础上,利用效果更好的EVI-LST构建的TVDI模型反演山东省2014—2016年的干旱情况,最后利用气象站观测数据对TVDI结果进行了相关性分析。研究表明,山东省在2014—2015年全年平均干旱面积占比分别为37.62%,41.7%,2016年基本无旱情发生。气象站观测的降水、温度与TVDI的相关性均在0.32以上,且均通过显著性检验,说明植被覆盖信息和陆地表面温度信息相结合反演的TVDI空间和时间分布能够较好地反映表层土壤水分变化趋势,其作为旱情评价指标是合理的。
Shandong Province was selected as the study area in the study due to the conditions of droughts occurred frequently in this province in recent years. MODIS land surface temperature products MOD11 A2 and vegetation index data MOD13 A2 were reconstructed by using the S-G weighted filtering. The temperature vegetation dryness index(TVDI) covering the period from 2014 to 2016 in Shandong Province was calculated by using the data of reconstruction after the Temperature Vegetation Dryness Index models were constructed by comparing NDVI-LST and EVI-LST. The research found that the TVDI model constructed by EVI-LST could better reflect drought conditions from 2014 to 2016 in Shandong Province. Finally, the meteorological data were used to perform correlation analysis of TVDI. The results showed that annual average drought area accounted for 37.62% in 2014 and 47.1% in 2015, and no drought occurred in 2016 in Shandong. The coefficients of correlations between precipitation/temperature observed by meteorological stations and TVDI were greater than 0.32, suggesting that TVDI space and time distribution inverted by combination of vegetation information with the land surface temperature can well reflect the surface soil moisture change trend and it is reasonable to take TVDI as the drought evaluation index.
Shandong Province was selected as the study area in the study due to the conditions of droughts occurred frequently in this province in recent years. MODIS land surface temperature products MOD11 A2 and vegetation index data MOD13 A2 were reconstructed by using the S-G weighted filtering. The temperature vegetation dryness index(TVDI) covering the period from 2014 to 2016 in Shandong Province was calculated by using the data of reconstruction after the Temperature Vegetation Dryness Index models were constructed by comparing NDVI-LST and EVI-LST. The research found that the TVDI model constructed by EVI-LST could better reflect drought conditions from 2014 to 2016 in Shandong Province. Finally, the meteorological data were used to perform correlation analysis of TVDI. The results showed that annual average drought area accounted for 37.62% in 2014 and 47.1% in 2015, and no drought occurred in 2016 in Shandong. The coefficients of correlations between precipitation/temperature observed by meteorological stations and TVDI were greater than 0.32, suggesting that TVDI space and time distribution inverted by combination of vegetation information with the land surface temperature can well reflect the surface soil moisture change trend and it is reasonable to take TVDI as the drought evaluation index.
引文
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[2]王凤杰,冯文兰,扎西央宗,等.基于FY-3A/VIRR和TERRA/MODIS数据藏北干旱监测对比[J].自然资源学报,2017,32(7):1229-1239.
[3]齐述华,王长耀,牛铮.利用温度植被旱情指数(TVDI)进行全国旱情监测研究[J].遥感学报,2003(5):420-427,436.
[4]王婷婷,张洪岩,郭笑怡,等.基于温度植被干旱指数的松辽平原干旱时空特征[J].干旱区研究,2014,31(3):383-389.
[5]Savitzky A, Golay M J E. Smoothing and differentiation of data by simplified least squares procedures[J]. Analytical Chemistry, 1964,36(8):1627-1639.
[6]张悦,沈润平,彭露露,等.基于重建MODIS无云数据反演京津冀地区土壤湿度[J].江苏农业科学,2016,44(12):375-378.
[7]王乾坤,于信芳,舒清态,等. MODISEVI时序数据重建方法及拟合分析[J].地球信息科学学报,2015,17(6):732-741.
[8]苏红,刘峻明,王春艳,等.基于时间序列MODIS LST产品的重构研究[J].中国农业科技导报,2014,16(5):99-107.
[9]Lambin E F, Ehrlich D. The surface temperature-vegetation index space for land cover and land-cover change analysis[J]. International Journal of Remote Sensing, 1996,17(3):463-487.
[10]严婧,沈润平,鲍艳松,等.江苏地区MODIS LST产品重建研究[J].环境科学与技术,2014,37(1):160-167.
[11]朱寿东,刘慧平,冯徽徽,等.基于时间序列影像的中观尺度农作物长势监测采样方法[J].农业工程学报,2012,28(2):127-133.
[12]王思楠,李瑞平,韩刚,等.基于多源遥感数据的TVDI方法在荒漠草原旱情监测的应用[J].安徽农业大学报,2017,44(3):458-464.