【作者】 张学艺； 李剑萍； 秦其明； 韩颖娟； 张晓煜； 王连喜；

【Author】 Zhang Xueyi1,2, Li Jianping2 , Qin Qiming3 , Han Yingjuan2 , Zhang Xiaoyu2 , Wang Lianxi1(1.Nanjing Information Engineering University,Nanjing 210044;2 Ningxia key laboratory for meteorological disaster prevention and reduction,Yinchuan 750002;3.Institute of Remote Sensing and GIS,Peking University,Beijing 100871 )

【机构】 南京信息工程大学； 宁夏气象防灾减灾重点试验室； 北京大学遥感与GIS研究所；

【摘要】 采用一种基于MODIS遥感资料和地面自动气象站(AWS)观测数据反演地表温度(LST)的统计方法,结合MODIS-EVI,试验研究改进型温植被指数(MTVDI),运用通道2和LST试验研究改进型能量指数(MEI),运用通道1和2试验研究改进型垂直干旱指数(MPDI),结合农业气象常规业务地面取土测墒资料,建立各指数与土壤含水量的统计函数关系,均通过0.01显著性检验。将所建模型运用于宁夏干旱监测业务,结果表明:MTVDI在作物生长季监测效果最为显著,MEI和MPDI对裸露或稀疏植被地表旱情监测比较有效,但MEI监测精度略高,各类监测指数综合监测精度基本都能控制在90%左右。总的来说,几个模型各有优劣,综合运用才能在实际监测业务中发挥最佳效果。更多还原

【Abstract】 Used a statistical method to get the LST model with the MODIS data and the AWS data, combined with MODIS-EVI, Experimental study on improved temperature vegetation index (MTVDI), and used the ch1 data and LST to get the MEI, and used the ch1&ch2 data to get PDI &MPDI, combined with the soil moisture data of the agro-meteorological AB to build the every index statistical function, all get through the 0.01 test of significance. The models will be applied to Ningxia drought monitoring business, results showed that: MTVDI is the most significant in crop growing season, MEI and MPDI is more effective on bare ground or sparse vegetation, but MEI is slightly higher, the monitoring accuracy can be controlled about 90% .In general, some models have their own advantages and disadvantages, to make comprehensive use of the actual monitoring of the business to play the best results.更多还原