关中平原干旱遥感监测指数对比和应用研究
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摘要
采用2000-2016年5月份MODIS数据,构建NIR-Red特征空间,对比分析基于该特征空间的垂直干旱指数(Perpendicular Drought Index,PDI)、改进型垂直干旱指数(Modified Perpendicular Drought Index,MPDI)、土壤湿度监测指数(Soil Moisture Monitoring Index,SMMI)及改进型土壤湿度监测指数(Modified Soil Moisture Monitoring Index,MSMMI)这四种干旱监测指数的有效性,并与实测土壤湿度进行相关性分析;最后采用精度最高的SMMI分析关中平原的旱情时空分布特征和规律。结果表明:(1) PDI、MPDI、SMMI及MSMMI均与10cm深土壤湿度存在负相关关系,可决系数R2分别为0.60、0.40、0.64、0.40,表明PDI、MPDI、SMMI及MSMMI均可作为旱情监测指标,且SMMI略优于其它三种监测指数;(2)关中平原东部、中部、西部部分地区旱情严重,西南部地区旱情较轻,且旱情呈年际波动显著的特征;(3) SMMI与月平均气温呈正相关关系区域占75.66%,与月降水量呈负相关关系区域占74.34%,其中通过90%显著性检验区域分别占总面积的27.36%、17.26%,说明降雨和温度不是导致旱情变化的主要影响因子。
By using MODIS data from May 2000 to May 2016,constructing the NIR-Red space,and collecting the Perpendicular Drought Index(PDI),Modified Perpendicular Drought Index(MPDI),Soil Moisture Monitoring Index(SMMI) and Modified Soil Moisture Monitoring Index(MSMMI),we studied the validity of these four indexes and the correlation of them with the measured soil moisture data. Then,the spatial and temporal distribution characteristics of drought in Guanzhong plain was assessed based on SMMI that had the highest precision.The results showed that:(1) There was a negative correlation between PDI,MPDI,SMMI and MSMMI with measured soil moisture in the top 10 cm layer with R2,coefficient of correcation of 0.60,0.40,0.64,and 0.40,respectively. It was suggested that all four indexes could be used as drought indicators while SMMI was slightly better than the others.(2) The drought conditions in the eastern,central,and western regions of Guanzhong plain were more severe while soil water content was relatively higher in the southwestern area. There was a significant inter-annual fluctuation in drought of Guanzhong plain.(3) There was a positive correlation between SMMI and average monthly temperature in 75.66% of the total area but a negative correction of SMMI with monthly precipitation in 74.34% of the area. The significance test showed that 27.36% and 17.26% of that are at P<0.1 level,respectively,indicating that precipitation and temperature were not the major factors causing the changes in drought.
By using MODIS data from May 2000 to May 2016,constructing the NIR-Red space,and collecting the Perpendicular Drought Index(PDI),Modified Perpendicular Drought Index(MPDI),Soil Moisture Monitoring Index(SMMI) and Modified Soil Moisture Monitoring Index(MSMMI),we studied the validity of these four indexes and the correlation of them with the measured soil moisture data. Then,the spatial and temporal distribution characteristics of drought in Guanzhong plain was assessed based on SMMI that had the highest precision.The results showed that:(1) There was a negative correlation between PDI,MPDI,SMMI and MSMMI with measured soil moisture in the top 10 cm layer with R2,coefficient of correcation of 0.60,0.40,0.64,and 0.40,respectively. It was suggested that all four indexes could be used as drought indicators while SMMI was slightly better than the others.(2) The drought conditions in the eastern,central,and western regions of Guanzhong plain were more severe while soil water content was relatively higher in the southwestern area. There was a significant inter-annual fluctuation in drought of Guanzhong plain.(3) There was a positive correlation between SMMI and average monthly temperature in 75.66% of the total area but a negative correction of SMMI with monthly precipitation in 74.34% of the area. The significance test showed that 27.36% and 17.26% of that are at P<0.1 level,respectively,indicating that precipitation and temperature were not the major factors causing the changes in drought.
引文
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[20]宋扬,房世波,卫亚星.农业干旱遥感监测指数及其适用性研究进展[J].科技导报,2016,34(5):45-52.
[21] Sun L,Wu Q,Pei Z,et al. Study on drought index in major planting area of winter wheat of China[J]. Sensor Letters,2012,10(1):453-458.
[22]李菁,王连喜,沈澄,等.几种干旱遥感监测模型在陕北地区的对比和应用[J].中国农业气象,2014,35(1):97-102.
[23]杜灵通,田庆久,王磊,等.基于多源遥感数据的综合干旱监测模型构建[J].农业工程学报,2014,30(9):126-132.
[2]孙灏,陈云浩,孙洪泉.典型农业干旱遥感监测指数的比较及分类体系[J].农业工程学报,2012,28(14):147-154.
[3]蒋友严,韩涛,徐燕,等.基于多源卫星数据的3种干旱遥感监测效果比较[J].干旱地区农业研究,2014,32(2):47-51.
[4] Richardson A J,Wiegand C L. Distinguishing vegetation from soil background information[J]. Photogrammetric Engineering and Remote Sensing,1977,43(12):1541-1552.
[5] Ghulam A,Qin Q M,Zhan Z M. Designing of the perpendicular drought index[J]. Environmental Geology, 2007, 52(6):1045-1052.
[6] Ghulam A,Qin Q M,Teyip T,et al. Modified perpendicular drought index(MPDI):a real-time drought monitoring method[J]. ISPRS Journal of Photogrammetry&Remote Sensing,2007,62(2):150-164.
[7] Qin Q M,Ghulam A,Zhu L,et al. Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China[J]. International Journal of Remote Sensing,2008,29(7):1983-1995.
[8] Shahabfar A,Ghulam A,Eitzinger J. Drought monitoring in Iran using the perpendicular drought indices[J]. International Journal of Applied Earth Observation&Geoinformation,2012,18(1):119-127.
[9] Shahabfar A,Ghulam A,Conrad C. Understanding hydrological repartitioning and shifts in drought regimes in Central and SouthWest Asia using MODIS derived perpendicular drought index and TRMM data[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2014,7(3):983-993.
[10] Chen N C, Li J Z, Zhang X. Quantitative evaluation of observation capability of GF-1 wide field of view sensors for soil moisture inversion[J]. Journal of Applied Remote Sensing,2015,9(1):199-203.
[11] Zhang L F,Jiao W Z,Zhang H M,et al. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices[J]. Remote Sensing of Environment,2017,190:96-106.
[12] Zormand S,Jafari R,Koupaei S S. Assessment of PDI,MPDI and TVDI drought indices derived from MODIS Aqua/Terra Level1B data in natural lands[J]. Natural Hazards,2017,86:1-21.
[13]姚云军,秦其明,赵少华.基于MODIS短波红外光谱特征的土壤含水量反演[J].红外与毫米波学报,2011,30(1):9-14.
[14]刘英,吴立新,马保东.基于TM/ETM+光谱特征空间的土壤湿度遥感监测[J].中国矿业大学学报,2013,42(2):296-301.
[15]刘英,吴立新,岳辉,等.基于尺度化SMMI的神东矿区土壤湿度变化遥感分析[J].科技导报,2016,34(3):78-84.
[16]王鹏新,孙威.基于植被指数和地表温度的干旱监测方法的对比分析[J].北京师范大学学报(自然科学版),2007,43(3):319-323.
[17]张学艺,李剑萍,秦其明,等.几种干旱监测模型在宁夏的对比应用[J].农业工程学报,2009,25(8):18-23.
[18]杨绍锷,闫娜娜,吴炳方.农业干旱遥感监测研究进展[J].遥感信息,2010,(1):103-109.
[19]孙丽,裴志远,马尚杰,等.基于多种卫星的县级尺度干旱监测指数比较——以河北玉田县为例[J].地理与地理信息科学,2014,30(4):46-50.
[20]宋扬,房世波,卫亚星.农业干旱遥感监测指数及其适用性研究进展[J].科技导报,2016,34(5):45-52.
[21] Sun L,Wu Q,Pei Z,et al. Study on drought index in major planting area of winter wheat of China[J]. Sensor Letters,2012,10(1):453-458.
[22]李菁,王连喜,沈澄,等.几种干旱遥感监测模型在陕北地区的对比和应用[J].中国农业气象,2014,35(1):97-102.
[23]杜灵通,田庆久,王磊,等.基于多源遥感数据的综合干旱监测模型构建[J].农业工程学报,2014,30(9):126-132.