川渝地区农业气象干旱风险区划与损失评估研究
|
摘要
2006年入夏以来,川渝地区遭受了50年来最严重的旱灾,2009～2010年又经历了西南五省持续性特大干旱,作物产量损失严重。水稻是川渝地区的主要粮食作物,因此准确、定量地评估农业气象灾害风险、旱情监测及灾后损失评估对农业可持续发展及防灾减灾对策和措施的制定意义重大。目前针对川渝地区的农业(水稻)气象灾害风险评估研究仍未开展,本文利用该地区43个气象台站50年的气象资料、水稻单产、种植面积资料及灾情资料等多元数据,首先对川渝地区开展农业(水稻)气象干旱风险评估,然后进行遥感监测,并在此基础上对2006年水稻产量进行定量灾损评估。
本文选取了致灾因子危险性(H)、承灾体脆弱性(V)及抗灾减灾能力(RE)等3个一级评价指标和11个二级评价指标,构建川渝地区农业(水稻)气象干旱风险综合评价指标(R),对其进行评估。在遥感监测方面,利用TRMM3B43数据构建的月降水量距平和累积降水距平,监测并分析了2000～2012年气象干旱空间分布,并选取了19个典型干旱时期,作为土壤湿度和植被干旱监测的研究基础。构建了温度植被干旱指数TVDI(TVDIN、TVDIE、TVDIM),分析其特征空间、并与降水趋势和98个农业气象观测点的10cm、20cm的土壤墒情资料进行相关性分析,进而选取TVDIM反演的土壤湿度与19个典型干旱时期进行空间对比分析。通过考虑“大气降水—土壤湿度—植被响应”之间的关系,利用距平植被指数(ANDVI)对大气降水和土壤湿度的“时滞”效应进行分析。在水稻灾损评估方面,利用拉格朗日插值法、直线滑动平均法和借助于遥感手段的平均减产分成法(水稻种植面积提取,估产,受灾面积信息)估算了2006年川渝地区水稻产量的损失量。
建立和完善农业气象干旱风险评估,遥感监测和灾损评估是农业气象灾害的研究重点。本文紧密围绕以上三个主题对川渝地区进行上述研究,得到的主要结论包括：
(1)综合风险指数(R)高的地区集中在成都市、德阳市、重庆市、遂宁市地区。R值高的地区往往并不是由单一因素所决定,而是多方面因素综合作用的结果,其中承灾体的高脆弱性是导致高风险的主要因素。R值低的地区主要集中在川西和川北地区,如阿坝藏族羌族自治州、甘孜藏族自治州等,这些地区均表现出较低的致灾因子危险性。利用水稻产量损失模型对构建的农业气象干旱风险模型进行验证,两者显著相关(R2=0.45,P<0.05)。
(2) TRMM降水数据与实测降水数据显著相关(P<0.001)。基于TRMM的降水距平数据显示,2006年旱情集中出现在6-8月的宜宾市、沙坪坝和遂宁地区,高温和降水偏少是导致重旱的主要原因,干旱范围覆盖了四川省除北部以外的大部分地区,这与通过(1)中所得到的部分高(低)风险区基本一致。基于TRMM的降水监测能够很好的反映出如2006年川渝大旱和2009-2010年持续性干旱的空间和时间演变过程,并对农业干旱等提供了重要的预警作用。基于TVDIE的土壤湿度空间分布特征与基于TRMM的降水量距平空间分布特征具有一定的相似性,大部分时期的空间匹配度较高。在距平植被指数(ANDVI)得到的旱情监测空间分布图的基础上,发现ANDVI与TRMM降水量的相关系数在第40天和第48天分别达到0.32和0.33(P<0.05),与TVDIE土壤湿度的相关系数在第16天为0.35(P<0.05),说明三者之间具有一定的滞后性。
(3)拉格朗日法得到的期望单产曲线位于实际单产曲线上方,2006年利用该方法得到四川省水稻产量损失达273万吨,重庆市水稻产量损失139万吨,合计401万吨。1949～2011年四川省平均水稻损失量为118万吨,年平均灾损率为8.35%,1997-2011年重庆市平均水稻损失量为42.87万吨,年平均灾损率为7.6%,2006年水稻灾损率偏高。直线滑动平均法得到的趋势单产曲线围绕实际单产曲线上下波动,统计得到2006年四川省水稻损失量为156万吨。分析发现,利用拉格朗日法计算灾损量和灾损率的时,由于所选取的完全无灾害的理想状态极少,以此为基础所得到的期望单产往往比实际估产的结果偏大,在利用直线滑动平均进行水稻估产中,由于没有充分利用理想无灾害年份,得到的趋势产量与气象产量无法完全剥离,导致结果偏小。利用遥感手段提取的川渝地区水稻种植区为3.5×106ha,与统计数据相对误差为15%左右。在植被指数距平基础上,提取了水稻绝收面积、成灾面积和受灾面积分别为8.10×103ha,45.2×103ha和2.67×106ha。在此基础上,基于遥感手段的平均减产法得到川渝地区水稻损失为302.31万吨。
In the summer of2006, Sichuan-Chongqing region suffered the worst drought in recent50years, combined with the persistent drought occurring in Southwest of China from2009to2010, both of which caused great losses in crop yield. Paddy rice is the staple in this region. Quantitative assessing and monitoring agriculture meteorological drought has great significance on agriculture structural adjustment and policy-making with respect to disaster prevention. Recently, few studies have focused on meteorological drought risk assessment in Sichuan-Chongqing region. This paper uses the50-year meteorological data obtained from43meteorological stations, and the information associated with rice yield, rice growth area and disaster statistics to explore agriculture meteorological disaster risk assessment in the Sichuan-Chongqing region with quantitative analysis of rice yield losses in2006.
This paper selected3first-grade indices, including the hazard risk (H), the order vulnerability (V) and the disaster resistance (RE) and12second-grade indices to establish the comprehensive assessment index (R). In the aspect of remote sensing monitoring, TRMM3B43(Tropical Rainfall Measure Mission) was used to calculate monthly precipitation anomaly and cumulative monthly precipitation anomaly in order to indicate the spatial distribution of meteorological drought from2000to2012in studied region. The19typical drought periods were selected according to TRMM data. Three temperature-vegetation drought indices (TVDIN, TVDIE, and TVDIM) were constructed to analyze their feature spaces, and their relationship with precipitation trends and soil moisture data in10cm and20cm depth from98meteorological sites. Spatial comparison between TVDIM-based soil moisture and TRMM-based precipitation data from these selected19typical drought periods was made. Given the relationship as "precipitation-soil moisture-vegetation growth" the lag-response of anomaly vegetation index (ANDVI) to precipitation and soil moisture was analyzed. Three methods as Lagrange interpolation method, Linear moving average method and Average yield-reduction method (with rice area extraction, yield assessment, and drought-suffering area estimation) were used to estimate rice yield losses in2006.
This paper focuses on agriculture meteorological drought risk assessment, remote sensing monitoring and rice yield losses assessment, all of which are crucial in agriculture drought study area. The key findings are as follows:
(1) The highest R values were found in Chengdu, Deyang, Chongqing and Suining city, et al. Various factors could lead to the high R values, and the high order vulnerability was the main contributor. The areas in the western and northern region of Sichuan, such as Aba and Ganzi Tibetan Autonomous Prefecture have the lowest R values. Rice yield loss model was used to validate the agriculture meteorological drought risk model used in this study. These two models had a significant correlation (R2=0.45, P<0.05).
(2) The data derived from TRMM and observed precipitation amount were significant correlated (P<0.001). TRMM-based precipitation anomalies showed that the severe drought occurring in June to August of2006were mainly found in areas such as Yibin, Shapingba, and Suining city. The high temperatures and shortened precipitation were key reasons. The drought covered all the areas except for the northern part of Sichuan. This result is well corresponded to those obtained in Chapter one. TRMM-based precipitation anomalies can well reflect spatial distribution and temporal evolution of drought in Sichuan-Chongqing region, especially for2006and2009-2010. The spatial distribution of TVDIE-based soil moisture in most observed month was matched or similar to those of TRMM-based precipitation anomaly. Then the spatial distribution of anomaly vegetation index (ANDVI) in studied region was established. The correlation coefficients of ANDVI and TRMM-based precipitation anomaly reached to0.32and0.33in40th and48th day, respectively. Meanwhile, the correlation coefficients of ANDVI and TVDIE-based soil moisture reached to0.35in16th day, which clearly shows a lag-response among them.
(3) The expectation yield curve obtained by the Lagrange interpolation method was over the actual yield curve. The rice yield losses in Sichuan and Chongqing region by this method are2.73and1.39million tons, respectively (with a total of4.01million tons). The average (1949-2011) rice yield losses amount and percent in Sichuan were1.18million tons and8.35%, respectively, while these data were0.423million tons and7.6%in Chongqing from1997to2011. The rice yield loss amount (million tons) and percent(%) in2006was much higher. Trend yield curve obtained by Linear moving average method fluctuated among the actual yield curve. The estimated rice yield losses in Sichuan by this method were1.56million tons. It shows that the expectation yield obtained by Lagrange interpolation method may lead to over-estimation due to fact that the selected expectation yield is too ideal, while Linear moving average method may under-estimate the results since the meteorological yield cannot be well separated from the actual yield. The extracted rice areas in Sichuan-Chongqing region by remote sensing technology were3.5×106ha with the relative errors about15%when compared with data from National Bureau of Statistics. The no harvest areas, drought-occurred areas and drought-induced areas were estimated to be8.1×103ha,45.2×103ha and2.67×106ha, respectively in studied region based on vegetation anomalies and results obtained above. The rice yield losses according to the Average yield-reduction method are3.02million tons.
本文选取了致灾因子危险性(H)、承灾体脆弱性(V)及抗灾减灾能力(RE)等3个一级评价指标和11个二级评价指标,构建川渝地区农业(水稻)气象干旱风险综合评价指标(R),对其进行评估。在遥感监测方面,利用TRMM3B43数据构建的月降水量距平和累积降水距平,监测并分析了2000～2012年气象干旱空间分布,并选取了19个典型干旱时期,作为土壤湿度和植被干旱监测的研究基础。构建了温度植被干旱指数TVDI(TVDIN、TVDIE、TVDIM),分析其特征空间、并与降水趋势和98个农业气象观测点的10cm、20cm的土壤墒情资料进行相关性分析,进而选取TVDIM反演的土壤湿度与19个典型干旱时期进行空间对比分析。通过考虑“大气降水—土壤湿度—植被响应”之间的关系,利用距平植被指数(ANDVI)对大气降水和土壤湿度的“时滞”效应进行分析。在水稻灾损评估方面,利用拉格朗日插值法、直线滑动平均法和借助于遥感手段的平均减产分成法(水稻种植面积提取,估产,受灾面积信息)估算了2006年川渝地区水稻产量的损失量。
建立和完善农业气象干旱风险评估,遥感监测和灾损评估是农业气象灾害的研究重点。本文紧密围绕以上三个主题对川渝地区进行上述研究,得到的主要结论包括：
(1)综合风险指数(R)高的地区集中在成都市、德阳市、重庆市、遂宁市地区。R值高的地区往往并不是由单一因素所决定,而是多方面因素综合作用的结果,其中承灾体的高脆弱性是导致高风险的主要因素。R值低的地区主要集中在川西和川北地区,如阿坝藏族羌族自治州、甘孜藏族自治州等,这些地区均表现出较低的致灾因子危险性。利用水稻产量损失模型对构建的农业气象干旱风险模型进行验证,两者显著相关(R2=0.45,P<0.05)。
(2) TRMM降水数据与实测降水数据显著相关(P<0.001)。基于TRMM的降水距平数据显示,2006年旱情集中出现在6-8月的宜宾市、沙坪坝和遂宁地区,高温和降水偏少是导致重旱的主要原因,干旱范围覆盖了四川省除北部以外的大部分地区,这与通过(1)中所得到的部分高(低)风险区基本一致。基于TRMM的降水监测能够很好的反映出如2006年川渝大旱和2009-2010年持续性干旱的空间和时间演变过程,并对农业干旱等提供了重要的预警作用。基于TVDIE的土壤湿度空间分布特征与基于TRMM的降水量距平空间分布特征具有一定的相似性,大部分时期的空间匹配度较高。在距平植被指数(ANDVI)得到的旱情监测空间分布图的基础上,发现ANDVI与TRMM降水量的相关系数在第40天和第48天分别达到0.32和0.33(P<0.05),与TVDIE土壤湿度的相关系数在第16天为0.35(P<0.05),说明三者之间具有一定的滞后性。
(3)拉格朗日法得到的期望单产曲线位于实际单产曲线上方,2006年利用该方法得到四川省水稻产量损失达273万吨,重庆市水稻产量损失139万吨,合计401万吨。1949～2011年四川省平均水稻损失量为118万吨,年平均灾损率为8.35%,1997-2011年重庆市平均水稻损失量为42.87万吨,年平均灾损率为7.6%,2006年水稻灾损率偏高。直线滑动平均法得到的趋势单产曲线围绕实际单产曲线上下波动,统计得到2006年四川省水稻损失量为156万吨。分析发现,利用拉格朗日法计算灾损量和灾损率的时,由于所选取的完全无灾害的理想状态极少,以此为基础所得到的期望单产往往比实际估产的结果偏大,在利用直线滑动平均进行水稻估产中,由于没有充分利用理想无灾害年份,得到的趋势产量与气象产量无法完全剥离,导致结果偏小。利用遥感手段提取的川渝地区水稻种植区为3.5×106ha,与统计数据相对误差为15%左右。在植被指数距平基础上,提取了水稻绝收面积、成灾面积和受灾面积分别为8.10×103ha,45.2×103ha和2.67×106ha。在此基础上,基于遥感手段的平均减产法得到川渝地区水稻损失为302.31万吨。
In the summer of2006, Sichuan-Chongqing region suffered the worst drought in recent50years, combined with the persistent drought occurring in Southwest of China from2009to2010, both of which caused great losses in crop yield. Paddy rice is the staple in this region. Quantitative assessing and monitoring agriculture meteorological drought has great significance on agriculture structural adjustment and policy-making with respect to disaster prevention. Recently, few studies have focused on meteorological drought risk assessment in Sichuan-Chongqing region. This paper uses the50-year meteorological data obtained from43meteorological stations, and the information associated with rice yield, rice growth area and disaster statistics to explore agriculture meteorological disaster risk assessment in the Sichuan-Chongqing region with quantitative analysis of rice yield losses in2006.
This paper selected3first-grade indices, including the hazard risk (H), the order vulnerability (V) and the disaster resistance (RE) and12second-grade indices to establish the comprehensive assessment index (R). In the aspect of remote sensing monitoring, TRMM3B43(Tropical Rainfall Measure Mission) was used to calculate monthly precipitation anomaly and cumulative monthly precipitation anomaly in order to indicate the spatial distribution of meteorological drought from2000to2012in studied region. The19typical drought periods were selected according to TRMM data. Three temperature-vegetation drought indices (TVDIN, TVDIE, and TVDIM) were constructed to analyze their feature spaces, and their relationship with precipitation trends and soil moisture data in10cm and20cm depth from98meteorological sites. Spatial comparison between TVDIM-based soil moisture and TRMM-based precipitation data from these selected19typical drought periods was made. Given the relationship as "precipitation-soil moisture-vegetation growth" the lag-response of anomaly vegetation index (ANDVI) to precipitation and soil moisture was analyzed. Three methods as Lagrange interpolation method, Linear moving average method and Average yield-reduction method (with rice area extraction, yield assessment, and drought-suffering area estimation) were used to estimate rice yield losses in2006.
This paper focuses on agriculture meteorological drought risk assessment, remote sensing monitoring and rice yield losses assessment, all of which are crucial in agriculture drought study area. The key findings are as follows:
(1) The highest R values were found in Chengdu, Deyang, Chongqing and Suining city, et al. Various factors could lead to the high R values, and the high order vulnerability was the main contributor. The areas in the western and northern region of Sichuan, such as Aba and Ganzi Tibetan Autonomous Prefecture have the lowest R values. Rice yield loss model was used to validate the agriculture meteorological drought risk model used in this study. These two models had a significant correlation (R2=0.45, P<0.05).
(2) The data derived from TRMM and observed precipitation amount were significant correlated (P<0.001). TRMM-based precipitation anomalies showed that the severe drought occurring in June to August of2006were mainly found in areas such as Yibin, Shapingba, and Suining city. The high temperatures and shortened precipitation were key reasons. The drought covered all the areas except for the northern part of Sichuan. This result is well corresponded to those obtained in Chapter one. TRMM-based precipitation anomalies can well reflect spatial distribution and temporal evolution of drought in Sichuan-Chongqing region, especially for2006and2009-2010. The spatial distribution of TVDIE-based soil moisture in most observed month was matched or similar to those of TRMM-based precipitation anomaly. Then the spatial distribution of anomaly vegetation index (ANDVI) in studied region was established. The correlation coefficients of ANDVI and TRMM-based precipitation anomaly reached to0.32and0.33in40th and48th day, respectively. Meanwhile, the correlation coefficients of ANDVI and TVDIE-based soil moisture reached to0.35in16th day, which clearly shows a lag-response among them.
(3) The expectation yield curve obtained by the Lagrange interpolation method was over the actual yield curve. The rice yield losses in Sichuan and Chongqing region by this method are2.73and1.39million tons, respectively (with a total of4.01million tons). The average (1949-2011) rice yield losses amount and percent in Sichuan were1.18million tons and8.35%, respectively, while these data were0.423million tons and7.6%in Chongqing from1997to2011. The rice yield loss amount (million tons) and percent(%) in2006was much higher. Trend yield curve obtained by Linear moving average method fluctuated among the actual yield curve. The estimated rice yield losses in Sichuan by this method were1.56million tons. It shows that the expectation yield obtained by Lagrange interpolation method may lead to over-estimation due to fact that the selected expectation yield is too ideal, while Linear moving average method may under-estimate the results since the meteorological yield cannot be well separated from the actual yield. The extracted rice areas in Sichuan-Chongqing region by remote sensing technology were3.5×106ha with the relative errors about15%when compared with data from National Bureau of Statistics. The no harvest areas, drought-occurred areas and drought-induced areas were estimated to be8.1×103ha,45.2×103ha and2.67×106ha, respectively in studied region based on vegetation anomalies and results obtained above. The rice yield losses according to the Average yield-reduction method are3.02million tons.
引文
[1]Alley, W.M. The palmer drought severity index as a measure of hydrological drought [J]. Water Resources Bulletin,1985,21(1):105-114.
[2]Bindlish, R., Jackson, T.J.,Gasiewski, A.J., Klein, M., Njoku, E.G. Soil moisture mapping and AMSR-E validation using the PSR in SMEX02 [J]. Remote Sensing of Environment,2006, 103(2):127-139.
[3]Brown, J.F, Wardlow, B.D, Tsegaye, T. The vegetation drought response index a new integrated approach for monitoring drought stress in vegetation [J]. GIScience and Remote Sensing,2008,45(1):16-46.
[4]Brown, John. E.M.An analysis of the performance of hybrid infrared and microwave satellite precipitation algorithms over India and adjacent regions. Remote Sensing of Environment [J]. 2006,101(1):63-81.
[5]Carlson,T.N. An overview of the "triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery [J]. Sensors,2007,7(8):1612-1629.
[6]Chauhan, N.S., Miller, S., Ardanuy, P. Space borne soil moisture estimation at high remote sensing of resolution:a microwave-optical/IR synergistic approach [J]. International Journal of Remote Sensing,2003.24(22):4599-4622.
[7]Chen, C.F., Nguyen, T.S., Li, Y.C. Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data [J]. Applied Geography, 2011,31(2):463-475.
[8]Chiu, L.J., Vongsaard. S. Morain,A. Budge, C. Comparison of TRMM and water division rain rates over New Mexico [J]. Advanced Atmosphere Sciences.2006,23(1):1-13.
[9]Daniel, A. Vila. Luis Gustavo G. de Goncalves,David L. Toll Jose Roberto Rozante. Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America [J]. Journal of Hydrometeorology,2009.10(2):533-543.
[10]David, L. B. J. Constrained two layer models for estimating evaportranspiration [G].11th Asia conference on Remote sensing, Zhongshan University, Guangzhou, China,16-19 Nov 1990.
[11]Earson, R.L, Miller, L.D. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grasslands, Colorado[J]. Proceedings of the 8th International Symposium on Remote Sensing of Environment,1972,1357-1381.
[12]Fensholt, R., Sandholt, I. Derivation of a shortwave infrared water stress index from MODIS near-and shortwave infrared data in a semiarid environment [J]. Remote Sensing of Environment,2003,87(1):111-121.
[13]Frate, D., Schiavon, G. Retrieving soil moisture and agricultural variables by microwave radiometry using neural networks [J]. Remote Sensing of Environment,2003,84(2):174-183.
[14]Fu, B.J., Chen, L.D. Agricultural landscape spatial pattern analysis in the semi-arid hill area of the Loess Plateau, China [J]. Journal of Arid Environments,2002,44(3):291-303.
[15]Gao, B.C. NDWI- a normalized difference water index for remote sensing of vegetation liquid water from space [J]. Remote Sensing of Environment,1996,58(3):257-266.
[16]Garen, D.C. Revised surface-water supply index for the western United States [J]. Journal of Water Resources Planning and Management,1992,119(4):437-454.
[17]Gillies, R.R., Carlson, T.N, Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models [J]. Journal of Applied Meteorology, 1995,34(4):745-756.
[18]Goetz, S.J. Multisensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site [J]. International Journal of Remote Sensing,1997,18(1):71-94.
[19]Goetz, S.J. Multisensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site [J]. International Journal of Remote Sensing,1997,18(1):71-94.
[20]Goward, S.N., Hope, A.S. Evaporation from combined reflected solar and emitted terrestrial radiation Preliminary FIFE results from AVHRR data [J]. Advances in Space Research,1989,9(7): 239-249.
[21]Groten, S.M.E. NDVI-crop monitoring and early yield assessment of Burkina Faso [J]. International Journal of Remote Sensing,1993,14(8):1495-1515.
[22]Gutman, G.G. Towards monitoring drought from space [J]. Journal of Climate,1990,3(2): 282-295.
[23]Guttman. N.B. Comparing the palmer drought index and the standardized precipitation index [J]. Journal of the American water resources association,1998.34(1):113-121.
[24]Han, Y.. Wang, Y. and Zhao, Y. Estimating soil moisture conditions of the Greater Changbai Mountains by land surface temperature and NDVI [J]. IEEE Transactions on Geoscience and Remote Sensing.2010,48:2509-2515.
[25]Heim, R.R. Drought indices:a review. Drought:A Global Assessment [M]. New York: Routledge,2000.
[26]Henry, A.J. The Cmatology of the United States [M]. Washington DC:U.S. Weather Bureau Bulletin Q,1906:51-58.
[27]Hong, S.Y.. Lee, K.S., Rim. S.K., et al. Estimation of rice field area using two-date Landsat TM images [G]. IEEE International Geoscience and Remote Sensing Symposium.1999,6(2): 732-734.
[28]Hu. M.Q, Mao. F., Sun, H., Hou. Y.Y. Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region [J]. International Journal of Applied Earth Observation and Geoinformation,2011,13:24-33.
[29]Hua-sheng Sun, Jing-feng, Huang, Alfredo, R. Mapping paddy rice in China with multi-date MODIS data [J]. Journal of Zhejiang University (Science A),2008.
[30]Huete, A., Didan, K., Miura, T., et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices [J]. Remote Sensing of Environment,2002,83(1-2): 195-213.
[31]Huete, A., Didan, k., Miura, T., et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices [J]. Remote Sensing of Environment,2002,83(1-2): 195-213.
[32]Huete, A.R. A soil-adjusted vegetation index (SAVI) [J]. Remote Sensing of Environment, 1988,25:295-309.
[33]Idso, S.B., Jackson, R.D., Pinter, P.J. Measuring yield-reducing plant water potential depressions in wheat by infrared thermometry [J]. Irrigation Science,1981,2(4):205-212.
[34]Idso, S.B., Jackson, R.D., Pinter, P.J. Measuring yield-reducing plant water potential depressions in wheat by infrared thermometry [J]. Irrigation Science,1981,2(4):205-212.
[35]IUGS. Quantitative risk assessment for slopes and landslides-the state of the art. In Cruden D and Fell R, eds. Landslide Risk Assessment [C]. Rotterdam:AA, Balkema,1997.
[36]Jackson, R.D., Idso, S.B. Canopy temperature as a crop water stress indicator [J].Water resources Research,1981,17(4):1133-1138.
[37]Jackson, R.D., Kustas, W.P., Choudhury, B.J. A reexamination of crop water stress index [J]. Irrigation Science,1988,9:309-317.
[38]Jackson, T. J., Le Vine, D.M., Hsu, A.Y., Oldak, A., Starks, P.J., Swift, C.T., Isham, J.D., Haken, M. Soil moisture mapping at regional scales using microwave radiometry:The southern Great Plains hydrology experiment [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999,37 (5):2136-2151.
[39]Ji, L., Peters, A.J. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices [J]. Remote Sensing of Environment,2003,87(1):85-98.
[40]Karl, T.R. The sensitivity of the palmer drought severity index and Palmer's Z-Index to their calibration coefficients including potential evaportranspiration [J]. Journal of Climate and Applied Meteorology,1986(25):77-86.
[41]Kogan, EN. Application of vegetation index and brightness temperature for drought Detection [J]. Advances in Space Research,1995,15(11):91-100.
[42]Kogan, F.N. Remote sensing of weather impacts on vegetation in no-homogeneous areas [J]. International Journal of Remote Sensing,1990,11:1405-1419.
[43]Kuusk, A. The hot spot effect of a uniform vegetative cover [J]. Soviet Russia Journal of Remote Sensing,1985:3645-3658.
[44]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.
[45]Lewis, J.E., Rowland, J., Nadeau, A. Estimating maize production in Kenya using NDVI some statistical considerations [J]. International Journal of Remote Sensing,1998,19(13): 2609-2617.
[46]Maskrey, A. Disaster Mitigation:A Community Based Approach [M]. Oxford:Oxfam,1989.
[47]McGuire, J.K., Palmer, W.C. The 1957 drought in the eastern United States [J]. Monthly Weather Review,1957,85(9):305-314.
[48]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.
[49]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.
[50]Munger, T.T. Graphic method of representing and comparing. Drought intensities [J]. Monthly Weather Review,1916, (44):642-643.
[51]Myneni, R.B., Asrar, G. Photon interaction cross sections for aggregations of finite dimensional leaves [J]. Remote Sensing of Environment,1991,37:219-224.
[52]Nemani, R., Pierce. L., Running, S. Developing satellite-derived estimates of surface Moisture status [J]. Journal of Hydrology,1993.32:548-557.
[53]Nishida, K. An operational remote sensing algorithm of land surface evaporation [J]. Journal of Geophysical Research,2003,108 (D9):5-14.
[54]Njoku, E.G., Li, L. Retrieval of land surface parameters using passive microwave measurements at 6-18GHz [J]. IEEE Trans Geoscience Remote Sensing,1999.37(1):79-93.
[55]Okada, N., Tatano, H., Hagihara, Y., et al. Integrated Research on Methodological Development of Urban Diagnosis for Disaster Risk and its Applications [J]. Disaster Prevention Research Institute Annuals, Kyoto University,2004,47(C):1-8.
[56]Oker-Blom, P., Lappi, J., Smolander, H. Radiation regime and photosynthesis of coniferous stands. Photon-Vegetation Interactions:Applications in Plant Physiology and Optical Remote Sensing [M]. New York:Springer-Verlag,1991:469-499.
[57]Palmer, W.C.Meteorological Drought [R]. Research Paper No.45. Washington DC:U S Department of Commerce, Weather Bureau,1965:45-58.
[58]Palmer, W.C. Keeping track of crop moisture conditions, nationwide:The new crop moisture index [J]. Weatherwise,1968 (21):156-161.
[59]Penuelas, J., Filella, I., Biel, C., et al. The reflectance at the 950-970mm region as an indicator of plant water status [J]. International Journal of Remote Sensing,1993,14(10): 1887-1905.
[60]Peters, A.J., Walter-Shea, E.A., Lei, J., et al. Drought monitoring with NDVI-based standardized vegetation index [J]. Photogrammetric Engineering and Remote Sensing,2002,65(1): 71-75.
[61]Price, J.C. On the analysis of thermal infrared imagery the limited utility of apparent thermal inertia [J]. Remote Sensing of Environment,1985,18(1):59-93.
[62]Price, J.C. Thermal inertia mapping a new view of the earth [J]. Journal of Geophysical Research,1977,82(18):2582-2590.
[63]Price, J.C. Using spatial context in satellite data to infer regional scale evaportranspiration [J]. IEEE transactions on Geoscience and Remote Sensing,1990, (28):940-948.
[64]Qi, J., Chehbouni, A., Huete. A.R. A modified soil adjusted vegetation index [J]. Remote Sensing of Environment.1994,48(2):119-126.
[65]Quiring, S.M., Srinivasan, G. Evaluating the utility of the vegetation condition index (VCI) for monitoring meteorological drought in Texas [J].Agricultural and Forest Meteorology. 2010,150:330-339.
[66]Reed, B.C. Using remote sensing and geographic information systems for analyzing landscape/drought interaction [J].International Journal of Remote Sensing,1993,14(18): 3489-3503.
[67]Rousse. J.W, Haas, R.W, Schelle, J.A. Monitoring vegetation systems in the great plains with ERTS[C]. Third ERTS Symposium, NASA SP-3511,1973:309-317.
[68]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.
[69]Sehmugge, T. J., Gloersen, P., Wilheit, T. Remote sensing of soil moisture with microwave radiometers [J]. Journal of Geophysical Research,1974,79(2):317-323.
[70]Sellers, P.J., Berry, J.A., Collatz, G.J., et al. Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme [J]. Remote Sensing of Environment,1992,42(3):187-216.
[71]Sellers, P.J.. Dickinson. R.E., Randall, D.A.. et al. Modeling the exchange of energy.water. and carbon between continents and atmosphere [J]. Science.1997.275:602-509.
[72]Shook, G. An assessment of disaster risk and its management in Thailand [J]. Disasters. 1997,21(1):77-88.
[73]Smith, K. Environmental Hazards:Assessing Risk and Reducing Disaster [M].New York. Routledge,1996.
[74]Teng, W.L., Wang, J.R., Doraiswmay, P.C. Relationship between satellite microwave radiometric data, antecedent precipitation index, and regional soil moisture [J].International Journal of Remote Sensing.1993.14(13):2483-2500.
[75]Tobin. G.A., Montz, B.E. Natural Hazards:Explanation and Integration [M]. New York, the Guilford Press,1997.
[76]Tsakiris, G., Pangalou, D., Vangelis, H. Regional drought assessment based on the reconnaissance Drought Index (RDI) [J]. Water Resources Management,2007,21(5):821-833.
[77]Turner, M.D., Congalton, R.G. Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African flood plain [J]. International Journal of Remote Sensing, 1998,19(1):12-41.
[78]Verstraete, M.M., Pinty, B., Dickenson, R.E. A physical model of the bidirectional reflectance of vegetation canopies [J]. Journal of Geophysical Research,1990,95:11765-11775.
[79]Wang, J., Price, K.P., Rich, P.M. Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains [J]. International Journal of Remote Sensing,2001,22(18): 3827-3844.
[80]Wang, J., Richp, M., Price, K.P. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA [J]. International Journal of Remote Sensing,2003, 24(11):2345-2364.
[81]Wang, L., Li, Z., Chen, Q. Soil moisture retrieval with AMSR-E in the region with vegetation coverage [J]. High Technology Letters,2006,16 (2):204-209.
[82]Watson, R.L.C., Offield, T.W. Application of thermal modeling in the geologic interpretation of IR images [J]. Remote Sensing of Environment,1971,2017-2041.
[83]Xiao, X., Boles, S., Frolking, S., et al. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images [J]. Remote Sensing of Environment,2006, 100:95-113.
[84]Xiao, X., Boles, S., Liu, J., et al. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images [J]. Remote Sensing of Environment,2005,95:480-492.
[85]Zhou, Tianjun, Yu, Rucong, Chen, Haoming, Dai, Aiguo, Pan, Yang. Summer Precipitation Frequency, Intensity, and Diurnal Cycle over China:A Comparison of Satellite Data with Rain Gauge Observations [J]. Journal of Climate,2008,21:3997-4010.
[86]阿布都瓦斯提·吾拉木,李召良,秦其明等.全覆盖植被冠层水分遥感监测的一种方法：短波红外垂直失水指数[J].中国科学(D辑),2007,37(7)：957-965.
[87]阿布都瓦斯提·吾拉木.基于n维光谱特征空间的农田干旱遥感监测[D].北京：北京大学博士论文,2006.
[88]安顺清,邢久星.修正的帕默尔干旱指数及其应用[J].气象,1985,11(12)：17-19.
[89]陈怀亮,冯定原,邹春辉.麦田土壤水分NOAA/AVHRR遥感监测方法研究[J].遥感技术与应用,1998,13(4)：27-35.
[90]陈琼,周强,张海峰,刘峰贵.三江源地区基于植被生长季的NDVI对气候因子响应的差异性研究[J].生态环境学报,2010,19(6)：1284-1289.
[91]陈万金,信乃诠.中国北方旱地农业综合发展与对策[M].北京：中国农业科技出版社,1994.
[92]陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱监测中的应用[J].环境遥感,1994,9(2)：394-400
[93]陈香,福建省台风灾害风险评估与区划[J].生态学杂志,2007,26(6)：961-966.
[94]陈玉民,孙景生,肖俊夫.节水灌溉的土壤水分控制标准问题研究[J].灌溉排水,1997,16(1)：24-28.
[95]陈仲全,张正栋.干旱指数与旱灾测防系统[J].中国沙漠,1995,15(1)：10-18.
[96]成玉祥,任春林,张骏.基于BP神经网络的地质灾害风险评估方法探讨——以天水地区为例[J].中国地质灾害与防治学报,2008,19(2)：100-104.
[97]重庆市统计年鉴[EB/OL].http://www.cqtj.gov.cn/.
[98]杜宏成,闫玉静.综合评价中不可缺少的部分—权重的确定[J].黑龙江科技信息,2009,3：57-58.
[99]樊高峰,苗长明,毛裕定.干旱指标及其在浙江省干早监测分析中的应用[J].气象,2006,32(2)：70-74.
[100]樊运晓,罗云,陈庆寿.区域承灾体脆弱性评价因子体系研究[J].现代地质.2001.15(1)：113-116.
[101]冯平,李绍飞,王仲珏.干旱识别与分析指标综述[J].中国农村水利水电,2002,7：13-15.
[102]高中灵,徐新刚,王纪华等.基于时间序列NDVI相似性分析的棉花估产[J].农业工程学报,2012,28(2)：148-153.
[103]宫德吉,陈素华.农业气象灾害损失评估方法及其在产量预报中的应用[J].应用气象学报,1999,10(1)：66-71.
[104]顾晓鹤,宋国宝,韩立建等.基于变化向量分析的冬小麦长势变化监测研究[J].农业工程学报,2008,24(4)：159-165.
[105]郭妮,陈添宇,雷建勤等.用NOAA卫星可见光和近红外资料估算甘肃省东部农田区土壤湿度[J].应用气象学报,1997,8(2)：212-218.
[106]郭迎春,张广录,阮新等.气候对农业影响的定量评价模式及方法研究以河北省为例[J].地理学与国土研究,2002,18(4)：100-103.
[107]韩丽娟,王鹏新,王锦地等.植被指数-地表温度构成的特征空间研究[J].中国科学D辑,2005,35(4)：371-377.
[108]何斌,赵林,刘明等.湖南省农业旱灾风险综合分析与定量评价[J].安徽农业科学,2010,38(3)：1559-1562,1578.
[109]侯英雨,何延波,柳钦火等.干旱监测指数研究[J].生态学杂志,2007,26(6)：892-897.
[110]侯英雨,王石立.基于作物植被指数和温度的产量估算模型研究[J].地理学与国土研究,2002,18(3)：105-107.
[111]胡学文.我国气象灾害时空观[J].贵州气象,1994,18(4)39-42.
[112]黄晚华,杨晓光,李茂松等.基于标准化降水指数的中国南方季节性干旱近58 a演变特征[J].农业工程学报,2010,26(7)：50-58.
[113]季漩,罗毅TRMM降水数据在中天山区域的精度评估分析[J].干旱区地理,2013,36(2)：253-262.
[114]江东,王乃斌,杨小唤.植被指数-地面温度特征空间的生态学内涵及其应用[J].地理科学进展,2001,20(2)：146-152.
[115]焦险峰,杨邦杰,裴志远,王飞.基于植被指数的作物产量监测方法研究[J].农业工程学报,2005,21(4)：104-108.
[116]康绍忠,熊运章.作物缺水状况的判断方法与灌水指标研究[J].水利学报,1991,1：34-39.
[117]李本纲,陶澍.AVHRR NDVI与气候因子的相关分析[J].生态学报,2000.20(5)：898-902.
[118]李炳辉,杨礼祥.湖南省旱情评价指标体系研究[J].人民长江,2007,38(8)：160-163.
[119]李红英,张晓煜,曹宁等.两种干旱指标在干旱致灾因子危险性中的对比分析—以宁夏为例[J].灾害学,2012,27(2)：58-61.
[120]李华朋,张树清,高自强,孙妍MODIS值被指数监测农业干旱的适宜性评价[J].光谱学与光谱分析,2013,,33(3)：756-761.
[121]李景刚,黄诗峰.基于TRMM数据的汉江流域1998年—2010年降水变化特征[J].南水北调与水利科技,2011.9(6)：1-7
[122]李景刚,阮宏勋,李纪人,黄诗峰TRMM降水数据在气象干旱监测中的应用研究[J].水文.2010,30(4)：43-46.
[123]李景宜,周旗,严瑞.国民灾害感知能力测评指标体系研究[J].自然灾害学报.2002,,11(4)：129-134.
[124]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.
[125]李文梅,覃志豪,李文娟等MODIS NDVI与MODIS EVI的比较分析[J].遥感信息,2010,6：74-78.
[126]李晓兵.史培军.中国典型植被类型NDVI动态变化与气温、降水变化的敏感性分析[J].植物生态学报,2000,24(3)：379-382.
[127]李杏朝.微波遥感监测土壤水分的研究初探[J].遥感技术与应用.1995.10(4)：1-8.
[128]李喆.谭德宝,秦其明,催远来.基于特征空间的遥感干旱监测方法综述[J].长江科学院院报,2010.27(1)：37-40.
[129]梁芸,金文岩,郭铌等.陇东地区植被指数NDVI与降水的关系研究[J].干旱地区农业研究,2009,27(6)：247-251.
[130]刘庚山,郭安红,安顺清等.帕默尔干旱指标及其应用研究进展[J].自然灾害学报,2004,13(4)：21-27.
[131]刘良明.基于EOSMODIS数据的遥感干旱预警模型研究[D].武汉大学博士学位论文.2004.
[132]刘希林.区域泥石流风险评价研究[J].自然灾害学报,2000,9(1)：54-61.
[133]刘晓帆,任立良,袁飞,徐静,刘薇,喇承芳.老哈河流域NDVI动态变化及其与气候因子的关系[J].河海大学学报(自然科学版),2010,38(1)：1-5.
[134]刘晓英,郝卫平,张健.农田尺度下干旱指标及应用[J].中国农业气象,2005,26(2)：99-105.
[135]刘永红,李茂松.四川季节性干旱与农业防空节水技术研究[M].北京,.科学术版社.2011
[136]刘元波,傅巧妮,宋平,赵晓松,豆翠翠.卫星遥感反演降水研究综述[J].地球科学进展,2011,26(11)：1162-1171.
[137]刘志明.利用气象卫星信息遥感土壤水分的探讨[J].遥感信息,1992,(1)：21-23.
[138]罗伯良,黄晚华,帅细强,谢佰承,彭莉.湖南水稻生产旱灾风险区划研究[J].安徽农业科学,2009,37(33)：16429-16431.
[139]罗秀陵,薛勤,张长虹等.应用NOAA-AVHRR资料监测四川干旱[J].气象,1996,22(5)：35-38.
[140]骆三TRMM3842产品检验及其与台站资料在中国区域的融合方法研究[D].南京：南京信息工程大学理学硕士学位论文.2011.
[141]马晓群,姚筠,许莹.安徽省农作物干旱损失动态评估模型及其试用[J].灾害学,2010,25(1)：13-17.
[142]毛鹏飞.基于多遥感参数的冬小麦长势综合监测指标研究[D].郑州：河南大学硕士学位论文,2011.
[143]么枕生,裕国.气侯统计[M].北京：气象出版社,1990：954.
[144]彭代亮,黄敬峰,王秀珍.基于MODIS-EVI区域植被季节变化与气象因子的关系[J]. 应用生态学报,2007,18(5)：983-989.
[145]彭京备,张庆云,布和朝鲁.2006年川渝地区高温干旱特征及其成因分析[J].气候与环境研究,2007,12(3)：464-474.
[146]齐述华.干旱监测遥感模型及中国干旱时空分析[D].北京：中国科学院应用研究所博士论文,2004.
[147]气象干旱等标(GB/T 20481-2006)[EB/OL]. http://www.csres.com/detail/127294.html.
[148]乔平林,张继贤.王翠华.基于星载被动微波遥感的地表土壤湿度反演.辽宁工程技术大学学报,2005,25(3)：342-344.
[149]秦越,徐翔宇,许凯,李爱花,杨大文.农业干旱灾害风险模糊评价体系及其应用[J].农业工程学报,,2013,29(10)：83-91.
[150]渠翠平,关德新,王安志,金昌杰,吴家兵,王纪军,倪攀,袁凤辉.科尔沁草甸草地归一化植被指数与气象因子的关系[J].应用生态学报.2009.20(1)：58-64.
[151]史培军.三论灾害研究的理论与实践[J].自然灾害学报,2002,11(3)：1-9.
[152]四川省统计年鉴[EB/OL]. http://www.sc.stats.gov.cn/sctj/.
[153]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.
[154]孙华生.利用多时相MODIS数据提取大尺度的中国水稻种植面积和长势信息[D].杭州：浙江的大学博士学位论文,2009.
[155]孙丽,陈焕伟,赵立军等.遥感监测旱情的研究进展[J].农业环境科学学报,2004,26(1)：202-206.
[156]王宝英,张学.农作物高产的适宜土壤水分指标研究[J].灌溉排水,1996,15(3)：35-37.
[157]王博,崔春光,彭涛,康志强,赵晓玲.暴雨灾害风险评估与区划的研究现状与进展[J].暴雨灾害.2007.26(3)：281-286.
[158]王春乙.重大农业气象灾害研究进展[M].北京,气象出版社.2007.
[159]王纯枝,毛留喜,何延波等.温度植被干旱指数(TVDI)在黄淮海平原土壤湿度反演中的应用研究[J].土壤通报,2009,40(5)：998-1004.
[160]王道龙.我国主要粮食品种区域比较优势研究[J].中国农业资源区划与研究.2004,25(2)：13-17.
[161]王建华.基于模糊综合评判法的洪水灾害风险评估[J].水利科技与济,2009,15(4)：338-340.
[162]王劲松,郭江勇,倾继祖.一种K干旱指数在西北地区春旱分析中的应用[J].自然资源学报,2007,22(5)：709-717.
[163]王密侠,马成军,蔡焕杰.农业干旱指标研究与进展[J].干旱地区农业研究,1998,16(3)：119-124.
[164]王明田,张玉芳,马均,刘娟,李金建,陈东东.四川省盆地区玉米干旱灾害风险评估及区划[J].应用生态学报,2012,23(10)：20832811.
[165]王鹏新,WAN, Z.M.,龚健雅等.基于植被指数和土地表面温度的干旱监测模型[J].地球科学进展,2003,18(8)：527-533.
[166]王鹏新,孙威.条件植被温度指数干旱监测方法的研究与应用田[J].科技导报,2006,2(4)：56-58.
[167]王友贺,谷秀杰.农业干旱指标研究综述[J].第26届中国气象学会年会.会议论文,2009.
[168]王瑜莎.气候变化背景下西北地区玉米干旱灾损评估技术研究[D].兰州：甘肃农业大学硕士论文.2012.
[169]王雨,杨修.黑龙江省水稻气象灾害损失评估[J].中国农业气象,2007,28(4)：457-45气象与环境学报,2011,27(2).
[170]王长耀,骆成凤,齐述华等NDVI-Ts空间全国土地覆盖分类方法研究.遥感学报,2005,(1)：93-99.
[171]卫捷,马柱国.2003Palme干旱指数、地表湿润指数与降水距平的比较[J].地理学报,2003,58：117-124.
[172]魏瑞江,姚树然,王云秀.河北省主要农作物农业气象灾害灾损评估方法[J].中国农业气象,2000,21(1)：27-31.
[173]吴洪宝.我国东南部夏季干旱指数研究[J].应用气象学报,2000,11(2)：137-144.
[174]吴素霞,毛任钊,李红军,侯美亭,杨帆.中国农作物长势遥感监测研究综述[J].中国农学通报,2005,21(3)319-319.
[175]武建军,杨勤业.干旱区农作物长势综合监测[J].地理研究,2002,21(5)：593-598.
[176]武晓波,阎守邕,田国良等.在GIS支持下用NOAA/AVHRR数据进行旱情监测[J].遥感学报,1998,4：280-284.
[177]肖国杰,李国春.遥感方法进行土壤水分监测的现状与进展[J].西北农业学报,2006,15(1)：121-126.
[178]肖乾广,陈维英,盛永伟等.用气象卫星监测土维水分的试验研究[J].应用气象学报,1994,5(3)：312-317.
[179]徐凡.华北地区冬小麦旱灾灾损评估研究[D].北京：中国农科学院学位论文,2009
[180]徐国昌.干旱气象的研究内容和手段[M].北京：气象出版社,1989.
[181]徐新刚、吴炳方,蒙继华等.农作物单产遥感估算模型研究进展[J].农业工程学报,2008,24(2)：290—298.
[182]徐新刚.农作物产量模型研究[D].北京：中国科学院研究生院,2007.
[183]闫娜,杜继稳,李登科等.干旱遥感监测方法研究应用进展[J].灾害学.2008,23(4)：117-121.
[184]闫岩,柳钦火,刘强等.基于遥感数据与作物生长模型同化的冬小麦长势监测和估产方法研究[J].遥感学报,2006,10(5)：804-811.
[185]严翼,肖飞,杜耘,凌峰,李晓冬,李元征.利用植被状态指数距平监测2011年长江中下游5省春、夏干旱[J].长江流域资源与环境,2012,21(9)：1154-1159.
[186]杨涛,宫辉力.李小娟,赵文吉,盂丹.土壤水分遥感监测研究进展[J].生态学报2010.30(22)：6264—6277.
[187]杨邦杰,裴志远,周清波等.我国农情遥感监测关键技术研究进展[J].农业工程学报,2002,18(3)：191-194.
[188]杨绍锷,吴炳方,熊隽,闫娜娜.基于TRMM降水产品计算月降水量距平百分率[J]. 遥感信息,2010,5：62-66.
[189]杨小利,刘庚山,杨兴国.甘肃黄土高原帕尔默旱度模式的修正[J].干旱气象,2005,23(2)：8-12.
[190]姚国章,袁敏.干旱预警系统建设的国际经验与借鉴[J].中国应急管理,2010,(3)：43-48.
[191]叶金玉,林广发,张明锋.自然灾害风险评估研究进展[J].2010,防灾科技报,2010,12(3)：20-25.
[192]于文颖,冯锐,纪瑞鹏,张玉书,陈鹏狮,赵先丽,武晋雯.基于MODIS数据的水稻种植面积提取研究进展[J],气象与环境学报,2011,27(2)：56-61.
[193]袁文平,周广胜.标准化降水指标与Z指数在我国应用的对比分析[J].植物生态学报,2004,28(4)：523-529.
[194]袁文平,周广胜.干旱指标的理论分析与研究展望[J].地球科学进展,2004,19(6)：982-991.
[195]詹志明,秦其明,阿布都瓦斯提·吾拉木等.基于NIR-Red光谱特征空间的土壤水分监测新方法[J].中国科学D辑,2006,36(11)：1020-1026.
[196]张强,张良,崔显成,曾剑.干旱监测与评价技术的发展及其科学挑战[J].地球科学进展,2011,26(7)：763-778.
[197]张爱民,马晓群,杨太明等.安徽省旱涝灾害及其对农作物产量影响[J].应用气象学报,2007,18(5)：619-626.
[198]张超.基于GIS干旱风险评估与区划方法研究—京津冀冬小麦为例[D].石家庄：河北师范大学学位论文,2010.
[199]张艟锐.重庆地区水稻需水量分析与计算[J].四川水利,1990,11(8)：31-34.
[200]张存杰,王宝灵,刘德祥.西北地区旱涝指标的研究[J].高原气象,1998,17(4)：381-389.
[201]张宏群,马晓群,陈晓艺,吴文玉.基于GIS的安徽省冬小麦农业干旱脆弱性评价[J]中国农学通报,2011,27(33)：146-150
[202]张会,张继权,韩俊山.基于GIS技术的洪涝灾害风险评估与区划研究——以辽河中 下游地区为例[J].自然灾害学报,2005,14(6)：141-146.
[203]张继权,李宁.主要气象灾害风险评价与管理的数量化方法及其应用[M].北京：北京师范大学出版社,2007.
[204]张继权,刘兴朋,佟志军.草原火灾风险评价与分区[J].地理研究,2007,26(4)：755-762.
[205]张继权,严登华,王春乙,刘兴朋,佟志军.辽西北地区农业干旱灾害风险评价与风险区划研究[J].防灾减灾工程学报,2012,6(3)：300-306.
[206]张继权,冈田宪夫,多多纳裕一.综合自然灾害风险管理—全面整合的模式与中国的战略选择[J].自然灾害学报,2006,15(1)：29-37.
[207]张强,潘学标,马柱国.干旱[M].北京：气象出版社,2009.
[208]张强.华北地区干旱指数的确定及其应用[J].灾害学,1998,13(4)：34-38.
[209]张仁华,孙晓敏,朱治林等.以微分热惯量为基础的地表蒸发全遥感信息模型及在甘肃沙坡头地区的验证[J].中国科学(D辑),2002,2(12)：1041-1050.
[210]张星,张春桂,吴菊薪等.福建农业气象灾害的产量灾损风险评估[J].自然灾害学报,2009.18(1)：90-94.
[211]张学艺,张晓煜,李剑萍,舒志亮,曹宁.我国干旱遥感监测技术方法研究进展[J].气象科技,2007,35(4)：574-578.
[212]张正斌,山仑.作物水分利用效率和蒸发蒸腾估算模型的研究进展[J].干旱地区农业研究,1997,15(1)：73-76.
[213]赵虎,杨正伟,李霖,狄黎平.作物长势遥感监测指标的改进与比较分析[J].农业工程学报.2011.27(1)：243-249.
[214]赵昕奕,刘继韩.黄淮海平原各小麦生长期旱情分析[J].地理科学,1999.19(2)：181-185.
[215]赵英时.遥感应用分析原理与方法[M].北京：科学出版社.2003.
[216]郑晓东,鲁帆,马静等.基于标准化降水指数的淮河流域干旱演变特征分析[J].水利水电技术,2012,43(4)：102-105.
[217]郑长春,王秀珍,黄敬峰.多时相MODIS影像的浙江省水稻种植面积信息提取方法研 究[J].浙江大学学报(农业与生命科学版),2009,35(1)：98-104.
[218]中国统计年鉴[EB/OL].http://www.stats.gov.cn/.
[219]朱永豪,邓仁达,卢亚非等.不同湿度条件下黄棕壤光谱反射率的变化特征及其遥感意义[J].土壤学报,1984,21(2)：194.
[2]Bindlish, R., Jackson, T.J.,Gasiewski, A.J., Klein, M., Njoku, E.G. Soil moisture mapping and AMSR-E validation using the PSR in SMEX02 [J]. Remote Sensing of Environment,2006, 103(2):127-139.
[3]Brown, J.F, Wardlow, B.D, Tsegaye, T. The vegetation drought response index a new integrated approach for monitoring drought stress in vegetation [J]. GIScience and Remote Sensing,2008,45(1):16-46.
[4]Brown, John. E.M.An analysis of the performance of hybrid infrared and microwave satellite precipitation algorithms over India and adjacent regions. Remote Sensing of Environment [J]. 2006,101(1):63-81.
[5]Carlson,T.N. An overview of the "triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery [J]. Sensors,2007,7(8):1612-1629.
[6]Chauhan, N.S., Miller, S., Ardanuy, P. Space borne soil moisture estimation at high remote sensing of resolution:a microwave-optical/IR synergistic approach [J]. International Journal of Remote Sensing,2003.24(22):4599-4622.
[7]Chen, C.F., Nguyen, T.S., Li, Y.C. Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data [J]. Applied Geography, 2011,31(2):463-475.
[8]Chiu, L.J., Vongsaard. S. Morain,A. Budge, C. Comparison of TRMM and water division rain rates over New Mexico [J]. Advanced Atmosphere Sciences.2006,23(1):1-13.
[9]Daniel, A. Vila. Luis Gustavo G. de Goncalves,David L. Toll Jose Roberto Rozante. Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America [J]. Journal of Hydrometeorology,2009.10(2):533-543.
[10]David, L. B. J. Constrained two layer models for estimating evaportranspiration [G].11th Asia conference on Remote sensing, Zhongshan University, Guangzhou, China,16-19 Nov 1990.
[11]Earson, R.L, Miller, L.D. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass prairie, Pawnee National Grasslands, Colorado[J]. Proceedings of the 8th International Symposium on Remote Sensing of Environment,1972,1357-1381.
[12]Fensholt, R., Sandholt, I. Derivation of a shortwave infrared water stress index from MODIS near-and shortwave infrared data in a semiarid environment [J]. Remote Sensing of Environment,2003,87(1):111-121.
[13]Frate, D., Schiavon, G. Retrieving soil moisture and agricultural variables by microwave radiometry using neural networks [J]. Remote Sensing of Environment,2003,84(2):174-183.
[14]Fu, B.J., Chen, L.D. Agricultural landscape spatial pattern analysis in the semi-arid hill area of the Loess Plateau, China [J]. Journal of Arid Environments,2002,44(3):291-303.
[15]Gao, B.C. NDWI- a normalized difference water index for remote sensing of vegetation liquid water from space [J]. Remote Sensing of Environment,1996,58(3):257-266.
[16]Garen, D.C. Revised surface-water supply index for the western United States [J]. Journal of Water Resources Planning and Management,1992,119(4):437-454.
[17]Gillies, R.R., Carlson, T.N, Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models [J]. Journal of Applied Meteorology, 1995,34(4):745-756.
[18]Goetz, S.J. Multisensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site [J]. International Journal of Remote Sensing,1997,18(1):71-94.
[19]Goetz, S.J. Multisensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site [J]. International Journal of Remote Sensing,1997,18(1):71-94.
[20]Goward, S.N., Hope, A.S. Evaporation from combined reflected solar and emitted terrestrial radiation Preliminary FIFE results from AVHRR data [J]. Advances in Space Research,1989,9(7): 239-249.
[21]Groten, S.M.E. NDVI-crop monitoring and early yield assessment of Burkina Faso [J]. International Journal of Remote Sensing,1993,14(8):1495-1515.
[22]Gutman, G.G. Towards monitoring drought from space [J]. Journal of Climate,1990,3(2): 282-295.
[23]Guttman. N.B. Comparing the palmer drought index and the standardized precipitation index [J]. Journal of the American water resources association,1998.34(1):113-121.
[24]Han, Y.. Wang, Y. and Zhao, Y. Estimating soil moisture conditions of the Greater Changbai Mountains by land surface temperature and NDVI [J]. IEEE Transactions on Geoscience and Remote Sensing.2010,48:2509-2515.
[25]Heim, R.R. Drought indices:a review. Drought:A Global Assessment [M]. New York: Routledge,2000.
[26]Henry, A.J. The Cmatology of the United States [M]. Washington DC:U.S. Weather Bureau Bulletin Q,1906:51-58.
[27]Hong, S.Y.. Lee, K.S., Rim. S.K., et al. Estimation of rice field area using two-date Landsat TM images [G]. IEEE International Geoscience and Remote Sensing Symposium.1999,6(2): 732-734.
[28]Hu. M.Q, Mao. F., Sun, H., Hou. Y.Y. Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region [J]. International Journal of Applied Earth Observation and Geoinformation,2011,13:24-33.
[29]Hua-sheng Sun, Jing-feng, Huang, Alfredo, R. Mapping paddy rice in China with multi-date MODIS data [J]. Journal of Zhejiang University (Science A),2008.
[30]Huete, A., Didan, K., Miura, T., et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices [J]. Remote Sensing of Environment,2002,83(1-2): 195-213.
[31]Huete, A., Didan, k., Miura, T., et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices [J]. Remote Sensing of Environment,2002,83(1-2): 195-213.
[32]Huete, A.R. A soil-adjusted vegetation index (SAVI) [J]. Remote Sensing of Environment, 1988,25:295-309.
[33]Idso, S.B., Jackson, R.D., Pinter, P.J. Measuring yield-reducing plant water potential depressions in wheat by infrared thermometry [J]. Irrigation Science,1981,2(4):205-212.
[34]Idso, S.B., Jackson, R.D., Pinter, P.J. Measuring yield-reducing plant water potential depressions in wheat by infrared thermometry [J]. Irrigation Science,1981,2(4):205-212.
[35]IUGS. Quantitative risk assessment for slopes and landslides-the state of the art. In Cruden D and Fell R, eds. Landslide Risk Assessment [C]. Rotterdam:AA, Balkema,1997.
[36]Jackson, R.D., Idso, S.B. Canopy temperature as a crop water stress indicator [J].Water resources Research,1981,17(4):1133-1138.
[37]Jackson, R.D., Kustas, W.P., Choudhury, B.J. A reexamination of crop water stress index [J]. Irrigation Science,1988,9:309-317.
[38]Jackson, T. J., Le Vine, D.M., Hsu, A.Y., Oldak, A., Starks, P.J., Swift, C.T., Isham, J.D., Haken, M. Soil moisture mapping at regional scales using microwave radiometry:The southern Great Plains hydrology experiment [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999,37 (5):2136-2151.
[39]Ji, L., Peters, A.J. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices [J]. Remote Sensing of Environment,2003,87(1):85-98.
[40]Karl, T.R. The sensitivity of the palmer drought severity index and Palmer's Z-Index to their calibration coefficients including potential evaportranspiration [J]. Journal of Climate and Applied Meteorology,1986(25):77-86.
[41]Kogan, EN. Application of vegetation index and brightness temperature for drought Detection [J]. Advances in Space Research,1995,15(11):91-100.
[42]Kogan, F.N. Remote sensing of weather impacts on vegetation in no-homogeneous areas [J]. International Journal of Remote Sensing,1990,11:1405-1419.
[43]Kuusk, A. The hot spot effect of a uniform vegetative cover [J]. Soviet Russia Journal of Remote Sensing,1985:3645-3658.
[44]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.
[45]Lewis, J.E., Rowland, J., Nadeau, A. Estimating maize production in Kenya using NDVI some statistical considerations [J]. International Journal of Remote Sensing,1998,19(13): 2609-2617.
[46]Maskrey, A. Disaster Mitigation:A Community Based Approach [M]. Oxford:Oxfam,1989.
[47]McGuire, J.K., Palmer, W.C. The 1957 drought in the eastern United States [J]. Monthly Weather Review,1957,85(9):305-314.
[48]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.
[49]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.
[50]Munger, T.T. Graphic method of representing and comparing. Drought intensities [J]. Monthly Weather Review,1916, (44):642-643.
[51]Myneni, R.B., Asrar, G. Photon interaction cross sections for aggregations of finite dimensional leaves [J]. Remote Sensing of Environment,1991,37:219-224.
[52]Nemani, R., Pierce. L., Running, S. Developing satellite-derived estimates of surface Moisture status [J]. Journal of Hydrology,1993.32:548-557.
[53]Nishida, K. An operational remote sensing algorithm of land surface evaporation [J]. Journal of Geophysical Research,2003,108 (D9):5-14.
[54]Njoku, E.G., Li, L. Retrieval of land surface parameters using passive microwave measurements at 6-18GHz [J]. IEEE Trans Geoscience Remote Sensing,1999.37(1):79-93.
[55]Okada, N., Tatano, H., Hagihara, Y., et al. Integrated Research on Methodological Development of Urban Diagnosis for Disaster Risk and its Applications [J]. Disaster Prevention Research Institute Annuals, Kyoto University,2004,47(C):1-8.
[56]Oker-Blom, P., Lappi, J., Smolander, H. Radiation regime and photosynthesis of coniferous stands. Photon-Vegetation Interactions:Applications in Plant Physiology and Optical Remote Sensing [M]. New York:Springer-Verlag,1991:469-499.
[57]Palmer, W.C.Meteorological Drought [R]. Research Paper No.45. Washington DC:U S Department of Commerce, Weather Bureau,1965:45-58.
[58]Palmer, W.C. Keeping track of crop moisture conditions, nationwide:The new crop moisture index [J]. Weatherwise,1968 (21):156-161.
[59]Penuelas, J., Filella, I., Biel, C., et al. The reflectance at the 950-970mm region as an indicator of plant water status [J]. International Journal of Remote Sensing,1993,14(10): 1887-1905.
[60]Peters, A.J., Walter-Shea, E.A., Lei, J., et al. Drought monitoring with NDVI-based standardized vegetation index [J]. Photogrammetric Engineering and Remote Sensing,2002,65(1): 71-75.
[61]Price, J.C. On the analysis of thermal infrared imagery the limited utility of apparent thermal inertia [J]. Remote Sensing of Environment,1985,18(1):59-93.
[62]Price, J.C. Thermal inertia mapping a new view of the earth [J]. Journal of Geophysical Research,1977,82(18):2582-2590.
[63]Price, J.C. Using spatial context in satellite data to infer regional scale evaportranspiration [J]. IEEE transactions on Geoscience and Remote Sensing,1990, (28):940-948.
[64]Qi, J., Chehbouni, A., Huete. A.R. A modified soil adjusted vegetation index [J]. Remote Sensing of Environment.1994,48(2):119-126.
[65]Quiring, S.M., Srinivasan, G. Evaluating the utility of the vegetation condition index (VCI) for monitoring meteorological drought in Texas [J].Agricultural and Forest Meteorology. 2010,150:330-339.
[66]Reed, B.C. Using remote sensing and geographic information systems for analyzing landscape/drought interaction [J].International Journal of Remote Sensing,1993,14(18): 3489-3503.
[67]Rousse. J.W, Haas, R.W, Schelle, J.A. Monitoring vegetation systems in the great plains with ERTS[C]. Third ERTS Symposium, NASA SP-3511,1973:309-317.
[68]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.
[69]Sehmugge, T. J., Gloersen, P., Wilheit, T. Remote sensing of soil moisture with microwave radiometers [J]. Journal of Geophysical Research,1974,79(2):317-323.
[70]Sellers, P.J., Berry, J.A., Collatz, G.J., et al. Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme [J]. Remote Sensing of Environment,1992,42(3):187-216.
[71]Sellers, P.J.. Dickinson. R.E., Randall, D.A.. et al. Modeling the exchange of energy.water. and carbon between continents and atmosphere [J]. Science.1997.275:602-509.
[72]Shook, G. An assessment of disaster risk and its management in Thailand [J]. Disasters. 1997,21(1):77-88.
[73]Smith, K. Environmental Hazards:Assessing Risk and Reducing Disaster [M].New York. Routledge,1996.
[74]Teng, W.L., Wang, J.R., Doraiswmay, P.C. Relationship between satellite microwave radiometric data, antecedent precipitation index, and regional soil moisture [J].International Journal of Remote Sensing.1993.14(13):2483-2500.
[75]Tobin. G.A., Montz, B.E. Natural Hazards:Explanation and Integration [M]. New York, the Guilford Press,1997.
[76]Tsakiris, G., Pangalou, D., Vangelis, H. Regional drought assessment based on the reconnaissance Drought Index (RDI) [J]. Water Resources Management,2007,21(5):821-833.
[77]Turner, M.D., Congalton, R.G. Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African flood plain [J]. International Journal of Remote Sensing, 1998,19(1):12-41.
[78]Verstraete, M.M., Pinty, B., Dickenson, R.E. A physical model of the bidirectional reflectance of vegetation canopies [J]. Journal of Geophysical Research,1990,95:11765-11775.
[79]Wang, J., Price, K.P., Rich, P.M. Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains [J]. International Journal of Remote Sensing,2001,22(18): 3827-3844.
[80]Wang, J., Richp, M., Price, K.P. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA [J]. International Journal of Remote Sensing,2003, 24(11):2345-2364.
[81]Wang, L., Li, Z., Chen, Q. Soil moisture retrieval with AMSR-E in the region with vegetation coverage [J]. High Technology Letters,2006,16 (2):204-209.
[82]Watson, R.L.C., Offield, T.W. Application of thermal modeling in the geologic interpretation of IR images [J]. Remote Sensing of Environment,1971,2017-2041.
[83]Xiao, X., Boles, S., Frolking, S., et al. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images [J]. Remote Sensing of Environment,2006, 100:95-113.
[84]Xiao, X., Boles, S., Liu, J., et al. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images [J]. Remote Sensing of Environment,2005,95:480-492.
[85]Zhou, Tianjun, Yu, Rucong, Chen, Haoming, Dai, Aiguo, Pan, Yang. Summer Precipitation Frequency, Intensity, and Diurnal Cycle over China:A Comparison of Satellite Data with Rain Gauge Observations [J]. Journal of Climate,2008,21:3997-4010.
[86]阿布都瓦斯提·吾拉木,李召良,秦其明等.全覆盖植被冠层水分遥感监测的一种方法：短波红外垂直失水指数[J].中国科学(D辑),2007,37(7)：957-965.
[87]阿布都瓦斯提·吾拉木.基于n维光谱特征空间的农田干旱遥感监测[D].北京：北京大学博士论文,2006.
[88]安顺清,邢久星.修正的帕默尔干旱指数及其应用[J].气象,1985,11(12)：17-19.
[89]陈怀亮,冯定原,邹春辉.麦田土壤水分NOAA/AVHRR遥感监测方法研究[J].遥感技术与应用,1998,13(4)：27-35.
[90]陈琼,周强,张海峰,刘峰贵.三江源地区基于植被生长季的NDVI对气候因子响应的差异性研究[J].生态环境学报,2010,19(6)：1284-1289.
[91]陈万金,信乃诠.中国北方旱地农业综合发展与对策[M].北京：中国农业科技出版社,1994.
[92]陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱监测中的应用[J].环境遥感,1994,9(2)：394-400
[93]陈香,福建省台风灾害风险评估与区划[J].生态学杂志,2007,26(6)：961-966.
[94]陈玉民,孙景生,肖俊夫.节水灌溉的土壤水分控制标准问题研究[J].灌溉排水,1997,16(1)：24-28.
[95]陈仲全,张正栋.干旱指数与旱灾测防系统[J].中国沙漠,1995,15(1)：10-18.
[96]成玉祥,任春林,张骏.基于BP神经网络的地质灾害风险评估方法探讨——以天水地区为例[J].中国地质灾害与防治学报,2008,19(2)：100-104.
[97]重庆市统计年鉴[EB/OL].http://www.cqtj.gov.cn/.
[98]杜宏成,闫玉静.综合评价中不可缺少的部分—权重的确定[J].黑龙江科技信息,2009,3：57-58.
[99]樊高峰,苗长明,毛裕定.干旱指标及其在浙江省干早监测分析中的应用[J].气象,2006,32(2)：70-74.
[100]樊运晓,罗云,陈庆寿.区域承灾体脆弱性评价因子体系研究[J].现代地质.2001.15(1)：113-116.
[101]冯平,李绍飞,王仲珏.干旱识别与分析指标综述[J].中国农村水利水电,2002,7：13-15.
[102]高中灵,徐新刚,王纪华等.基于时间序列NDVI相似性分析的棉花估产[J].农业工程学报,2012,28(2)：148-153.
[103]宫德吉,陈素华.农业气象灾害损失评估方法及其在产量预报中的应用[J].应用气象学报,1999,10(1)：66-71.
[104]顾晓鹤,宋国宝,韩立建等.基于变化向量分析的冬小麦长势变化监测研究[J].农业工程学报,2008,24(4)：159-165.
[105]郭妮,陈添宇,雷建勤等.用NOAA卫星可见光和近红外资料估算甘肃省东部农田区土壤湿度[J].应用气象学报,1997,8(2)：212-218.
[106]郭迎春,张广录,阮新等.气候对农业影响的定量评价模式及方法研究以河北省为例[J].地理学与国土研究,2002,18(4)：100-103.
[107]韩丽娟,王鹏新,王锦地等.植被指数-地表温度构成的特征空间研究[J].中国科学D辑,2005,35(4)：371-377.
[108]何斌,赵林,刘明等.湖南省农业旱灾风险综合分析与定量评价[J].安徽农业科学,2010,38(3)：1559-1562,1578.
[109]侯英雨,何延波,柳钦火等.干旱监测指数研究[J].生态学杂志,2007,26(6)：892-897.
[110]侯英雨,王石立.基于作物植被指数和温度的产量估算模型研究[J].地理学与国土研究,2002,18(3)：105-107.
[111]胡学文.我国气象灾害时空观[J].贵州气象,1994,18(4)39-42.
[112]黄晚华,杨晓光,李茂松等.基于标准化降水指数的中国南方季节性干旱近58 a演变特征[J].农业工程学报,2010,26(7)：50-58.
[113]季漩,罗毅TRMM降水数据在中天山区域的精度评估分析[J].干旱区地理,2013,36(2)：253-262.
[114]江东,王乃斌,杨小唤.植被指数-地面温度特征空间的生态学内涵及其应用[J].地理科学进展,2001,20(2)：146-152.
[115]焦险峰,杨邦杰,裴志远,王飞.基于植被指数的作物产量监测方法研究[J].农业工程学报,2005,21(4)：104-108.
[116]康绍忠,熊运章.作物缺水状况的判断方法与灌水指标研究[J].水利学报,1991,1：34-39.
[117]李本纲,陶澍.AVHRR NDVI与气候因子的相关分析[J].生态学报,2000.20(5)：898-902.
[118]李炳辉,杨礼祥.湖南省旱情评价指标体系研究[J].人民长江,2007,38(8)：160-163.
[119]李红英,张晓煜,曹宁等.两种干旱指标在干旱致灾因子危险性中的对比分析—以宁夏为例[J].灾害学,2012,27(2)：58-61.
[120]李华朋,张树清,高自强,孙妍MODIS值被指数监测农业干旱的适宜性评价[J].光谱学与光谱分析,2013,,33(3)：756-761.
[121]李景刚,黄诗峰.基于TRMM数据的汉江流域1998年—2010年降水变化特征[J].南水北调与水利科技,2011.9(6)：1-7
[122]李景刚,阮宏勋,李纪人,黄诗峰TRMM降水数据在气象干旱监测中的应用研究[J].水文.2010,30(4)：43-46.
[123]李景宜,周旗,严瑞.国民灾害感知能力测评指标体系研究[J].自然灾害学报.2002,,11(4)：129-134.
[124]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.
[125]李文梅,覃志豪,李文娟等MODIS NDVI与MODIS EVI的比较分析[J].遥感信息,2010,6：74-78.
[126]李晓兵.史培军.中国典型植被类型NDVI动态变化与气温、降水变化的敏感性分析[J].植物生态学报,2000,24(3)：379-382.
[127]李杏朝.微波遥感监测土壤水分的研究初探[J].遥感技术与应用.1995.10(4)：1-8.
[128]李喆.谭德宝,秦其明,催远来.基于特征空间的遥感干旱监测方法综述[J].长江科学院院报,2010.27(1)：37-40.
[129]梁芸,金文岩,郭铌等.陇东地区植被指数NDVI与降水的关系研究[J].干旱地区农业研究,2009,27(6)：247-251.
[130]刘庚山,郭安红,安顺清等.帕默尔干旱指标及其应用研究进展[J].自然灾害学报,2004,13(4)：21-27.
[131]刘良明.基于EOSMODIS数据的遥感干旱预警模型研究[D].武汉大学博士学位论文.2004.
[132]刘希林.区域泥石流风险评价研究[J].自然灾害学报,2000,9(1)：54-61.
[133]刘晓帆,任立良,袁飞,徐静,刘薇,喇承芳.老哈河流域NDVI动态变化及其与气候因子的关系[J].河海大学学报(自然科学版),2010,38(1)：1-5.
[134]刘晓英,郝卫平,张健.农田尺度下干旱指标及应用[J].中国农业气象,2005,26(2)：99-105.
[135]刘永红,李茂松.四川季节性干旱与农业防空节水技术研究[M].北京,.科学术版社.2011
[136]刘元波,傅巧妮,宋平,赵晓松,豆翠翠.卫星遥感反演降水研究综述[J].地球科学进展,2011,26(11)：1162-1171.
[137]刘志明.利用气象卫星信息遥感土壤水分的探讨[J].遥感信息,1992,(1)：21-23.
[138]罗伯良,黄晚华,帅细强,谢佰承,彭莉.湖南水稻生产旱灾风险区划研究[J].安徽农业科学,2009,37(33)：16429-16431.
[139]罗秀陵,薛勤,张长虹等.应用NOAA-AVHRR资料监测四川干旱[J].气象,1996,22(5)：35-38.
[140]骆三TRMM3842产品检验及其与台站资料在中国区域的融合方法研究[D].南京：南京信息工程大学理学硕士学位论文.2011.
[141]马晓群,姚筠,许莹.安徽省农作物干旱损失动态评估模型及其试用[J].灾害学,2010,25(1)：13-17.
[142]毛鹏飞.基于多遥感参数的冬小麦长势综合监测指标研究[D].郑州：河南大学硕士学位论文,2011.
[143]么枕生,裕国.气侯统计[M].北京：气象出版社,1990：954.
[144]彭代亮,黄敬峰,王秀珍.基于MODIS-EVI区域植被季节变化与气象因子的关系[J]. 应用生态学报,2007,18(5)：983-989.
[145]彭京备,张庆云,布和朝鲁.2006年川渝地区高温干旱特征及其成因分析[J].气候与环境研究,2007,12(3)：464-474.
[146]齐述华.干旱监测遥感模型及中国干旱时空分析[D].北京：中国科学院应用研究所博士论文,2004.
[147]气象干旱等标(GB/T 20481-2006)[EB/OL]. http://www.csres.com/detail/127294.html.
[148]乔平林,张继贤.王翠华.基于星载被动微波遥感的地表土壤湿度反演.辽宁工程技术大学学报,2005,25(3)：342-344.
[149]秦越,徐翔宇,许凯,李爱花,杨大文.农业干旱灾害风险模糊评价体系及其应用[J].农业工程学报,,2013,29(10)：83-91.
[150]渠翠平,关德新,王安志,金昌杰,吴家兵,王纪军,倪攀,袁凤辉.科尔沁草甸草地归一化植被指数与气象因子的关系[J].应用生态学报.2009.20(1)：58-64.
[151]史培军.三论灾害研究的理论与实践[J].自然灾害学报,2002,11(3)：1-9.
[152]四川省统计年鉴[EB/OL]. http://www.sc.stats.gov.cn/sctj/.
[153]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.
[154]孙华生.利用多时相MODIS数据提取大尺度的中国水稻种植面积和长势信息[D].杭州：浙江的大学博士学位论文,2009.
[155]孙丽,陈焕伟,赵立军等.遥感监测旱情的研究进展[J].农业环境科学学报,2004,26(1)：202-206.
[156]王宝英,张学.农作物高产的适宜土壤水分指标研究[J].灌溉排水,1996,15(3)：35-37.
[157]王博,崔春光,彭涛,康志强,赵晓玲.暴雨灾害风险评估与区划的研究现状与进展[J].暴雨灾害.2007.26(3)：281-286.
[158]王春乙.重大农业气象灾害研究进展[M].北京,气象出版社.2007.
[159]王纯枝,毛留喜,何延波等.温度植被干旱指数(TVDI)在黄淮海平原土壤湿度反演中的应用研究[J].土壤通报,2009,40(5)：998-1004.
[160]王道龙.我国主要粮食品种区域比较优势研究[J].中国农业资源区划与研究.2004,25(2)：13-17.
[161]王建华.基于模糊综合评判法的洪水灾害风险评估[J].水利科技与济,2009,15(4)：338-340.
[162]王劲松,郭江勇,倾继祖.一种K干旱指数在西北地区春旱分析中的应用[J].自然资源学报,2007,22(5)：709-717.
[163]王密侠,马成军,蔡焕杰.农业干旱指标研究与进展[J].干旱地区农业研究,1998,16(3)：119-124.
[164]王明田,张玉芳,马均,刘娟,李金建,陈东东.四川省盆地区玉米干旱灾害风险评估及区划[J].应用生态学报,2012,23(10)：20832811.
[165]王鹏新,WAN, Z.M.,龚健雅等.基于植被指数和土地表面温度的干旱监测模型[J].地球科学进展,2003,18(8)：527-533.
[166]王鹏新,孙威.条件植被温度指数干旱监测方法的研究与应用田[J].科技导报,2006,2(4)：56-58.
[167]王友贺,谷秀杰.农业干旱指标研究综述[J].第26届中国气象学会年会.会议论文,2009.
[168]王瑜莎.气候变化背景下西北地区玉米干旱灾损评估技术研究[D].兰州：甘肃农业大学硕士论文.2012.
[169]王雨,杨修.黑龙江省水稻气象灾害损失评估[J].中国农业气象,2007,28(4)：457-45气象与环境学报,2011,27(2).
[170]王长耀,骆成凤,齐述华等NDVI-Ts空间全国土地覆盖分类方法研究.遥感学报,2005,(1)：93-99.
[171]卫捷,马柱国.2003Palme干旱指数、地表湿润指数与降水距平的比较[J].地理学报,2003,58：117-124.
[172]魏瑞江,姚树然,王云秀.河北省主要农作物农业气象灾害灾损评估方法[J].中国农业气象,2000,21(1)：27-31.
[173]吴洪宝.我国东南部夏季干旱指数研究[J].应用气象学报,2000,11(2)：137-144.
[174]吴素霞,毛任钊,李红军,侯美亭,杨帆.中国农作物长势遥感监测研究综述[J].中国农学通报,2005,21(3)319-319.
[175]武建军,杨勤业.干旱区农作物长势综合监测[J].地理研究,2002,21(5)：593-598.
[176]武晓波,阎守邕,田国良等.在GIS支持下用NOAA/AVHRR数据进行旱情监测[J].遥感学报,1998,4：280-284.
[177]肖国杰,李国春.遥感方法进行土壤水分监测的现状与进展[J].西北农业学报,2006,15(1)：121-126.
[178]肖乾广,陈维英,盛永伟等.用气象卫星监测土维水分的试验研究[J].应用气象学报,1994,5(3)：312-317.
[179]徐凡.华北地区冬小麦旱灾灾损评估研究[D].北京：中国农科学院学位论文,2009
[180]徐国昌.干旱气象的研究内容和手段[M].北京：气象出版社,1989.
[181]徐新刚、吴炳方,蒙继华等.农作物单产遥感估算模型研究进展[J].农业工程学报,2008,24(2)：290—298.
[182]徐新刚.农作物产量模型研究[D].北京：中国科学院研究生院,2007.
[183]闫娜,杜继稳,李登科等.干旱遥感监测方法研究应用进展[J].灾害学.2008,23(4)：117-121.
[184]闫岩,柳钦火,刘强等.基于遥感数据与作物生长模型同化的冬小麦长势监测和估产方法研究[J].遥感学报,2006,10(5)：804-811.
[185]严翼,肖飞,杜耘,凌峰,李晓冬,李元征.利用植被状态指数距平监测2011年长江中下游5省春、夏干旱[J].长江流域资源与环境,2012,21(9)：1154-1159.
[186]杨涛,宫辉力.李小娟,赵文吉,盂丹.土壤水分遥感监测研究进展[J].生态学报2010.30(22)：6264—6277.
[187]杨邦杰,裴志远,周清波等.我国农情遥感监测关键技术研究进展[J].农业工程学报,2002,18(3)：191-194.
[188]杨绍锷,吴炳方,熊隽,闫娜娜.基于TRMM降水产品计算月降水量距平百分率[J]. 遥感信息,2010,5：62-66.
[189]杨小利,刘庚山,杨兴国.甘肃黄土高原帕尔默旱度模式的修正[J].干旱气象,2005,23(2)：8-12.
[190]姚国章,袁敏.干旱预警系统建设的国际经验与借鉴[J].中国应急管理,2010,(3)：43-48.
[191]叶金玉,林广发,张明锋.自然灾害风险评估研究进展[J].2010,防灾科技报,2010,12(3)：20-25.
[192]于文颖,冯锐,纪瑞鹏,张玉书,陈鹏狮,赵先丽,武晋雯.基于MODIS数据的水稻种植面积提取研究进展[J],气象与环境学报,2011,27(2)：56-61.
[193]袁文平,周广胜.标准化降水指标与Z指数在我国应用的对比分析[J].植物生态学报,2004,28(4)：523-529.
[194]袁文平,周广胜.干旱指标的理论分析与研究展望[J].地球科学进展,2004,19(6)：982-991.
[195]詹志明,秦其明,阿布都瓦斯提·吾拉木等.基于NIR-Red光谱特征空间的土壤水分监测新方法[J].中国科学D辑,2006,36(11)：1020-1026.
[196]张强,张良,崔显成,曾剑.干旱监测与评价技术的发展及其科学挑战[J].地球科学进展,2011,26(7)：763-778.
[197]张爱民,马晓群,杨太明等.安徽省旱涝灾害及其对农作物产量影响[J].应用气象学报,2007,18(5)：619-626.
[198]张超.基于GIS干旱风险评估与区划方法研究—京津冀冬小麦为例[D].石家庄：河北师范大学学位论文,2010.
[199]张艟锐.重庆地区水稻需水量分析与计算[J].四川水利,1990,11(8)：31-34.
[200]张存杰,王宝灵,刘德祥.西北地区旱涝指标的研究[J].高原气象,1998,17(4)：381-389.
[201]张宏群,马晓群,陈晓艺,吴文玉.基于GIS的安徽省冬小麦农业干旱脆弱性评价[J]中国农学通报,2011,27(33)：146-150
[202]张会,张继权,韩俊山.基于GIS技术的洪涝灾害风险评估与区划研究——以辽河中 下游地区为例[J].自然灾害学报,2005,14(6)：141-146.
[203]张继权,李宁.主要气象灾害风险评价与管理的数量化方法及其应用[M].北京：北京师范大学出版社,2007.
[204]张继权,刘兴朋,佟志军.草原火灾风险评价与分区[J].地理研究,2007,26(4)：755-762.
[205]张继权,严登华,王春乙,刘兴朋,佟志军.辽西北地区农业干旱灾害风险评价与风险区划研究[J].防灾减灾工程学报,2012,6(3)：300-306.
[206]张继权,冈田宪夫,多多纳裕一.综合自然灾害风险管理—全面整合的模式与中国的战略选择[J].自然灾害学报,2006,15(1)：29-37.
[207]张强,潘学标,马柱国.干旱[M].北京：气象出版社,2009.
[208]张强.华北地区干旱指数的确定及其应用[J].灾害学,1998,13(4)：34-38.
[209]张仁华,孙晓敏,朱治林等.以微分热惯量为基础的地表蒸发全遥感信息模型及在甘肃沙坡头地区的验证[J].中国科学(D辑),2002,2(12)：1041-1050.
[210]张星,张春桂,吴菊薪等.福建农业气象灾害的产量灾损风险评估[J].自然灾害学报,2009.18(1)：90-94.
[211]张学艺,张晓煜,李剑萍,舒志亮,曹宁.我国干旱遥感监测技术方法研究进展[J].气象科技,2007,35(4)：574-578.
[212]张正斌,山仑.作物水分利用效率和蒸发蒸腾估算模型的研究进展[J].干旱地区农业研究,1997,15(1)：73-76.
[213]赵虎,杨正伟,李霖,狄黎平.作物长势遥感监测指标的改进与比较分析[J].农业工程学报.2011.27(1)：243-249.
[214]赵昕奕,刘继韩.黄淮海平原各小麦生长期旱情分析[J].地理科学,1999.19(2)：181-185.
[215]赵英时.遥感应用分析原理与方法[M].北京：科学出版社.2003.
[216]郑晓东,鲁帆,马静等.基于标准化降水指数的淮河流域干旱演变特征分析[J].水利水电技术,2012,43(4)：102-105.
[217]郑长春,王秀珍,黄敬峰.多时相MODIS影像的浙江省水稻种植面积信息提取方法研 究[J].浙江大学学报(农业与生命科学版),2009,35(1)：98-104.
[218]中国统计年鉴[EB/OL].http://www.stats.gov.cn/.
[219]朱永豪,邓仁达,卢亚非等.不同湿度条件下黄棕壤光谱反射率的变化特征及其遥感意义[J].土壤学报,1984,21(2)：194.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |