基于遥感技术的区域地表蒸散估算研究
|
摘要
区域地表蒸散的变化特征反映了陆面过程中能量和水分收支状况的演变趋势,同时也进一步影响区域气候和水资源总量分布,进而对区域经济发展产生影响。随着遥感技术的发展与逐渐成熟,研究大尺度范围的地表蒸散变得更为便捷。因此,开展基于遥感手段的区域地表能量与水分动态监测研究具有重要的意义和研究价值。
本文以遥感技术为主要手段,分别利用ETM+、MODIS两种不同尺度的遥感数据,并引入DEM数字高程数据,对广泛应用的SEBS蒸散模型加以改进,拓展其在复杂地形条件下的适用范围,同时探讨两种不同尺度的遥感数据对蒸散估算精度的影响,并融合ETM+和MODIS数据构建了多尺度遥感模型,改善了MODIS数据的尺度效应(混合像元)问题。本文以伊洛河流域为研究区域进行了实证研究,并对研究区2002年、2008年的地表蒸散时空分布格局进行了分析和探讨,具有比较重要的理论意义和实践价值。研究的主要结论如下
(1)利用ETM+和MODIS遥感数据可以较好地估算区域地表蒸散量。其中,利用ETM+数据估算的结果比利用MODIS数据估算的结果精度要高,与地面实测数据的对比分析表明,平均相对误差分别在10%和20%左右,高分辨率的影像数据对蒸散估算的精度有一定程度的提高。
(2)基于DEM改进的净辐射通量估算山区模式可以用于地形复杂区域的净辐射通量估算。
山区模式考虑到区域海拔高度、坡度、坡向的不同以及周围地形的遮蔽,从三个方面进行太阳直接辐射的地形修正：首先,依据坡度、坡向、纬度等因子修正坡面太阳入射角的余弦值；其次,利用坡面太阳入射角的余弦值与太阳天顶角的余弦值的比值来修正坡面接收的辐射量；最后,引入一个二值函数——地形遮蔽因子来修正邻近地形的阴影遮蔽带来的影响。在研究区的实证结果表明,未考虑DEM情况下的净辐射通量估算结果不能准确反应研究区的地表特征,存在较大偏差,因此在地形复杂条件下的区域净辐射通量估算必须考虑地形的影响。
(3)改进的SEBS模型估算的蒸散精度较高,模型在研究区的适应性和稳定性较好。
以广泛应用的SEBS模型为基础,引入DEM数据,对坡面上太阳辐射、大气透射率、地表温度、地表粗糙度等地表参数及动力学参数进行了地形(海拔高度、坡度、坡向)修正使得模型更能适应复杂地形条件下的区域地表蒸散估算,拓展了蒸散模型的适用范围。经过在研究区的应用结果表明,改进的SEBS模型估算的日蒸散量和实测值的误差较小,估算精度较高,模型在研究区的适应性和稳定性较好,利用ETM+数据对研究区地表蒸散的估算结果精度要高于MODIS数据,改进的SEBS模型能够反映出研究区的地表覆盖类型和水分分布的时空格局。
(4)分析了MODIS数据在蒸散估算中由混合象元造成的尺度误差,并基于空间增强方法构建了多尺度遥感模型,提高了模型估算的精度,使得进行高频率的区域地表能量与水分的动态监测具有可操作性,也拓展了MODIS和TM/ETM+数据的应用范围,提高了数据的利用率。
多尺度遥感模型结合TM/ETM+的高空间分辨率数据对MODIS精度较高的地表温度数据进行增强,发挥二者的优势,以得到的30m高分辨率LST为输入参数,提高了模型估算的精度。尤其在F垫面破碎度高、土地利用类型复杂、混合像元严重的区域,多尺度遥感模型可以使估算的蒸散量精度得到一定程度的改善甚至显著提高。同时,MODIS的多时相优势也能得到充分发挥。
(5)最后,选择了2002年和2008年四个季节中晴空无云或云量较少的典型日MODIS数据(8d),对伊洛河流域地表蒸散的时空格局进行了分析。研究结果表明,研究区地表蒸散总体特征为地区分布差异大、年内分配不均。研究区地表蒸散量受植被覆盖度、地表温度、地形条件的影响较大,其时空分布特征也与这些因素的变化相一致。
流域内地表蒸散的空间分布有三个特点：具有明显的地域差异；地表蒸散量受地形影响显著；地表蒸散受供水条件的影响较大。另外,日蒸散量在空间分布上也与地表覆盖类型的分布具有很好的一致性。日蒸散量与NDVI、地表温度呈线性相关,与海拔高度呈对数相关,与坡度、坡向无明显相关性。未考虑地形影响的日蒸散量平均偏大0.17mm/d,在不同坡向上的变化不大,并随坡度的增加呈增加趋势,这与研究区的实际情形不符,因此,在地形复杂的山区,必须要考虑地形因素的影响。
从地表通量及蒸散的年内变化看,季节性差异特征明显,夏季最大,春秋次之,冬季最小,年内分布呈“单峰”变化趋势。2008年的地表净辐射、土壤热通量、日蒸散量均高于2002年,2008年年内日蒸散量差异比2002年大,表明2002年至2008年间,研究区内人类活动的影响加剧,地表蒸散过程逐渐增强,蒸散量逐年增加,年内季节性差异显著。
The variation of regional land surface Evapotranspiration (ET) reflects the trend of energy and water balance in land surface process. Also it further affects distribution of the regional climate and water, and thus has an impact on regional economic development. With the development of remote sensing technology, it becomes more easily to explore the land surface ET of large scale. Therefore, it is a promising theme to carry out a research related to monitoring land surface energy and water balance on regional scale based on the remote sensing technique.
Based on the remote sensing technique, using ETM+and MODIS, two different scales of remote sensing data, and also bringing DEM data, the thesis improves the generally-applied SEBS model and expands its application in the situation where the topography is complicated. At the same time, it explores how these two different data with different scales affect the accuracy of ET. It also builds a multi-scales remote sensing model by using data of ETM+and MODIS to improve the scale effect (mixed pixel) of the MODIS data. The thesis regards the YILUO River basin as a case, and also analyses and explores the temporal and spatial distribution of land surface ET in the year2002and2008, which has more important theoretical and practical value. Specific research work and conclusions of this paper are as follows.
1) ETM+and MODIS can be used to estimate the regional land surface ET better. Especially, the estimate result which uses the ETM+data is more accurate than that uses the MODIS data. Comparison with ground truth data shows that the average relative error is of10%and20%and high-resolution image data can improve ET estimation accuracy to some extent.
2) DEM-based net radiation flux estimation mountain area mode can be used in the net radiation flux estimation of complex terrain area.
Taking into account of the regional sea level elevation, ratio of slope, aspect and the shelter of the topography around in a mountain area model, it corrects the topography affected by the sun radiation from three aspects:firstly. according to the factors such as ratio of slope, aspect, latitude to correct the cosine of slope angle of the sun; Secondly, using the cosine ratio of the slope angle of the sun and solar zenith angle to correct the radiation quantity the slope received; Finally, it introduces a binary function-terrain masking factor to correct the impact of near terrain shadowing. The empirical results in the experimental area show that the net radiation flux estimation result without the consideration of DEM can not reflect the feature of the land surface, so we must take the effect of the topography into consideration.
(3) The improved SEBS model of ET is of higher accuracy, and has a better adaptability and stability in the studied area.
Based on the widely-applied SEBS model and introducing DEM data, it carries out the terrain (elevation, slope, aspect) amendments on the surface parameters and Kinetic parameters such as the solar radiation, atmospheric transmittance, land surface temperatures and land surface roughness, which makes the SEBS model can even adapt to complicated geography condition, and expands the application of the regional surface ET model. The application result of the studied area indicates that the daily ET amount estimated by the improved SEBS model has a smaller error with the measured value, and a higher accuracy. The suitability and stability of the model is better. Making use of ETM+data, the surface ET estimation in the studied area is more accurate than a MODIS data. The SEBS model can better reflect the land cover type and pattern of spatial and temporal distribution of water in the studied area.
(4) This paper analyzes the size error caused by the mixed pixel in ET estimation with MODIS data. It sets up the multi-scale remote sensing model based on the space enhancement method, and raises the accuracy that the model estimates, making it operable for high-frequency dynamic monitoring of regional land surface energy and water. And also it expands the application of MODIS data and TM/ETM+data, and raises the data utilization ratio.
(5) Finally, it selects the MODIS data for typical days of clear sky having no cloud or less cloud during four seasons in2002and2008, and analyzes the temporal and special pattern of land surface ET in YiLuo River. Research result shows that the major characters of the land surface ET in the studied area are the large differences in regional distribution and the uneven distribution of the year. The land surface ET is greatly influenced by vegetation cover, surface temperature and terrain conditions. Temporal and spatial distributions are also consistent with the changes in these factors.
Inside the river valley, the spatial distribution of land surface ET has three characteristics:it has apparent regional differences; the land surface ET is greatly affected by topography; the land surface ET is also under the impact of water supply condition. In addition, the spatial distribution of daily ET is also in accordance with the distribution of different land cover types. The daily ET has a linear correlation with NDVI and surface temperature, has a logarithmic correlation with altitude, and has no significant correlation with slope and aspect. Without considering effects of terrain, the daily ET is a large0.17mm/d on the average, and there is little change in different aspects, increasing with the slope's increasing trend.This is not in accordance with the actual situation in the studied area. Therefore, at mountain area with complicated topography, the terrain factors must be taken into consideration.
From the land surface flux and ET changes in the year, the character of seasonal variation is obvious, largest in summer, followed by the spring and autumn, winter in the end. Distribution during the year has a "single peak" trend. The surface net radiation, soil heat flux and daily ET quantity in2008are all higher than those in2002, and differences of daily ET quantity within the year in2008are bigger than those in2002, which shows from2002to2008, the activities of the human beings have an increasing impact in the studied area. There exists a gradual increase in both land surface ET process and ET quantity by the year, and the seasonal variation inside the year is obvious.
本文以遥感技术为主要手段,分别利用ETM+、MODIS两种不同尺度的遥感数据,并引入DEM数字高程数据,对广泛应用的SEBS蒸散模型加以改进,拓展其在复杂地形条件下的适用范围,同时探讨两种不同尺度的遥感数据对蒸散估算精度的影响,并融合ETM+和MODIS数据构建了多尺度遥感模型,改善了MODIS数据的尺度效应(混合像元)问题。本文以伊洛河流域为研究区域进行了实证研究,并对研究区2002年、2008年的地表蒸散时空分布格局进行了分析和探讨,具有比较重要的理论意义和实践价值。研究的主要结论如下
(1)利用ETM+和MODIS遥感数据可以较好地估算区域地表蒸散量。其中,利用ETM+数据估算的结果比利用MODIS数据估算的结果精度要高,与地面实测数据的对比分析表明,平均相对误差分别在10%和20%左右,高分辨率的影像数据对蒸散估算的精度有一定程度的提高。
(2)基于DEM改进的净辐射通量估算山区模式可以用于地形复杂区域的净辐射通量估算。
山区模式考虑到区域海拔高度、坡度、坡向的不同以及周围地形的遮蔽,从三个方面进行太阳直接辐射的地形修正：首先,依据坡度、坡向、纬度等因子修正坡面太阳入射角的余弦值；其次,利用坡面太阳入射角的余弦值与太阳天顶角的余弦值的比值来修正坡面接收的辐射量；最后,引入一个二值函数——地形遮蔽因子来修正邻近地形的阴影遮蔽带来的影响。在研究区的实证结果表明,未考虑DEM情况下的净辐射通量估算结果不能准确反应研究区的地表特征,存在较大偏差,因此在地形复杂条件下的区域净辐射通量估算必须考虑地形的影响。
(3)改进的SEBS模型估算的蒸散精度较高,模型在研究区的适应性和稳定性较好。
以广泛应用的SEBS模型为基础,引入DEM数据,对坡面上太阳辐射、大气透射率、地表温度、地表粗糙度等地表参数及动力学参数进行了地形(海拔高度、坡度、坡向)修正使得模型更能适应复杂地形条件下的区域地表蒸散估算,拓展了蒸散模型的适用范围。经过在研究区的应用结果表明,改进的SEBS模型估算的日蒸散量和实测值的误差较小,估算精度较高,模型在研究区的适应性和稳定性较好,利用ETM+数据对研究区地表蒸散的估算结果精度要高于MODIS数据,改进的SEBS模型能够反映出研究区的地表覆盖类型和水分分布的时空格局。
(4)分析了MODIS数据在蒸散估算中由混合象元造成的尺度误差,并基于空间增强方法构建了多尺度遥感模型,提高了模型估算的精度,使得进行高频率的区域地表能量与水分的动态监测具有可操作性,也拓展了MODIS和TM/ETM+数据的应用范围,提高了数据的利用率。
多尺度遥感模型结合TM/ETM+的高空间分辨率数据对MODIS精度较高的地表温度数据进行增强,发挥二者的优势,以得到的30m高分辨率LST为输入参数,提高了模型估算的精度。尤其在F垫面破碎度高、土地利用类型复杂、混合像元严重的区域,多尺度遥感模型可以使估算的蒸散量精度得到一定程度的改善甚至显著提高。同时,MODIS的多时相优势也能得到充分发挥。
(5)最后,选择了2002年和2008年四个季节中晴空无云或云量较少的典型日MODIS数据(8d),对伊洛河流域地表蒸散的时空格局进行了分析。研究结果表明,研究区地表蒸散总体特征为地区分布差异大、年内分配不均。研究区地表蒸散量受植被覆盖度、地表温度、地形条件的影响较大,其时空分布特征也与这些因素的变化相一致。
流域内地表蒸散的空间分布有三个特点：具有明显的地域差异；地表蒸散量受地形影响显著；地表蒸散受供水条件的影响较大。另外,日蒸散量在空间分布上也与地表覆盖类型的分布具有很好的一致性。日蒸散量与NDVI、地表温度呈线性相关,与海拔高度呈对数相关,与坡度、坡向无明显相关性。未考虑地形影响的日蒸散量平均偏大0.17mm/d,在不同坡向上的变化不大,并随坡度的增加呈增加趋势,这与研究区的实际情形不符,因此,在地形复杂的山区,必须要考虑地形因素的影响。
从地表通量及蒸散的年内变化看,季节性差异特征明显,夏季最大,春秋次之,冬季最小,年内分布呈“单峰”变化趋势。2008年的地表净辐射、土壤热通量、日蒸散量均高于2002年,2008年年内日蒸散量差异比2002年大,表明2002年至2008年间,研究区内人类活动的影响加剧,地表蒸散过程逐渐增强,蒸散量逐年增加,年内季节性差异显著。
The variation of regional land surface Evapotranspiration (ET) reflects the trend of energy and water balance in land surface process. Also it further affects distribution of the regional climate and water, and thus has an impact on regional economic development. With the development of remote sensing technology, it becomes more easily to explore the land surface ET of large scale. Therefore, it is a promising theme to carry out a research related to monitoring land surface energy and water balance on regional scale based on the remote sensing technique.
Based on the remote sensing technique, using ETM+and MODIS, two different scales of remote sensing data, and also bringing DEM data, the thesis improves the generally-applied SEBS model and expands its application in the situation where the topography is complicated. At the same time, it explores how these two different data with different scales affect the accuracy of ET. It also builds a multi-scales remote sensing model by using data of ETM+and MODIS to improve the scale effect (mixed pixel) of the MODIS data. The thesis regards the YILUO River basin as a case, and also analyses and explores the temporal and spatial distribution of land surface ET in the year2002and2008, which has more important theoretical and practical value. Specific research work and conclusions of this paper are as follows.
1) ETM+and MODIS can be used to estimate the regional land surface ET better. Especially, the estimate result which uses the ETM+data is more accurate than that uses the MODIS data. Comparison with ground truth data shows that the average relative error is of10%and20%and high-resolution image data can improve ET estimation accuracy to some extent.
2) DEM-based net radiation flux estimation mountain area mode can be used in the net radiation flux estimation of complex terrain area.
Taking into account of the regional sea level elevation, ratio of slope, aspect and the shelter of the topography around in a mountain area model, it corrects the topography affected by the sun radiation from three aspects:firstly. according to the factors such as ratio of slope, aspect, latitude to correct the cosine of slope angle of the sun; Secondly, using the cosine ratio of the slope angle of the sun and solar zenith angle to correct the radiation quantity the slope received; Finally, it introduces a binary function-terrain masking factor to correct the impact of near terrain shadowing. The empirical results in the experimental area show that the net radiation flux estimation result without the consideration of DEM can not reflect the feature of the land surface, so we must take the effect of the topography into consideration.
(3) The improved SEBS model of ET is of higher accuracy, and has a better adaptability and stability in the studied area.
Based on the widely-applied SEBS model and introducing DEM data, it carries out the terrain (elevation, slope, aspect) amendments on the surface parameters and Kinetic parameters such as the solar radiation, atmospheric transmittance, land surface temperatures and land surface roughness, which makes the SEBS model can even adapt to complicated geography condition, and expands the application of the regional surface ET model. The application result of the studied area indicates that the daily ET amount estimated by the improved SEBS model has a smaller error with the measured value, and a higher accuracy. The suitability and stability of the model is better. Making use of ETM+data, the surface ET estimation in the studied area is more accurate than a MODIS data. The SEBS model can better reflect the land cover type and pattern of spatial and temporal distribution of water in the studied area.
(4) This paper analyzes the size error caused by the mixed pixel in ET estimation with MODIS data. It sets up the multi-scale remote sensing model based on the space enhancement method, and raises the accuracy that the model estimates, making it operable for high-frequency dynamic monitoring of regional land surface energy and water. And also it expands the application of MODIS data and TM/ETM+data, and raises the data utilization ratio.
(5) Finally, it selects the MODIS data for typical days of clear sky having no cloud or less cloud during four seasons in2002and2008, and analyzes the temporal and special pattern of land surface ET in YiLuo River. Research result shows that the major characters of the land surface ET in the studied area are the large differences in regional distribution and the uneven distribution of the year. The land surface ET is greatly influenced by vegetation cover, surface temperature and terrain conditions. Temporal and spatial distributions are also consistent with the changes in these factors.
Inside the river valley, the spatial distribution of land surface ET has three characteristics:it has apparent regional differences; the land surface ET is greatly affected by topography; the land surface ET is also under the impact of water supply condition. In addition, the spatial distribution of daily ET is also in accordance with the distribution of different land cover types. The daily ET has a linear correlation with NDVI and surface temperature, has a logarithmic correlation with altitude, and has no significant correlation with slope and aspect. Without considering effects of terrain, the daily ET is a large0.17mm/d on the average, and there is little change in different aspects, increasing with the slope's increasing trend.This is not in accordance with the actual situation in the studied area. Therefore, at mountain area with complicated topography, the terrain factors must be taken into consideration.
From the land surface flux and ET changes in the year, the character of seasonal variation is obvious, largest in summer, followed by the spring and autumn, winter in the end. Distribution during the year has a "single peak" trend. The surface net radiation, soil heat flux and daily ET quantity in2008are all higher than those in2002, and differences of daily ET quantity within the year in2008are bigger than those in2002, which shows from2002to2008, the activities of the human beings have an increasing impact in the studied area. There exists a gradual increase in both land surface ET process and ET quantity by the year, and the seasonal variation inside the year is obvious.
引文
[1]Allen R G, Pereira L S, Raes D et al. Crop evapotranspiration-guidelines for computing crop water requirements [M]. FAO Irrigation and Drainage Paper 56. FAO,1998.
[2]Allen R G,Tasumi M,Trezza R,et al. Surface Energy Balance Algorithm for Land (SEBAL)-Advanced training and Users Manual [M]. Publisher:Idaho Department of Water Resources, University of Idaho,2002.
[3]Allen R G,Tasumi M,Morse A,et al. A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning [J]. Irrigation and Drainage Systems,2005,19(3):251-268.
[4]Allen R G. The ASCE Standardized Reference Evapotranspiration Equation [M]. American Society of Civil Engineers,2005.
[5]Allen R G,Trezza R,Tasumi M. Analytical integrated functions for daily solar radiation on slopes [J]. Agricultural and Forest Meteorology,2006,139(1-2):55-73.
[6]Ambast S k,Keshari A K,Gosain A K.An operational model for estimating Regional Evapotranspiratinn through Surface Energy Partitioning(RESEP) [J].International Journal of Remote Sensing,2002,23(22):4917-4930.
[7]Anderson M C, Norman J M, Diak G R, et al. A two source time-integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing [J]. Remote Sensing of Environment,1997,60(2):195-216.
[8]Anderson M C, Norman J M, Mechikalski J R, et al. A multiscale remote sensing model for disaggregating regional fluxes to micrometeorological scales [J]. Journal of hydronmeteorology,2004,5(2):343-363.
[9]Ballantine J A, Okin G S, Prentiss D E, et al. Mapping north a frican landforms using continental scale unmixing of MOD1S imagery[J]. Remote sensing of environment,2005, 97(4):470-483.
[10]Barducci A, Pippi I. Temperature and Emissivity Retrieval from Remotely Sensed Images Using the "Grey Body Emissivity" Method [J]. IEEE Transactions on Geosciences and Remote Sensine,1996,34:681-695.
[11]Bastiaanssen W G M, Bandara K M P S. Evaporative Depletion Assessments for Irrigated Watersheds in Sri Lanka [J].Irrig.Sci,2001(21):1-15.
[12]Bastiaanssen W G M, Menenti M, Feddes R A, et al. A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL)-1. Formulation [J]. Journal of Hydrology,1998, (212/213):198-212.
[13]Bastiaanssen W G M, Pelgrum H, Wang J, et al. A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL)-2. Validation [J]. Journal of Hydrology,1998,213(1/4): 213-229.
[14]Bastiaanssen W G M, Molden D J, Makin I W. Remote Sensing for Irrigated Agriculture: Examples from Research and Possible Applications [J]. Agricultural Water Management, 2000,46(2),137-155.
[15]Bastiaanssen W G M. SEBAL-based Sensible and Latent Heat Fluxes in the Irrigated Gediz Basin Turkey [J]. Journal of Hydrology,2000,229(1/2):87-100.
[16]Bastiaanssen W G M, Noordman E J M, Pelgrum H D, et al. SEBAL Model with Remotely Sensed Data to Improve Water-resources Management under Actual Field Conditions [J]. ASCE Journal of Irrigation and Drainage Engineering,2005,131(1):85-93.
[17]Batra N S, Islam V, Venturini G, et al. Estimation and Comparison of Evapotranspiration from MODIS and AVHRR Sensors for Clear Sky Days over the Southern Great Plains [J]. Remote Sensing of Environment,2006.103(1):1-15.
[18]Beljaars A C M, Holtslag A A M. Flux parameterization over land surfaces for atmospheric models [J], J. Appl. Meteorol.,1991,30,327-341.
[19]Berk A,Bernstein L S,Anderson G P,et al. MODTRAN Cloud and Multiple Scattering Upgrades with Application to AVIRIS-editions of 1991 and 1992[J]. Remote Sensing of Environment.1998.65(3):367-375.
[20]Blyth E M, Harding R J. Application of Aggregation Model to Surface Heat Flux from the Sahelian Tiger Bush[J]. Agricultural and Forest Meteorology,1995,72(3/4):213-215
[21]Bowen I S. The Ratio of Heat Losses by Conduction and by Evaporation from any Water Surface [J]. Physical Review,1926,27(6):779-787.
[22]Brutsaert W. Evaporation into the Atmosphere:Theory, History and Application [M]. TM Dordrecht:D. Reidel Publishing Company,1982.
[23]Burba G G,Verma S B,Kim J. Surface energy fluxes of Phragmites australis in a prairie wetland [J]. Agricultural and Forest Meteorology,1999,94(1):31-51.
[24]Burman R, Pochop L. Evaporation, Evapotranspiration and Climate Data [M]. The Netherlands:Elsevier Science,1994.
[25]Carlson T N, Perry E M, Schmugge T J. Remote Estimation of Soil Moisture Availability and Fractional Vegetation Cover for Agriculture Fields [J]. Agricultural and Forest Meteorology, 1990,52(1):45-69.
[26]Carlson T N, Capehart W J, Gillies R R. A New Look at the Simplified Method for Remote Sensing of Daily Evapotranspiration [J]. Remote Sensing of Environment,1995, 54(2):161-167.
[27]Carlson T N, Ripley D A J. On the Relationship between Fractional Vegetation Cover, Leaf Area Index, and NDVI [J]. Remote Sensing of Environment.1997,62(3):241-252.
[28]Chander G, Markham B. Revised Landsat-5 TM Radiometric Calibration Procedures and Post calibration Dynamic Ranges [J]. IEEE Transactions on Geosciences and Remote Sensing, 2003 (41):2674-2677.
[29]Cleugh H A, Leuning R, Mu Q Z, et al. Regional Evaporation Estimates from Flux Tower and MOD1S Satellite Data[J]. Remote Sensing of Environment,2007,106(3):285-304.
[30]Conrad C, Dech S W, Hafeez M, et al. mapping and assessing water use in a central Asian irrigation system by utilizing MODIS remote sensing products [J]. Irrigation and Draninage systems,2007,21(3/4):197-218.
[31]Cothier B E, Clawson K L, Printer P J, et al. Estimation of Soil Heat Flux from Net Radiation during the Growth of Alfalfa [J]. Agricultural and Forest Meteorology,1986,37,312-329.
[32]Delecollet R, Mass S J, Guerif M, et al. Remote Sensing and Crop Production Models: Present Trends[J]. ISPRS Journal of Photogrammetric and Remote Sensing,1992, 47(2/3):145-161.
[33]Dozier J, Frew J. Rapid calculation of terrain parameters for radiation modeling from digital elevation data [J]. IEEE Transaction on Geoscience and Remote Sensing,1990,28(5): 963-969.
[34]Dubayah R. Estimation net solar radiation using Landsat Thematic Mapper and digital elevation data [J]. Water Resource Research,1992,28:2469-2484.
[35]Gan Y N,Zhang W C.A simple empirical topographic correction method for ETM+imagery[J].International Journal of Remote Sensing,2009,30(9):2259-2275.
[36]Gillespie A, Rokugawa S, Matsunaga T. A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images [J]. IEEE Transactions on Geoscience and Remote Sensing,1998,36:311-326.
[37]Gillies R R, Carlson T N. Kustas W P, et al. A Verification of the "triangle method" for Obtaining Surface Soil Water Content and Energy Fluxes from Remote Measurements of the Normalized Difference Vegetation index(NDVI) and Surface Radiant Temperature [J]. Int. J. Remote Sens.,1997,18(15),3145-3165.
[38]Gomez M, Olioso A. Sobrino J A, et al. Retrieval of Evapotranspiration over the Alpilles/ReSeDA Experimental Site Using Airborne POLDER Sensor and a Thermal Camera[J]. Remote Sensing of Environment,2005,96(3/4):399-408.
[39]Griend A A, Owe M. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces [J]. International Journal of Remote Sensing. 1993,14(6):1119-1131.
[40]Gutman,ignatov A.The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models[J].International Journal of Remote sensing,1998,19(8):1533-1543.
[41]Hatfield J L,et al.Evaluation of canopy temperature evapotranspiration models over various surfaces [J].Agric Forest Meteor.1984,32:41-53.
[42]Idso S B. Reginato R J, Jackson R D. An Equation for Potential Evaporation from Soil, Water and Crop Surfaces Adaptable to Use by Remote Sensing [J]. Geophysical Research Letters.1977(4),187-188.
[43]Iqbal M. An Introduction to Solar Radiation [M]. Ontario:Academic Press Canada,1983.
[44]Isaacs R G.Wang W C.Worsham R D,et al. Multiple scattering LOWTRAN and FASCODE models [J]. Applied Optics.1987.26(7):1272-1281.
[45]Jackson R D, Reginato R J, Idso S B. Wheat Canopy Temperature:a Practical Tool for Evaluating Water Requirements [J]. Water Resource Research,1977,13(3):651-656.
[46]Jackson R D,et al.Plant canopy information extraction from composition scene reflectance of row crops[J].Appl Opt,1979,18(22):1892-1905.
[47]Jia L,Su Z,Hurk B,et al.Estimation of sensible heat flux using the Surface Energy Balance System(SEBS)and ATSR measurements[J].Physics and Chemistry of the Earth,2003,28:75-88.
[48]Jiang L, Islam S. An Intercomparison of Regional Latent Heat Flux Estimation Using Remote Sensing Data [J]. International Journal of Remote Sensing,2003,24(11):2221-2236.
[49]Kalluri S N V, Townshend J R G, Doraiswamy P A. Simple Single Layer Model to Estimate Transpiration from Vegetation Using Multispectral and Meteorological Data [J]. Int. J Remote Sensing,1998(19):1037-1053.
[50]Kaufman Y J,Karniell A,Tanre D,et al. Detection of dust over deserts using satellite data in the solarwavelengths [J]. Geoscience and Remote Sensing,IEEE Transactions on,2000,38(1):525-531.
[51]Kerenyi J, Putsay M. Investigation of Land Surface Temperature Algorithms Using NOAA ANVHRR Images [J]. Adv. Space Res,2000,26(17):1077-1080.
[52]Kjaersgaard J H, Plauborg F L, Hansen S. Comparison of Models for Calculating Daytime Long-wave Irradiance using Long Term Dataset[J]. Agricultural and Forest Meteorology 143. 2007,49-63.
[53]Kornfield J, Susskind J. On the Effect of Surface Emissivity on Temperature Retrievals [J]. Monthly weather Review,1977,105:1605-1608.
[54]Kustas W P, Choudhury B J, Moran M S, et al. Determination of Sensible Heat Flux Sparse Canopy Using Thermal Infrared Data [J]. Agricultural and Forest Meteorology,1989,44(3/4): 197-216.
[55]Kustas W P, Daughtry C S T. Estimation of the Soil Heat Flux/net Radiation Ratio from Spectral Data [J], Agric. For. Meteorology,1989,49,205-223.
[56]Kustas W P. Estimates of evapotranspiration within a one-and two-layer model of heat transfer over partial vegetation cover [J]. Journal of Applied Meteorology,1990,29:704-715.
[57]Kustas W P, Norman J M. Evaluation of Soil and Vegetation Heat Flux Predictions using A Simple Two Source Model with Radiometric Temperatures for Partial Canopy Cover[J]. Agricultural and Forest Meteorology.1999(94):13-29.
[58]Kustas W P, Norman J M, Anderson M C, et al. Estimating subpixel surface temperatures and energy fulxes from the vegetation index-radiometric temperature relationship [J]. Remote sensing of environment,2003,85(4):429-440.
[59]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.
[60]Lhomme J P,Monteny B,Amadou M. Estimating sensible heat flux from radiometric temperature over sparse miller[J].Agricultural and Forest Meteorlgy,1994,68:77-91.
[61]Lhomme J P, Troufleau D, Monteny B, et al. Sensible Heat Flux and Radiometric Surface Temperature over Sparse Sahelian Vegetation. A model for the kB-1 Parameter [J]. Journal of Hydro logy,1997(188-189):839-854.
[62]Lhomme J P, Chehbouni A. Comments on Dual-source Vegetation Atmosphere-transfer Models [J]. Agricultural and Forest Meteorology,1999,94(3/4):269-273.
[63]Li Jia, Zhongbo Su, Bart van dan Hurk, et al. Estimation of sensible heat flux using the surface energy balance system (SEBS) and ATSR measurements [J]. Physics and Chemistry of the Earthe,2003,28:75-88.
[64]Li X, Cheng G D, Chen X Z, et al. Modification of solar radiation model over rugged terrain [J]. Chinese Science Bulletin,1999,44(15):1345-1350.
[65]Liang S L. Narrowband to broadband conversions of land surface albedo. I-Algorithms [J]. Remote Sensing of Environment,2001,76(2):213-238.
[66]Liang S L,Fang H,Morisette J T,et al. Atmospheric correction of Landsat ETM+land surface imagery.H. Validation and applications [J]. Geoscience and Remote sensing, IEEE Transactions on,2002,40(12):2736-2746.
[67]Liang S L,Shuey C J.Russ A L,et al. Narrow band to broadband Conversions of land surface albedo. Ⅱ-Validation [J]. Remote Sensing of Environment,2003,84(1):25-41.
[68]Lin Wenjing, Su Zhongbo, Dong Hua, et al. Regional evapotranspiration estimation based on MOIDS products and Surface Energy Balance System (SEBS) in Hebei Plain,Northeastern China[J]. Journal of Remote Sensing,2008,12(4):663-672.
[69]Liou K.N.大气辐射导论(第2版)[M].北京：气象出版社,2004
[70]Malliek K, Bhattacharya B K, Chaurasia S, et al. Evapotranspiration using MODIS data and limited ground observations over selected agroeco systems in India [J]. International Journal of Remote Sensing,2007,28(10):2091-2110.
[71]Massman W J. An analytical one-dimensional model of momentum transfer by vegeration of arbitrary structure [J]. Boundary-Layer Meteor,1997,83:407-421.
[72]Massman W J. A model study of kB-1 for vegetated surfaces using 'localized near-field' Lagrangian theory [J]. J Hydrol,1999,223:27-43.
[73]Matson M E P, Mcclain D F. McGinnis Satellite Detection of Urban Heat Islands [J]. Monthly Weather Review.1978,106(12):1725-1734.
[74]McMillin L M. Estimation of Sea Surface Temperature from Two Infrared Window Measurements with Different Absorption [J]. Journal Geophysical Research.1975, 20:5113-5117.
[75]Mecikalski J R, Diak G R, Anderson M C, et al. Estimating Fluxes on Continental Scales using Remotely Sensed Data in An Atmospheric Land Exchange Model [J]. Journal of Applied Meteorology,1999(38):1352-1369.
[76]Menenti M, Choudhury B. Parameterization of Land Surface Evaporation by Means of Location Dependent Potential Evaporation and Surface Temperature Range [C]//Proceedings of IAHS conference on Land Surface Processes,1993,212:561-568.
[77]Monteith J L. Principles of Environmental Physics [D]. London:Edward Aronld,1973.
[78]Moran M S, Clarke T R, Inoue Y, et al. Estimating Crop Water Deficit Using the Relationship between Surface-air Temperature and Spectral Vegetation Index[J]. Remote Sensing of Environment,1994,49(3):246-263.
[79]Nishida K, Nemani R R, Running S W, et al. Remote Sensing of Land Surface Evaporation(1) Theoretical Basis for an Operational Algorithm [EB/OL]. [2011-05-20]. Http://secure.ntsg.umt.edu/projects/files/documents/mod16/SER-ver9-JGR.DOC
[80]Norman J M, Kustas W P, Humes K S. A Two-source Approach for Estimating Soil and Vegetation Energy Fluxes from Observation of Directional Radiometric Surface Temperature [J]. Agricultural and Forest Meteorology,1995(77):263-293.
[81]Oke T R, Johnson G T, Steyn D G, et al. Simulation of Surface Urban Heat Islands under 'ideal' Conditions at Night Part 2 :Diagnosis of Causation [J]. Boundary-Layer Meteorology, 1991,56:339.
[82]Olioso A, Chauki H, Courault D, et al. Estimating of Evapotranspiration and Photosynthesis by Assimilation of Remote Sensing Data into SVAT Models [J]. Remote Sensing of Environment,1999,68(3):341-356.
[83]Penman H L. Natural Evaporation from Open Water, Bare Soil and Grass[C]. Proceedings of the Royal Society of London, Series A, Mathematical and Physical Sciences,1948:120-145.
[84]Prata A J, Caselles V, et al. Thermal Remote Sensing of Land Surface Temperature from Satellites:Current Status and Future Prospects [J]. Remote Sensing Review,1995,12, 175-224.
[85]Prata A J. A New Long-wave Formula for Estimating Downward Clear-sky Radiation at the Surface [J]. Quarterly Journal of the Royal Meteorological Society.1996,122,1127-1151.
[86]Prater M R, Delucia E H. Non-native Grasses Alter Evapotranspiration and Energy Balance in Great Basin Sagebrush Communities [J]. Agricultural and Forest Meteorology,2006, 139(1/2):154-163.
[87]Pricec J C. Land Surface Temperature Measurements from Split window Channel of the NOAA 7 Advance Very High Resolution Radiometer [J]. Geophysical Research.1984, 98:7231-7237.
[88]Qin Z, Berliner P, Karnieli A. Numerical Solution of A Complete Surface Energy Balance Model for Simulation of Heat Fluxes and Surface Temperature under Bare Soil Environment [J]. Applied Mathematics and Computation,2002 (130):171-200.
[89]Ranzi R, Rosso R. Distributed estimation of incoming direct solar radiation over a drainage basin [J]. Journal of Hydrology,1995,166:461-478.
[90]Roerink G J, Su Z, Menenti M. A Simple Remote Sensing Algorithm to Estimate the Surface Energy Balance [J]. Physics and Chemistry of the Earth (B),2000,25(2):147-157.
[91]Rouse J W, Haas R H, Schell J A, et al. Monitoring Vegetation Systems in the Great Plains with ERTS [J]. Third ERTS Symposium, NASA SP-351,1973 (1):309-317.
[92]Sanchez J M, Caselles V, Niclos R, et al. Evaluation of the B-method for Determining Actual Evapotranspiration in a Boreal Forest from MODIS Data [J]. International Journal of Remote Sensing,2007,28(6):1231-1250.
[93]Sanchez J M, Scavone G, Caselles V, Valor E, et al. Monitoring Daily Evapotranspiration at A Regional Scale from Landsat TM and ETM+ Data:Application to the Basilicata Region [J]. Journal of Hydrology,2008,351,58-70.
[94]Schmugge T J, Kustas W P, Ritchie J C, et al. Remote Sensing in Hydrology [J]. Advances in Water Resources,2002,25(8/12):1367-1385.
[95]Seguin B, Itier B. Using Midday Surface Temperature to Estimate Daily Evaporation from Satellite Thermal IR Data [J]. International Journal of Remote sensing,1983,4(2):371-383.
[96]Sells P J, Hall E G, Asrar G, et al. The First ISLSCP Field Experiment (FIFE). J. Bull. Amer. Meteor, Soc.,1998,69 (1):22-27.
[97]Shuttleworth W J, Gurney, R J. The Theoretical Relationship between Foliage Temperature and Canopy Resistance in Sparse Crop [J]. Quarterly Journal of Royal Meteorological Society, 1990,116(492):497-519.
[98]Sobrino J A, Caselles V. A Methodology for Obtaining the Crop Temperature from NOAA-9 AVHRR Data [J]. International Journal of Remote Sensing,1991,12,461-475.
[99]Sobrino J A, Jimenez-Munoza J C, Paolini L. Land surface temperature retrieval from Landsat TM5 [J]. Remote Sens Environ.2003,75:256-266.
[100]Sobrino J A, Gomez M, Jimenez-Munoz J, et al. A Simple Algorithm to Estimate Evapotranspiration from DAIS Data:Application to the DA1SEX campaigns [J]. Journal of Hydrology,2005,315(1/4):117-125.
[101]Stull B R,赵长新(译).边界层气象学导论[M].北京：气象出版社，1991.156-316.
[102]Su Z. Remote sensing of land use and vegetation for mesoscale hydrological studies [J].Remote sensing.2000,21:213-233.
[103]Su Z. A Surface Energy Balance System(SEBS) for estimation of turbulent heat fluxes from point to continental scale[M].//Su Z, Jacobs J, eds. Advanced Earth Observation Land Surface Climate. Publications of the National Remote Sensing Board(BCRS). USP-2.01-02, 2001:91-108.
[104]Su Z. The Surface Energy Balance System (SEBS) for Estimation of Turbulent Heat Fluxes [J]. Hydrology and Earth System Sciences,2002,6(1):85-99.
[105]Su Z,Wen J,Li W. A methodology for the retrieval of land physical parameter and actual evaporation using NOAA/AVHRR Data [J]. Journal of Jilin University(Science),2003,33:106-118.
[106]Su Z. Estimation of the Surface Engery Balance.[A] Anderson M G. Encyclopedia of Hydrological Science. [M] John Wiley & Sons, Ltd,2005:1-22.
[107]Sun X M, Zhu Z L, Tang X Z, et al. The Impact of Local Advection on the Accuracy of Surface Fluxes based on Remote Sensing [J]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2002 (V4):2323-2325.
[108]Tasumi M, Allen R G.Trezza R,et al. Satellite-Based Energy Balance to Assess Within-Population Variance of Crop Coefficient Curves [J]. Journal of Irrigation and Drainage Engineering ASCE,2005,131(1):94-109.
[109]Tasumi M, Allen R G. Satellite-based ET Mapping to Assess Variation in ET with Timing of Crop Development [J]. Agricultural Water Management,2007,88(1/3):54-62.
[110]Thornthwaite C W, Holzman A. Report of the Commutation on Transpiration and Evaporation [J]. Transactions of the American Geophysical Union,1944,25(1):683-693.
[111]Timmermans W J,vailder Kwast,Gieske A S M, et al. Intercomparison of energy flux models using ASTER imagery at the SPARC 2004 site(Barrax,Spain).Presented at the ESA proceedings WPP-250:SPARC final workshop,4-5,July,2005.Enschede,ESA,2005.8 P.
[112]Van den Hurk, Holtslag. On the bulk parameterization of surface fluxes for various conditions and parameter ranges [J]. Boundary-Layer Meteorol.1995,82,199-234.
[113]Vermote E F,Tanre D,Deuze J L,et al. Second Simulation of the Satellite Signal in the Solar Spectrum,6S:An overview [J]. Geoscience and Remote Sensing,IEEE Transactions on,1997,35(3):675-686.
[114]Voogt J A, Oke T R. Thermal Remote Sensing of Urban Climates [J]. Remote Sensing of Environment,2003,86:370-384.
[115]Wang J, White K, Robinson G J. Estimating surface net solar radiation by use of Landsat-5 TM and digital elevation models [J]. International Journal of Remote Sensing,2000, 21(1):31-43.
[116]Wang K, Li Z, Cribb M. Estimation of Evaporative Fraction from A Combination of Day and Night Land Surface Temperature and NDV1:A New Method to Determine the Priestley Taylor Parameter [J]. Remote Sensing of Environment,2006,102:293-305.
[117]Weng Q H, Lu D S, Jacquelyn S. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 2004,89:467-483
[118]Weng Q. A Remote Sensing-GIS Evaluation of Urban Expansion and Its Impact on Surface Temperature in Zhujiang Delta, China [J]. International Journal of Remote Sensing,2001,22 (10):1999
[119]Willmott C J. Some comments on the evaluation of model performance [J]. Bulletin of the American Meteorological Society,1982,11:1309-1313.
[120]Zhan X, Kustas W P, Humes K S. An Intercomparison Study on Model of Sensible Heat Flux over Partial Canopy Surfaces with Remotely Sensed Surface Temperature [J]. Remote Sensing of Evironment,1996,58(3):242-256.
[121]Zhang L, Lemeur R, Goutorbe J P. A one-layer Resistance Model for Estimating Regional Evapotranspiration Using Remote Sensing Data [J]. Agricultural and Forest Meteorology, 1995,77(3/4):241-261.
[122]Zhang W C, Zhang Y S. Observation and Estimation of Daily Actual Evapotranspiration and Evaporation on a Glacier zed Watershed at the Headwater of the Urumqi River, Tianshan, China [J]. Hydrological Processes,1999,13 (11):1589-1601.
[123]陈镜明.现用遥感蒸散模式中的一个重要缺点及改进[J].科学通报.1988(6)：454-457.
[124]陈鸣,潘之棣.用卫星遥感红外数据估算大面积蒸散量[J].水科学进展,1994,5(2)：126-133.
[125]陈志明,李家国,余涛.TM遥感影像的地形辐射校正研究[J].遥感信息,2009,2：56-59.
[126]邓玉娇,刘诚,林锦祥.基于MODIS资料研究广州市热环境[J].热岛气象学报.2007.23(3)
[127]丁圣彦,梁国付.地理环境因素对伊洛河流域森林景观的影响[J].地理研究,2007,26(5)：906-914.
[128]杜非，伊洛河流域土地覆被变化水文效应研究[D].河海大学,2007.
[129]甘甫平,陈伟涛,张绪教,等.热红外遥感反演陆地表面温度研究进展[J].国土资源遥感,2006,1(67)：6-11.
[130]高彦春,龙笛.遥感蒸散发模刑研究进展[J].遥感学报,2008.12(3)：515-528.
[131]高水年.高俊峰,张万昌,等.地形效应下的区域蒸散遥感估算[J].农业工程科学 报,2010,26(10)：218-223.
[132]郭建民,郑金亮.伊洛河志[M].北京：中国科学技术出版社,1995.
[133]河南省洛阳市农牧局、洛阳市十壤普查办公室编.洛阳土壤[M].北京：海洋出版社,1991.
[134]何洪林,于贵瑞.复杂地形条件下的太阳资源辐射计算方法研究[J].资源科学,2003,25(1)：78-85.
[135]何玲,莫兴国,汪志农.基于MODIS遥感数据计算无定河流域日蒸散[J].农业工程学报,2007,23(5)：114-]50
[136]何延波,王石立.遥感数据支持下不同地表覆盖的区域蒸散[J].应用生态学报,2007,18(2)：289—295.
[137]何延波, Su Zhongbo, LI Jia,等SEBS模型在黄淮海地区地表能量通量估算中的应用[J].高原气象,2006,25(6)：1092-1100.
[138]贺瑞敏,王国庆.张建云.环境变化对黄河中游伊洛河流域径流量的影响[J].水十保持研究,2007,14(2)：297-301.
[139]郝芳华,陈利群,刘昌明.土地利用对产流和产沙的影响分析[J].水土保持学报,2004,18(3)：5-8.
[140]洪刚,李万彪.卫星遥感估算淮河流域区域能量通量的方法研究.北京大学学报：自然科学版,2001,37(5)：693-700.
[141]黄妙芬.地表通量研究进展[J].干旱区地理,2003(26)：159-165.
[142]黄妙芬,邢旭峰,刘素红,等.反演地表温度三要素获取途径研究及其研究价值[J].干旱区地理,2005，28(4)：541-547.
[143]李武彦,乔智克.杨金榜.等.嵩县水土保持生态环境建设成效与经验[J].河南水利与南水北调,2008,10：25-26.
[144]李小建,刘钢军.钱乐祥,等.中尺度流域土地利用/土地覆盖变化评估——以伊洛河中部地区为例[J].地理科学,2001,21(4)：289-296.
[145]李新,马明国,王建,等.黑河流域遥感—地面观测同步试验科学目标与试验方案[J].地球科学进展,2008,23(9)：897-914.
[146]李玉环,王静,吕春燕,等.基于TM ETM遥感数据的地面相对反射率反演[J].中国农业大 学学报(自然科学版),2005,36(4)：545-551.
[147]李古清,翁笃鸣.一个计算山地日照时间的计算机模式[J].科学通报,1987,32(17)：1333-1335.
[148]李古清,翁笃鸣.丘陵山区地面辐射场的数据模拟[J].南京气象学报,1990,13(2)：184-193.
[149]李发鹏,徐宗学,李景玉.基于MODIS数据的黄河三角洲区域蒸散发量时空分布特征[J].农业工程学报,2009,25(2)：113-120.
[150]刘朝顺,施润和,高炜,等.利用区域遥感ET分析山东省地表水分盈亏的研究[J].自然资源学报,2010,25(11)：1938-1948.
[151]梁国付,丁圣彦.森林景观变化的地理环境因素整合分析[J].生态学报,2010,30(6)：121-132.
[152]蔺文静,董华,陈立,等.基于SEBAL模型的区域蒸发蒸腾遥感估算[J].遥感信息,2008,16(5)：112-1]4.
[153]刘雅妮,辛晓洲,柳钦火,等.基于多尺度遥感数据估算地表通量的方法及其验证分析[J].地球科学进展,2010,25(11)：1261-1272.
[154]柳饮火,辛晓洲,唐娉,等.定量遥感模型、应用及不确定性研究[M].北京：科学出版社,2010.
[]55]卢俐,刘绍民,孙敏章,等.大孔径闪烁仪研究区域地表通量的进展[J].地球科学进展,2005,20(9)：932-938.
[156]卢娜,万力.基于RS的鄂尔多斯北部盆地地表蒸发量的计算[J].地质通报,2008,27(8)：1165-]167.
[157]卢氏县水利志编篡领导小组.卢氏县水利志[M].西安：陕西人民出版社,2001
[158]鹿洁忠.苏联对水面蒸发测定和计算方法的研究[J].中国农业气象,1991,12(8)：54-56.
[159]马耀明,戴有学,马伟强,等,干旱半干旱区非均匀地表区域能量通量的卫星遥感参数化[J].高原气象,2004,23(2)：139-146.
[160]毛克彪,覃志豪,施建成,等.针对MODIS影像的劈裂窗算法研究[J].武汉大学学报：信息科学版.2005,30(8).
[161]苗长虹等.伊洛河流域土地利用和土地覆被变化与可持续发展研究[M],中国环境出版 社,2006.
[162]潘志强,刘高焕.黄河三角洲蒸散的遥感研究[J].地球信息科学,2003(3)：91-96.
[]63]钱学伟,李秀珍.陆面蒸发计算方法述评[J].水文,1996,6：49-53.
[164]乔平林,张继贤,王翠华.石羊河流域蒸散发遥感反演方法[J].干旱区资源与环境,2007,21(4)：107-110.
[165]盛裴轩,毛节泰,李建国.大气物理学[M]：北京大学出版社,2003.
[166]孙睿,刘昌明.地表水热通量研究进展[J].应用生态学报,2003(14)：434-438.
[167]孙菽芬.陆面过程的物理、生化机理和参数化模型[M].北京：气象出版社,2005.
[]68]覃志豪,Zhang Minghua, Karrieli A用NOAA-AVHRR热通道数据算法地表温度的辟窗算法[J].国十资源遥感,2001,(2)：33-42.
[169]覃志豪,张明华,Amon Kamieli,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4)：456-466.
[170]覃志豪,李文娟,徐斌.等.陆地卫星TM6波段范围内地表比辐射率的估计[J].国土资源遥感,2004,61(3)：28-41.
[171]田国良.黄河流域典型地区遥感动态研究[C].北京：科学出版社,1989.
[172]田景环,崔庆,徐建华.等.黄河流域大中型水库水面蒸发对水资源量的影响[J].山东农业大学学报.自然科学版,2005,36(3)：391-394.
[173]田庆久,郑兰芬.基于遥感影像的大气辐射校正和反射率反演方法[J].应用气象学报,1998,9(4)：456-461.
[174]王兵,臧玲.伊洛河流域开发战略研究[J].地域研究与开发,2007,26(6)：63-56,74.
[175]王冬妮,李国春,张红玲,等.用MODIS数据反演辽西北地区陆面温度[J].农业网络信息.2006.1
[176]王红振,明亮,晋建设.等.近50年黄河中游三花区间降水特征分析[J].气象与环境科学,2009,32(2)：78-82.
[177]王记芳,程炳岩，朱业玉,等.黄河中游三花问区气候变化特征[J].气象,2009,35(3)：99-106.
[]78]王开存,刘晶淼,周秀骥,等.利用MODIS卫星资料反演中国地区晴空地表短波反照率及其特征分析[J],大气科学,2004,28(6)：941-949.
[179]王黎明.吉林省西部区域遥感蒸散模型研究及其应用[D].吉林：吉林大学,2005.
[180]王梅,于建波,那景坤.E-601型蒸发器水面蒸发实验分析[J].黑龙江水专学报,2004,31(3)：1-12.
[181]王远明,张彼,李成荣.宜昌站水面蒸发折算系数分析[J].人民长江,1999,30(1)：41-45.
[182]吴致尧,任德存.黄河流域综述[M].郑州：河南人民出版社,1998
[183]吴炳方,熊隽,闫娜娜ETWatch的模型与方法[J].遥感学报,2011,15(2)：224-239.
[184]吴炳方,邵建华.遥感估算蒸腾蒸发量的时空尺度推演方法及应用[J].水利学报,2006,37(3)：286-292.
[185]谢贤群.遥感瞬时作物表面温度估算农田全日蒸发散总量[J].环境遥感,1991,6(4)：253-259.
[186]辛晓洲,田国良,柳钦火.地表蒸散定量遥感的研究进展[J].遥感学报,2003,7(3)：233-240
[187]辛晓洲.定量遥感方法计算地表烝散[D].中国科学院研究生院博十论文,2003.6.
[188]徐同仁,刘绍民,秦军等.同化MODIS温度产品估算地表水热通量[J].遥感学报,2009,13(6)：999-1009..
[189]徐希孺,柳钦火,陈家宜.遥感陆面温度[J].北京大学学报(自然科学版),1998,34(2-3)：248-253.
[190]杨虎,杨忠东.中国陆地区域陆表温度业务化遥感反演算法及产品运行系统[J].遥感学报,2006,10(4)：600-607.
[191]杨青生,刘闯MODIS数据陆面温度反演研究[J].遥感技术与应用.2004.19(2)：90-94
[192]曾燕,邱新法,潘敖大等.地形对黄河流域太阳辐射影响的分析研究[J].地球科学进展,2008,23(11)：1185-1193.
[193]詹志明,冯兆东,秦其明.陇西黄土高原陆面蒸散的遥感研究[J].地理与地理信息科学,2004,20(1)：16—19.
[194]张长春,王晓燕,邵景力.利用NOAA数据估算黄河三角洲区域蒸散量[J].资源科 学,2005,27(1)：86-91.
[195]张东，张万吕,徐全芝.汉江上游流域蒸散量计算方法的比较和改进[J].资源科学,2005,27(1)：97-103.
[196]张仁华.实验遥感模型及地面基础[M].北京：科学出版社,1996.
[197]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,2002,32(12)：1041-1051.
[198]张仁华,孙晓敏,王伟民,等.一种可操作的区域尺度地表通量定量遥感二层模型的物理基础[J].中国科学D辑,2004,34(增刊11)：200-216.
[199]张仁华.定量热红外遥感模型及地面实验基础[M].北京：科学出版社,2009：335,344,494.
[200]张文江,陆其峰，高志强.区域地表水热通量遥感的空间不均匀性分析[J].自然科学进展.2007.17(10)：1383-1390.
[201]张晓涛,康绍忠.王鹏新,等.估算区域蒸发蒸腾量的遥感模型对比分析[J].农业工程学报.2006(22)：6-13.
[202]赵军.刘春雨,潘竞虎,等.基于MODIS数据的甘南草原区域蒸散发量时空格局分析[J].资源科学,2011.33(2)：341-346.
[203]周云轩,王黎明.陈圣波,等.吉林西部陆面遥感蒸散模型研究[J].吉林大学学报(地球科学版),2005.35(6)：812-817.
[2]Allen R G,Tasumi M,Trezza R,et al. Surface Energy Balance Algorithm for Land (SEBAL)-Advanced training and Users Manual [M]. Publisher:Idaho Department of Water Resources, University of Idaho,2002.
[3]Allen R G,Tasumi M,Morse A,et al. A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning [J]. Irrigation and Drainage Systems,2005,19(3):251-268.
[4]Allen R G. The ASCE Standardized Reference Evapotranspiration Equation [M]. American Society of Civil Engineers,2005.
[5]Allen R G,Trezza R,Tasumi M. Analytical integrated functions for daily solar radiation on slopes [J]. Agricultural and Forest Meteorology,2006,139(1-2):55-73.
[6]Ambast S k,Keshari A K,Gosain A K.An operational model for estimating Regional Evapotranspiratinn through Surface Energy Partitioning(RESEP) [J].International Journal of Remote Sensing,2002,23(22):4917-4930.
[7]Anderson M C, Norman J M, Diak G R, et al. A two source time-integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing [J]. Remote Sensing of Environment,1997,60(2):195-216.
[8]Anderson M C, Norman J M, Mechikalski J R, et al. A multiscale remote sensing model for disaggregating regional fluxes to micrometeorological scales [J]. Journal of hydronmeteorology,2004,5(2):343-363.
[9]Ballantine J A, Okin G S, Prentiss D E, et al. Mapping north a frican landforms using continental scale unmixing of MOD1S imagery[J]. Remote sensing of environment,2005, 97(4):470-483.
[10]Barducci A, Pippi I. Temperature and Emissivity Retrieval from Remotely Sensed Images Using the "Grey Body Emissivity" Method [J]. IEEE Transactions on Geosciences and Remote Sensine,1996,34:681-695.
[11]Bastiaanssen W G M, Bandara K M P S. Evaporative Depletion Assessments for Irrigated Watersheds in Sri Lanka [J].Irrig.Sci,2001(21):1-15.
[12]Bastiaanssen W G M, Menenti M, Feddes R A, et al. A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL)-1. Formulation [J]. Journal of Hydrology,1998, (212/213):198-212.
[13]Bastiaanssen W G M, Pelgrum H, Wang J, et al. A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL)-2. Validation [J]. Journal of Hydrology,1998,213(1/4): 213-229.
[14]Bastiaanssen W G M, Molden D J, Makin I W. Remote Sensing for Irrigated Agriculture: Examples from Research and Possible Applications [J]. Agricultural Water Management, 2000,46(2),137-155.
[15]Bastiaanssen W G M. SEBAL-based Sensible and Latent Heat Fluxes in the Irrigated Gediz Basin Turkey [J]. Journal of Hydrology,2000,229(1/2):87-100.
[16]Bastiaanssen W G M, Noordman E J M, Pelgrum H D, et al. SEBAL Model with Remotely Sensed Data to Improve Water-resources Management under Actual Field Conditions [J]. ASCE Journal of Irrigation and Drainage Engineering,2005,131(1):85-93.
[17]Batra N S, Islam V, Venturini G, et al. Estimation and Comparison of Evapotranspiration from MODIS and AVHRR Sensors for Clear Sky Days over the Southern Great Plains [J]. Remote Sensing of Environment,2006.103(1):1-15.
[18]Beljaars A C M, Holtslag A A M. Flux parameterization over land surfaces for atmospheric models [J], J. Appl. Meteorol.,1991,30,327-341.
[19]Berk A,Bernstein L S,Anderson G P,et al. MODTRAN Cloud and Multiple Scattering Upgrades with Application to AVIRIS-editions of 1991 and 1992[J]. Remote Sensing of Environment.1998.65(3):367-375.
[20]Blyth E M, Harding R J. Application of Aggregation Model to Surface Heat Flux from the Sahelian Tiger Bush[J]. Agricultural and Forest Meteorology,1995,72(3/4):213-215
[21]Bowen I S. The Ratio of Heat Losses by Conduction and by Evaporation from any Water Surface [J]. Physical Review,1926,27(6):779-787.
[22]Brutsaert W. Evaporation into the Atmosphere:Theory, History and Application [M]. TM Dordrecht:D. Reidel Publishing Company,1982.
[23]Burba G G,Verma S B,Kim J. Surface energy fluxes of Phragmites australis in a prairie wetland [J]. Agricultural and Forest Meteorology,1999,94(1):31-51.
[24]Burman R, Pochop L. Evaporation, Evapotranspiration and Climate Data [M]. The Netherlands:Elsevier Science,1994.
[25]Carlson T N, Perry E M, Schmugge T J. Remote Estimation of Soil Moisture Availability and Fractional Vegetation Cover for Agriculture Fields [J]. Agricultural and Forest Meteorology, 1990,52(1):45-69.
[26]Carlson T N, Capehart W J, Gillies R R. A New Look at the Simplified Method for Remote Sensing of Daily Evapotranspiration [J]. Remote Sensing of Environment,1995, 54(2):161-167.
[27]Carlson T N, Ripley D A J. On the Relationship between Fractional Vegetation Cover, Leaf Area Index, and NDVI [J]. Remote Sensing of Environment.1997,62(3):241-252.
[28]Chander G, Markham B. Revised Landsat-5 TM Radiometric Calibration Procedures and Post calibration Dynamic Ranges [J]. IEEE Transactions on Geosciences and Remote Sensing, 2003 (41):2674-2677.
[29]Cleugh H A, Leuning R, Mu Q Z, et al. Regional Evaporation Estimates from Flux Tower and MOD1S Satellite Data[J]. Remote Sensing of Environment,2007,106(3):285-304.
[30]Conrad C, Dech S W, Hafeez M, et al. mapping and assessing water use in a central Asian irrigation system by utilizing MODIS remote sensing products [J]. Irrigation and Draninage systems,2007,21(3/4):197-218.
[31]Cothier B E, Clawson K L, Printer P J, et al. Estimation of Soil Heat Flux from Net Radiation during the Growth of Alfalfa [J]. Agricultural and Forest Meteorology,1986,37,312-329.
[32]Delecollet R, Mass S J, Guerif M, et al. Remote Sensing and Crop Production Models: Present Trends[J]. ISPRS Journal of Photogrammetric and Remote Sensing,1992, 47(2/3):145-161.
[33]Dozier J, Frew J. Rapid calculation of terrain parameters for radiation modeling from digital elevation data [J]. IEEE Transaction on Geoscience and Remote Sensing,1990,28(5): 963-969.
[34]Dubayah R. Estimation net solar radiation using Landsat Thematic Mapper and digital elevation data [J]. Water Resource Research,1992,28:2469-2484.
[35]Gan Y N,Zhang W C.A simple empirical topographic correction method for ETM+imagery[J].International Journal of Remote Sensing,2009,30(9):2259-2275.
[36]Gillespie A, Rokugawa S, Matsunaga T. A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images [J]. IEEE Transactions on Geoscience and Remote Sensing,1998,36:311-326.
[37]Gillies R R, Carlson T N. Kustas W P, et al. A Verification of the "triangle method" for Obtaining Surface Soil Water Content and Energy Fluxes from Remote Measurements of the Normalized Difference Vegetation index(NDVI) and Surface Radiant Temperature [J]. Int. J. Remote Sens.,1997,18(15),3145-3165.
[38]Gomez M, Olioso A. Sobrino J A, et al. Retrieval of Evapotranspiration over the Alpilles/ReSeDA Experimental Site Using Airborne POLDER Sensor and a Thermal Camera[J]. Remote Sensing of Environment,2005,96(3/4):399-408.
[39]Griend A A, Owe M. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces [J]. International Journal of Remote Sensing. 1993,14(6):1119-1131.
[40]Gutman,ignatov A.The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models[J].International Journal of Remote sensing,1998,19(8):1533-1543.
[41]Hatfield J L,et al.Evaluation of canopy temperature evapotranspiration models over various surfaces [J].Agric Forest Meteor.1984,32:41-53.
[42]Idso S B. Reginato R J, Jackson R D. An Equation for Potential Evaporation from Soil, Water and Crop Surfaces Adaptable to Use by Remote Sensing [J]. Geophysical Research Letters.1977(4),187-188.
[43]Iqbal M. An Introduction to Solar Radiation [M]. Ontario:Academic Press Canada,1983.
[44]Isaacs R G.Wang W C.Worsham R D,et al. Multiple scattering LOWTRAN and FASCODE models [J]. Applied Optics.1987.26(7):1272-1281.
[45]Jackson R D, Reginato R J, Idso S B. Wheat Canopy Temperature:a Practical Tool for Evaluating Water Requirements [J]. Water Resource Research,1977,13(3):651-656.
[46]Jackson R D,et al.Plant canopy information extraction from composition scene reflectance of row crops[J].Appl Opt,1979,18(22):1892-1905.
[47]Jia L,Su Z,Hurk B,et al.Estimation of sensible heat flux using the Surface Energy Balance System(SEBS)and ATSR measurements[J].Physics and Chemistry of the Earth,2003,28:75-88.
[48]Jiang L, Islam S. An Intercomparison of Regional Latent Heat Flux Estimation Using Remote Sensing Data [J]. International Journal of Remote Sensing,2003,24(11):2221-2236.
[49]Kalluri S N V, Townshend J R G, Doraiswamy P A. Simple Single Layer Model to Estimate Transpiration from Vegetation Using Multispectral and Meteorological Data [J]. Int. J Remote Sensing,1998(19):1037-1053.
[50]Kaufman Y J,Karniell A,Tanre D,et al. Detection of dust over deserts using satellite data in the solarwavelengths [J]. Geoscience and Remote Sensing,IEEE Transactions on,2000,38(1):525-531.
[51]Kerenyi J, Putsay M. Investigation of Land Surface Temperature Algorithms Using NOAA ANVHRR Images [J]. Adv. Space Res,2000,26(17):1077-1080.
[52]Kjaersgaard J H, Plauborg F L, Hansen S. Comparison of Models for Calculating Daytime Long-wave Irradiance using Long Term Dataset[J]. Agricultural and Forest Meteorology 143. 2007,49-63.
[53]Kornfield J, Susskind J. On the Effect of Surface Emissivity on Temperature Retrievals [J]. Monthly weather Review,1977,105:1605-1608.
[54]Kustas W P, Choudhury B J, Moran M S, et al. Determination of Sensible Heat Flux Sparse Canopy Using Thermal Infrared Data [J]. Agricultural and Forest Meteorology,1989,44(3/4): 197-216.
[55]Kustas W P, Daughtry C S T. Estimation of the Soil Heat Flux/net Radiation Ratio from Spectral Data [J], Agric. For. Meteorology,1989,49,205-223.
[56]Kustas W P. Estimates of evapotranspiration within a one-and two-layer model of heat transfer over partial vegetation cover [J]. Journal of Applied Meteorology,1990,29:704-715.
[57]Kustas W P, Norman J M. Evaluation of Soil and Vegetation Heat Flux Predictions using A Simple Two Source Model with Radiometric Temperatures for Partial Canopy Cover[J]. Agricultural and Forest Meteorology.1999(94):13-29.
[58]Kustas W P, Norman J M, Anderson M C, et al. Estimating subpixel surface temperatures and energy fulxes from the vegetation index-radiometric temperature relationship [J]. Remote sensing of environment,2003,85(4):429-440.
[59]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.
[60]Lhomme J P,Monteny B,Amadou M. Estimating sensible heat flux from radiometric temperature over sparse miller[J].Agricultural and Forest Meteorlgy,1994,68:77-91.
[61]Lhomme J P, Troufleau D, Monteny B, et al. Sensible Heat Flux and Radiometric Surface Temperature over Sparse Sahelian Vegetation. A model for the kB-1 Parameter [J]. Journal of Hydro logy,1997(188-189):839-854.
[62]Lhomme J P, Chehbouni A. Comments on Dual-source Vegetation Atmosphere-transfer Models [J]. Agricultural and Forest Meteorology,1999,94(3/4):269-273.
[63]Li Jia, Zhongbo Su, Bart van dan Hurk, et al. Estimation of sensible heat flux using the surface energy balance system (SEBS) and ATSR measurements [J]. Physics and Chemistry of the Earthe,2003,28:75-88.
[64]Li X, Cheng G D, Chen X Z, et al. Modification of solar radiation model over rugged terrain [J]. Chinese Science Bulletin,1999,44(15):1345-1350.
[65]Liang S L. Narrowband to broadband conversions of land surface albedo. I-Algorithms [J]. Remote Sensing of Environment,2001,76(2):213-238.
[66]Liang S L,Fang H,Morisette J T,et al. Atmospheric correction of Landsat ETM+land surface imagery.H. Validation and applications [J]. Geoscience and Remote sensing, IEEE Transactions on,2002,40(12):2736-2746.
[67]Liang S L,Shuey C J.Russ A L,et al. Narrow band to broadband Conversions of land surface albedo. Ⅱ-Validation [J]. Remote Sensing of Environment,2003,84(1):25-41.
[68]Lin Wenjing, Su Zhongbo, Dong Hua, et al. Regional evapotranspiration estimation based on MOIDS products and Surface Energy Balance System (SEBS) in Hebei Plain,Northeastern China[J]. Journal of Remote Sensing,2008,12(4):663-672.
[69]Liou K.N.大气辐射导论(第2版)[M].北京：气象出版社,2004
[70]Malliek K, Bhattacharya B K, Chaurasia S, et al. Evapotranspiration using MODIS data and limited ground observations over selected agroeco systems in India [J]. International Journal of Remote Sensing,2007,28(10):2091-2110.
[71]Massman W J. An analytical one-dimensional model of momentum transfer by vegeration of arbitrary structure [J]. Boundary-Layer Meteor,1997,83:407-421.
[72]Massman W J. A model study of kB-1 for vegetated surfaces using 'localized near-field' Lagrangian theory [J]. J Hydrol,1999,223:27-43.
[73]Matson M E P, Mcclain D F. McGinnis Satellite Detection of Urban Heat Islands [J]. Monthly Weather Review.1978,106(12):1725-1734.
[74]McMillin L M. Estimation of Sea Surface Temperature from Two Infrared Window Measurements with Different Absorption [J]. Journal Geophysical Research.1975, 20:5113-5117.
[75]Mecikalski J R, Diak G R, Anderson M C, et al. Estimating Fluxes on Continental Scales using Remotely Sensed Data in An Atmospheric Land Exchange Model [J]. Journal of Applied Meteorology,1999(38):1352-1369.
[76]Menenti M, Choudhury B. Parameterization of Land Surface Evaporation by Means of Location Dependent Potential Evaporation and Surface Temperature Range [C]//Proceedings of IAHS conference on Land Surface Processes,1993,212:561-568.
[77]Monteith J L. Principles of Environmental Physics [D]. London:Edward Aronld,1973.
[78]Moran M S, Clarke T R, Inoue Y, et al. Estimating Crop Water Deficit Using the Relationship between Surface-air Temperature and Spectral Vegetation Index[J]. Remote Sensing of Environment,1994,49(3):246-263.
[79]Nishida K, Nemani R R, Running S W, et al. Remote Sensing of Land Surface Evaporation(1) Theoretical Basis for an Operational Algorithm [EB/OL]. [2011-05-20]. Http://secure.ntsg.umt.edu/projects/files/documents/mod16/SER-ver9-JGR.DOC
[80]Norman J M, Kustas W P, Humes K S. A Two-source Approach for Estimating Soil and Vegetation Energy Fluxes from Observation of Directional Radiometric Surface Temperature [J]. Agricultural and Forest Meteorology,1995(77):263-293.
[81]Oke T R, Johnson G T, Steyn D G, et al. Simulation of Surface Urban Heat Islands under 'ideal' Conditions at Night Part 2 :Diagnosis of Causation [J]. Boundary-Layer Meteorology, 1991,56:339.
[82]Olioso A, Chauki H, Courault D, et al. Estimating of Evapotranspiration and Photosynthesis by Assimilation of Remote Sensing Data into SVAT Models [J]. Remote Sensing of Environment,1999,68(3):341-356.
[83]Penman H L. Natural Evaporation from Open Water, Bare Soil and Grass[C]. Proceedings of the Royal Society of London, Series A, Mathematical and Physical Sciences,1948:120-145.
[84]Prata A J, Caselles V, et al. Thermal Remote Sensing of Land Surface Temperature from Satellites:Current Status and Future Prospects [J]. Remote Sensing Review,1995,12, 175-224.
[85]Prata A J. A New Long-wave Formula for Estimating Downward Clear-sky Radiation at the Surface [J]. Quarterly Journal of the Royal Meteorological Society.1996,122,1127-1151.
[86]Prater M R, Delucia E H. Non-native Grasses Alter Evapotranspiration and Energy Balance in Great Basin Sagebrush Communities [J]. Agricultural and Forest Meteorology,2006, 139(1/2):154-163.
[87]Pricec J C. Land Surface Temperature Measurements from Split window Channel of the NOAA 7 Advance Very High Resolution Radiometer [J]. Geophysical Research.1984, 98:7231-7237.
[88]Qin Z, Berliner P, Karnieli A. Numerical Solution of A Complete Surface Energy Balance Model for Simulation of Heat Fluxes and Surface Temperature under Bare Soil Environment [J]. Applied Mathematics and Computation,2002 (130):171-200.
[89]Ranzi R, Rosso R. Distributed estimation of incoming direct solar radiation over a drainage basin [J]. Journal of Hydrology,1995,166:461-478.
[90]Roerink G J, Su Z, Menenti M. A Simple Remote Sensing Algorithm to Estimate the Surface Energy Balance [J]. Physics and Chemistry of the Earth (B),2000,25(2):147-157.
[91]Rouse J W, Haas R H, Schell J A, et al. Monitoring Vegetation Systems in the Great Plains with ERTS [J]. Third ERTS Symposium, NASA SP-351,1973 (1):309-317.
[92]Sanchez J M, Caselles V, Niclos R, et al. Evaluation of the B-method for Determining Actual Evapotranspiration in a Boreal Forest from MODIS Data [J]. International Journal of Remote Sensing,2007,28(6):1231-1250.
[93]Sanchez J M, Scavone G, Caselles V, Valor E, et al. Monitoring Daily Evapotranspiration at A Regional Scale from Landsat TM and ETM+ Data:Application to the Basilicata Region [J]. Journal of Hydrology,2008,351,58-70.
[94]Schmugge T J, Kustas W P, Ritchie J C, et al. Remote Sensing in Hydrology [J]. Advances in Water Resources,2002,25(8/12):1367-1385.
[95]Seguin B, Itier B. Using Midday Surface Temperature to Estimate Daily Evaporation from Satellite Thermal IR Data [J]. International Journal of Remote sensing,1983,4(2):371-383.
[96]Sells P J, Hall E G, Asrar G, et al. The First ISLSCP Field Experiment (FIFE). J. Bull. Amer. Meteor, Soc.,1998,69 (1):22-27.
[97]Shuttleworth W J, Gurney, R J. The Theoretical Relationship between Foliage Temperature and Canopy Resistance in Sparse Crop [J]. Quarterly Journal of Royal Meteorological Society, 1990,116(492):497-519.
[98]Sobrino J A, Caselles V. A Methodology for Obtaining the Crop Temperature from NOAA-9 AVHRR Data [J]. International Journal of Remote Sensing,1991,12,461-475.
[99]Sobrino J A, Jimenez-Munoza J C, Paolini L. Land surface temperature retrieval from Landsat TM5 [J]. Remote Sens Environ.2003,75:256-266.
[100]Sobrino J A, Gomez M, Jimenez-Munoz J, et al. A Simple Algorithm to Estimate Evapotranspiration from DAIS Data:Application to the DA1SEX campaigns [J]. Journal of Hydrology,2005,315(1/4):117-125.
[101]Stull B R,赵长新(译).边界层气象学导论[M].北京：气象出版社，1991.156-316.
[102]Su Z. Remote sensing of land use and vegetation for mesoscale hydrological studies [J].Remote sensing.2000,21:213-233.
[103]Su Z. A Surface Energy Balance System(SEBS) for estimation of turbulent heat fluxes from point to continental scale[M].//Su Z, Jacobs J, eds. Advanced Earth Observation Land Surface Climate. Publications of the National Remote Sensing Board(BCRS). USP-2.01-02, 2001:91-108.
[104]Su Z. The Surface Energy Balance System (SEBS) for Estimation of Turbulent Heat Fluxes [J]. Hydrology and Earth System Sciences,2002,6(1):85-99.
[105]Su Z,Wen J,Li W. A methodology for the retrieval of land physical parameter and actual evaporation using NOAA/AVHRR Data [J]. Journal of Jilin University(Science),2003,33:106-118.
[106]Su Z. Estimation of the Surface Engery Balance.[A] Anderson M G. Encyclopedia of Hydrological Science. [M] John Wiley & Sons, Ltd,2005:1-22.
[107]Sun X M, Zhu Z L, Tang X Z, et al. The Impact of Local Advection on the Accuracy of Surface Fluxes based on Remote Sensing [J]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS),2002 (V4):2323-2325.
[108]Tasumi M, Allen R G.Trezza R,et al. Satellite-Based Energy Balance to Assess Within-Population Variance of Crop Coefficient Curves [J]. Journal of Irrigation and Drainage Engineering ASCE,2005,131(1):94-109.
[109]Tasumi M, Allen R G. Satellite-based ET Mapping to Assess Variation in ET with Timing of Crop Development [J]. Agricultural Water Management,2007,88(1/3):54-62.
[110]Thornthwaite C W, Holzman A. Report of the Commutation on Transpiration and Evaporation [J]. Transactions of the American Geophysical Union,1944,25(1):683-693.
[111]Timmermans W J,vailder Kwast,Gieske A S M, et al. Intercomparison of energy flux models using ASTER imagery at the SPARC 2004 site(Barrax,Spain).Presented at the ESA proceedings WPP-250:SPARC final workshop,4-5,July,2005.Enschede,ESA,2005.8 P.
[112]Van den Hurk, Holtslag. On the bulk parameterization of surface fluxes for various conditions and parameter ranges [J]. Boundary-Layer Meteorol.1995,82,199-234.
[113]Vermote E F,Tanre D,Deuze J L,et al. Second Simulation of the Satellite Signal in the Solar Spectrum,6S:An overview [J]. Geoscience and Remote Sensing,IEEE Transactions on,1997,35(3):675-686.
[114]Voogt J A, Oke T R. Thermal Remote Sensing of Urban Climates [J]. Remote Sensing of Environment,2003,86:370-384.
[115]Wang J, White K, Robinson G J. Estimating surface net solar radiation by use of Landsat-5 TM and digital elevation models [J]. International Journal of Remote Sensing,2000, 21(1):31-43.
[116]Wang K, Li Z, Cribb M. Estimation of Evaporative Fraction from A Combination of Day and Night Land Surface Temperature and NDV1:A New Method to Determine the Priestley Taylor Parameter [J]. Remote Sensing of Environment,2006,102:293-305.
[117]Weng Q H, Lu D S, Jacquelyn S. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 2004,89:467-483
[118]Weng Q. A Remote Sensing-GIS Evaluation of Urban Expansion and Its Impact on Surface Temperature in Zhujiang Delta, China [J]. International Journal of Remote Sensing,2001,22 (10):1999
[119]Willmott C J. Some comments on the evaluation of model performance [J]. Bulletin of the American Meteorological Society,1982,11:1309-1313.
[120]Zhan X, Kustas W P, Humes K S. An Intercomparison Study on Model of Sensible Heat Flux over Partial Canopy Surfaces with Remotely Sensed Surface Temperature [J]. Remote Sensing of Evironment,1996,58(3):242-256.
[121]Zhang L, Lemeur R, Goutorbe J P. A one-layer Resistance Model for Estimating Regional Evapotranspiration Using Remote Sensing Data [J]. Agricultural and Forest Meteorology, 1995,77(3/4):241-261.
[122]Zhang W C, Zhang Y S. Observation and Estimation of Daily Actual Evapotranspiration and Evaporation on a Glacier zed Watershed at the Headwater of the Urumqi River, Tianshan, China [J]. Hydrological Processes,1999,13 (11):1589-1601.
[123]陈镜明.现用遥感蒸散模式中的一个重要缺点及改进[J].科学通报.1988(6)：454-457.
[124]陈鸣,潘之棣.用卫星遥感红外数据估算大面积蒸散量[J].水科学进展,1994,5(2)：126-133.
[125]陈志明,李家国,余涛.TM遥感影像的地形辐射校正研究[J].遥感信息,2009,2：56-59.
[126]邓玉娇,刘诚,林锦祥.基于MODIS资料研究广州市热环境[J].热岛气象学报.2007.23(3)
[127]丁圣彦,梁国付.地理环境因素对伊洛河流域森林景观的影响[J].地理研究,2007,26(5)：906-914.
[128]杜非，伊洛河流域土地覆被变化水文效应研究[D].河海大学,2007.
[129]甘甫平,陈伟涛,张绪教,等.热红外遥感反演陆地表面温度研究进展[J].国土资源遥感,2006,1(67)：6-11.
[130]高彦春,龙笛.遥感蒸散发模刑研究进展[J].遥感学报,2008.12(3)：515-528.
[131]高水年.高俊峰,张万昌,等.地形效应下的区域蒸散遥感估算[J].农业工程科学 报,2010,26(10)：218-223.
[132]郭建民,郑金亮.伊洛河志[M].北京：中国科学技术出版社,1995.
[133]河南省洛阳市农牧局、洛阳市十壤普查办公室编.洛阳土壤[M].北京：海洋出版社,1991.
[134]何洪林,于贵瑞.复杂地形条件下的太阳资源辐射计算方法研究[J].资源科学,2003,25(1)：78-85.
[135]何玲,莫兴国,汪志农.基于MODIS遥感数据计算无定河流域日蒸散[J].农业工程学报,2007,23(5)：114-]50
[136]何延波,王石立.遥感数据支持下不同地表覆盖的区域蒸散[J].应用生态学报,2007,18(2)：289—295.
[137]何延波, Su Zhongbo, LI Jia,等SEBS模型在黄淮海地区地表能量通量估算中的应用[J].高原气象,2006,25(6)：1092-1100.
[138]贺瑞敏,王国庆.张建云.环境变化对黄河中游伊洛河流域径流量的影响[J].水十保持研究,2007,14(2)：297-301.
[139]郝芳华,陈利群,刘昌明.土地利用对产流和产沙的影响分析[J].水土保持学报,2004,18(3)：5-8.
[140]洪刚,李万彪.卫星遥感估算淮河流域区域能量通量的方法研究.北京大学学报：自然科学版,2001,37(5)：693-700.
[141]黄妙芬.地表通量研究进展[J].干旱区地理,2003(26)：159-165.
[142]黄妙芬,邢旭峰,刘素红,等.反演地表温度三要素获取途径研究及其研究价值[J].干旱区地理,2005，28(4)：541-547.
[143]李武彦,乔智克.杨金榜.等.嵩县水土保持生态环境建设成效与经验[J].河南水利与南水北调,2008,10：25-26.
[144]李小建,刘钢军.钱乐祥,等.中尺度流域土地利用/土地覆盖变化评估——以伊洛河中部地区为例[J].地理科学,2001,21(4)：289-296.
[145]李新,马明国,王建,等.黑河流域遥感—地面观测同步试验科学目标与试验方案[J].地球科学进展,2008,23(9)：897-914.
[146]李玉环,王静,吕春燕,等.基于TM ETM遥感数据的地面相对反射率反演[J].中国农业大 学学报(自然科学版),2005,36(4)：545-551.
[147]李古清,翁笃鸣.一个计算山地日照时间的计算机模式[J].科学通报,1987,32(17)：1333-1335.
[148]李古清,翁笃鸣.丘陵山区地面辐射场的数据模拟[J].南京气象学报,1990,13(2)：184-193.
[149]李发鹏,徐宗学,李景玉.基于MODIS数据的黄河三角洲区域蒸散发量时空分布特征[J].农业工程学报,2009,25(2)：113-120.
[150]刘朝顺,施润和,高炜,等.利用区域遥感ET分析山东省地表水分盈亏的研究[J].自然资源学报,2010,25(11)：1938-1948.
[151]梁国付,丁圣彦.森林景观变化的地理环境因素整合分析[J].生态学报,2010,30(6)：121-132.
[152]蔺文静,董华,陈立,等.基于SEBAL模型的区域蒸发蒸腾遥感估算[J].遥感信息,2008,16(5)：112-1]4.
[153]刘雅妮,辛晓洲,柳钦火,等.基于多尺度遥感数据估算地表通量的方法及其验证分析[J].地球科学进展,2010,25(11)：1261-1272.
[154]柳饮火,辛晓洲,唐娉,等.定量遥感模型、应用及不确定性研究[M].北京：科学出版社,2010.
[]55]卢俐,刘绍民,孙敏章,等.大孔径闪烁仪研究区域地表通量的进展[J].地球科学进展,2005,20(9)：932-938.
[156]卢娜,万力.基于RS的鄂尔多斯北部盆地地表蒸发量的计算[J].地质通报,2008,27(8)：1165-]167.
[157]卢氏县水利志编篡领导小组.卢氏县水利志[M].西安：陕西人民出版社,2001
[158]鹿洁忠.苏联对水面蒸发测定和计算方法的研究[J].中国农业气象,1991,12(8)：54-56.
[159]马耀明,戴有学,马伟强,等,干旱半干旱区非均匀地表区域能量通量的卫星遥感参数化[J].高原气象,2004,23(2)：139-146.
[160]毛克彪,覃志豪,施建成,等.针对MODIS影像的劈裂窗算法研究[J].武汉大学学报：信息科学版.2005,30(8).
[161]苗长虹等.伊洛河流域土地利用和土地覆被变化与可持续发展研究[M],中国环境出版 社,2006.
[162]潘志强,刘高焕.黄河三角洲蒸散的遥感研究[J].地球信息科学,2003(3)：91-96.
[]63]钱学伟,李秀珍.陆面蒸发计算方法述评[J].水文,1996,6：49-53.
[164]乔平林,张继贤,王翠华.石羊河流域蒸散发遥感反演方法[J].干旱区资源与环境,2007,21(4)：107-110.
[165]盛裴轩,毛节泰,李建国.大气物理学[M]：北京大学出版社,2003.
[166]孙睿,刘昌明.地表水热通量研究进展[J].应用生态学报,2003(14)：434-438.
[167]孙菽芬.陆面过程的物理、生化机理和参数化模型[M].北京：气象出版社,2005.
[]68]覃志豪,Zhang Minghua, Karrieli A用NOAA-AVHRR热通道数据算法地表温度的辟窗算法[J].国十资源遥感,2001,(2)：33-42.
[169]覃志豪,张明华,Amon Kamieli,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4)：456-466.
[170]覃志豪,李文娟,徐斌.等.陆地卫星TM6波段范围内地表比辐射率的估计[J].国土资源遥感,2004,61(3)：28-41.
[171]田国良.黄河流域典型地区遥感动态研究[C].北京：科学出版社,1989.
[172]田景环,崔庆,徐建华.等.黄河流域大中型水库水面蒸发对水资源量的影响[J].山东农业大学学报.自然科学版,2005,36(3)：391-394.
[173]田庆久,郑兰芬.基于遥感影像的大气辐射校正和反射率反演方法[J].应用气象学报,1998,9(4)：456-461.
[174]王兵,臧玲.伊洛河流域开发战略研究[J].地域研究与开发,2007,26(6)：63-56,74.
[175]王冬妮,李国春,张红玲,等.用MODIS数据反演辽西北地区陆面温度[J].农业网络信息.2006.1
[176]王红振,明亮,晋建设.等.近50年黄河中游三花区间降水特征分析[J].气象与环境科学,2009,32(2)：78-82.
[177]王记芳,程炳岩，朱业玉,等.黄河中游三花问区气候变化特征[J].气象,2009,35(3)：99-106.
[]78]王开存,刘晶淼,周秀骥,等.利用MODIS卫星资料反演中国地区晴空地表短波反照率及其特征分析[J],大气科学,2004,28(6)：941-949.
[179]王黎明.吉林省西部区域遥感蒸散模型研究及其应用[D].吉林：吉林大学,2005.
[180]王梅,于建波,那景坤.E-601型蒸发器水面蒸发实验分析[J].黑龙江水专学报,2004,31(3)：1-12.
[181]王远明,张彼,李成荣.宜昌站水面蒸发折算系数分析[J].人民长江,1999,30(1)：41-45.
[182]吴致尧,任德存.黄河流域综述[M].郑州：河南人民出版社,1998
[183]吴炳方,熊隽,闫娜娜ETWatch的模型与方法[J].遥感学报,2011,15(2)：224-239.
[184]吴炳方,邵建华.遥感估算蒸腾蒸发量的时空尺度推演方法及应用[J].水利学报,2006,37(3)：286-292.
[185]谢贤群.遥感瞬时作物表面温度估算农田全日蒸发散总量[J].环境遥感,1991,6(4)：253-259.
[186]辛晓洲,田国良,柳钦火.地表蒸散定量遥感的研究进展[J].遥感学报,2003,7(3)：233-240
[187]辛晓洲.定量遥感方法计算地表烝散[D].中国科学院研究生院博十论文,2003.6.
[188]徐同仁,刘绍民,秦军等.同化MODIS温度产品估算地表水热通量[J].遥感学报,2009,13(6)：999-1009..
[189]徐希孺,柳钦火,陈家宜.遥感陆面温度[J].北京大学学报(自然科学版),1998,34(2-3)：248-253.
[190]杨虎,杨忠东.中国陆地区域陆表温度业务化遥感反演算法及产品运行系统[J].遥感学报,2006,10(4)：600-607.
[191]杨青生,刘闯MODIS数据陆面温度反演研究[J].遥感技术与应用.2004.19(2)：90-94
[192]曾燕,邱新法,潘敖大等.地形对黄河流域太阳辐射影响的分析研究[J].地球科学进展,2008,23(11)：1185-1193.
[193]詹志明,冯兆东,秦其明.陇西黄土高原陆面蒸散的遥感研究[J].地理与地理信息科学,2004,20(1)：16—19.
[194]张长春,王晓燕,邵景力.利用NOAA数据估算黄河三角洲区域蒸散量[J].资源科 学,2005,27(1)：86-91.
[195]张东，张万吕,徐全芝.汉江上游流域蒸散量计算方法的比较和改进[J].资源科学,2005,27(1)：97-103.
[196]张仁华.实验遥感模型及地面基础[M].北京：科学出版社,1996.
[197]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,2002,32(12)：1041-1051.
[198]张仁华,孙晓敏,王伟民,等.一种可操作的区域尺度地表通量定量遥感二层模型的物理基础[J].中国科学D辑,2004,34(增刊11)：200-216.
[199]张仁华.定量热红外遥感模型及地面实验基础[M].北京：科学出版社,2009：335,344,494.
[200]张文江,陆其峰，高志强.区域地表水热通量遥感的空间不均匀性分析[J].自然科学进展.2007.17(10)：1383-1390.
[201]张晓涛,康绍忠.王鹏新,等.估算区域蒸发蒸腾量的遥感模型对比分析[J].农业工程学报.2006(22)：6-13.
[202]赵军.刘春雨,潘竞虎,等.基于MODIS数据的甘南草原区域蒸散发量时空格局分析[J].资源科学,2011.33(2)：341-346.
[203]周云轩,王黎明.陈圣波,等.吉林西部陆面遥感蒸散模型研究[J].吉林大学学报(地球科学版),2005.35(6)：812-817.