大尺度被动微波辐射计土壤水分降尺度方法研究
|
摘要
土壤水分是控制陆地和大气间水热能量交换的关键因子之一,作为水循环体系的重要组成部分,土壤水分在地球生态系统中起着重要的作用,是陆地植物以及其它土壤生物得以生存的物质基础。此外,土壤水分也是各类气候模型、水文模型、生态模型以及陆面过程模型的核心输入参数,其变化会影响水热过程,从而改变地表参数并影响气候变化。因此,定量化获取准确的土壤水分数值对农业生产、应对全球变化、生态环境保护等众多领域都有着重要的意义。
不同的遥感平台可提供不同时间和空间尺度的土壤水分,它们分别具有各自的优势与不足。被动微波数据较热红外、主动雷达数据而言,对土壤水分信息敏感,重返周期在1到3天,可以捕捉到土壤水分的时间变化信息,但是其空间分辨率较低(25km-40km),一般适用于大尺度研究；星载可见光/热红外数据可以达到中等分辨率到高分辨率(100m-1km),但是其应用可能受到天气条件限制,而且对土壤水分的敏感性不够理想。为了克服上述问题,就需要将被动微波数据和光学数据结合,从而同时提高数据的时间和空间分辨率,以得到可靠的土壤水分。
本文计划利用被动微波辐射计AMSR-E土壤水分数据和MODIS数据,发展大尺度土壤水分降尺度算法,并选取亚洲蒙古地区与青藏高原地区,作为方法验证区域。
论文的研究内容具体包括以下四点：
(1)被动微波辐射计AMSR-E反演大尺度土壤水分
选取被动微波辐射计AMSR-E数据,通过引入Qp模型对已有算法进行改进计算得到的。该算法在Jackson的单通道算法的框架下,利用37GHz V极化亮温计算地表温度,采用植被含水量和归一化植被指数的经验关系,获取植被光学厚度,消除植被对微波信号的影响,最后采用基于Qp模型发展的双通道算法消除地表粗糙度的影响从而直接获得25km土壤水分。
(2)利用空间频谱信息方法分解大尺度土壤水分
引入频谱降尺度方法,即通过傅里叶变换将土壤水分数据的灰度变化函数分解为一系列周期函数的叠加,再通过对上述周期函数的统计转化为由振幅和相位表示的频率域图像。从土壤水分遥感图像频域出发,在不同空间分辨率的土壤水分图像其空间频率与功率谱密度的之间存在固定关系的前提下,结合AMSR-E低分辨率土壤水分图像,建立该图像功率谱密度和空间频率的幂指数拟合关系。其中作者对M. Montopoli (2012)等人提出的方法上进行了部分改进,优化了幂指数拟合关系,得到高分辨率振幅信息,同时利用克里金插值方法随机生成了高分辨率相位信息,经过傅里叶逆变换,选取蒙古地区,实现大尺度土壤水分降尺度方法,并分析了插值方法的数据局限性,从而提出了结合高分辨率数据的必要性和方法大致思想。
(3)基于蒸散原理土壤水分相关参量响应机制分析
利用被动微波辐射计AMSR-E土壤水分和MODIS地表温度LST、归一化植被指数NDVI与反照率Albedo产品,以基于Ts-NDVI三角形特征空间的蒸散理论为基础,采用多元线性回归的统计方法对研究区内长时间序列中,被动微波辐射计空间尺度下土壤水分对地表温度、植被指数与地表反照率等光学遥感参量的响应机制进行分析,从而得到土壤水分数据与遥感获取的以上光学因子之间的经验关系。
(4)基于高分辨率相位信息频谱降尺度方法提出、实现与研究区验证
利用前文得到的AMSR-E土壤水分和MODIS地表温度LST、归一化植被指数NDVI与地表反照率Albedo等光学因子之间的经验关系式,由MODIS数据推导得到高分辨率相位信息,并代入替换土壤水分数据中的低分辨率相位信息。之后通过傅里叶逆变换算法,即可得到空间分辨率优于被动微波数据分辨率的降尺度土壤水分数据。
通过与蒙古实验区及青藏高原实验区内CEOP计划地面实测数据的比较表明,使用本文方法获取的高分辨率土壤水分数据的数值与变化取值与实测数据吻合较好,证实论文提出的降尺度方法的准确性及可信度较高。
Soil moisture is a key parameter in regulating the hydrothermal energy exchange between land and atmosphere, as well as an important part of the hydrologic cycle of the ecological system on ground surface and an important material source for terrestrial plants and soil organisms. Soil moisture is also an important input parameter of many models, such as climate model, hydrological model, ecological model, land surface model etc. The change of soil moisture will affect the hydrothermal process, then change the surface parameters and impact the climate. Therefore the quantitative monitoring of accurate soil moisture value is of great significance to agricultural production, global change strategy, ecological and environmental protection and many other fields.
Different types of remote sensing platforms are currently used to infer soil moisture at different spatial and temporal scales, each with its specific characteristics and limitations. Compared to thermal infrared and active radar data, passive microwave data gives more accurate inversion of ground surface soil moisture. The passive microwave data, being sensitive to soil moisture information and having a repeat cycle of1to3days, can capture the temporal variation information of soil moisture, although its relatively low spatial resolution (25km-40km) makes it generally suitable for large-scale research. Average soil moisture information of large-scale grid can be obtained from a variety of active and passive microwave remote sensor platform with much ease. While the satellite-borne visible light and thermal infrared data can achieve a medium to high level of resolution (100m-lkm), it's likely to be affected by weather conditions and has a less-than-ideal sensitivity to soil moisture. To overcome these issues, some of the current researches incorporate passive microwave data and optical data, with the purpose of improving both temporal and spatial resolution while achieving reliable soil moisture information.
Choosing Mongolia and the Qinghai-Tibet Plateau region as the study area, this paper improves the algorithm of decomposing large-scale soil moisture via passive microwave radiometer AMSR-E soil moisture data and MODIS surface temperature, NDVI and albedo products.
The four main study points of this paper are shown as follows:
I. Retrieving soil moisture from passive microwave radiometer AMSR-E
The author calculates the25km AMSR-E soil moisture data from passive microwave radiometer AMSR-E data through an improved version of the Qp model. Based on Jackson's single channel algorithm, the author uses37GHz V polarized brightness temperature to calculate the surface temperature, and get vegetation optical thickness from the experience relationship of vegetation water content and NDVI, in order to eliminate the influence of vegetation on the microwave signal. Finally, based on the dual-channel algorithm developed from Qp model, this paper eliminates the impact of surface roughness and obtains the25km soil moisture directly.
II. Decomposing large-scale soil moisture utilizing space spectrum method
This paper uses the method of space spectrum downscaling, which is improved from M. Montopoli's algorithm, to decompose AMSR-E soil moisture data. By breaking down the gray-scale changes function of the soil moisture data into a series of superimposed periodic function using the Fourier transform, the author converts the soil moisture data to frequency domain images of the amplitude and phase through statistical conversion on periodic functions. Due to the fixed relationship between the soil moisture and the spatial frequency of the power spectral density in different spatial resolution images, the exponent relationship of the power spectral density and spatial frequency can be created.
III. Analyzation of the empirical relationship of soil moisture parameters based on the evapotranspiration theory
The paper analyzes the long sequence response mechanism between soil moisture and optical remote sensing parameters, such as surface temperature, vegetation index and surface albedo. This method is based on the triangle Ts-NDVI feature space learning from the evapotranspiration theory and statistical method of multiple linear regression analysis, from which the author gets the empirical relationship between soil moisture and the remote sensing optical factors above.
IV. The method of space spectrum downscaling via high-resolution phase information with its application and field verification
The author retrieves high-resolution phase information from MODIS data via the relationship between AMSR-E soil moisture and MODIS surface temperature, NDVI and albedo, and then substitution to replace soil moisture data the low-resolution phase information. Then by the Fourier inverse transform, spatial resolution better than the resolution of passive microwave data can be obtained by downscaling soil moisture data.
By verifying the results with the CEOP ground data in Mongolia and Qinghai-Tibet Plateau study area, it shows that the high-resolution soil moisture data obtained using the downscaling method introduced in this paper matches the measured data in good agreement. Thus the high accuracy and credibility of the method is confirmed.
不同的遥感平台可提供不同时间和空间尺度的土壤水分,它们分别具有各自的优势与不足。被动微波数据较热红外、主动雷达数据而言,对土壤水分信息敏感,重返周期在1到3天,可以捕捉到土壤水分的时间变化信息,但是其空间分辨率较低(25km-40km),一般适用于大尺度研究；星载可见光/热红外数据可以达到中等分辨率到高分辨率(100m-1km),但是其应用可能受到天气条件限制,而且对土壤水分的敏感性不够理想。为了克服上述问题,就需要将被动微波数据和光学数据结合,从而同时提高数据的时间和空间分辨率,以得到可靠的土壤水分。
本文计划利用被动微波辐射计AMSR-E土壤水分数据和MODIS数据,发展大尺度土壤水分降尺度算法,并选取亚洲蒙古地区与青藏高原地区,作为方法验证区域。
论文的研究内容具体包括以下四点：
(1)被动微波辐射计AMSR-E反演大尺度土壤水分
选取被动微波辐射计AMSR-E数据,通过引入Qp模型对已有算法进行改进计算得到的。该算法在Jackson的单通道算法的框架下,利用37GHz V极化亮温计算地表温度,采用植被含水量和归一化植被指数的经验关系,获取植被光学厚度,消除植被对微波信号的影响,最后采用基于Qp模型发展的双通道算法消除地表粗糙度的影响从而直接获得25km土壤水分。
(2)利用空间频谱信息方法分解大尺度土壤水分
引入频谱降尺度方法,即通过傅里叶变换将土壤水分数据的灰度变化函数分解为一系列周期函数的叠加,再通过对上述周期函数的统计转化为由振幅和相位表示的频率域图像。从土壤水分遥感图像频域出发,在不同空间分辨率的土壤水分图像其空间频率与功率谱密度的之间存在固定关系的前提下,结合AMSR-E低分辨率土壤水分图像,建立该图像功率谱密度和空间频率的幂指数拟合关系。其中作者对M. Montopoli (2012)等人提出的方法上进行了部分改进,优化了幂指数拟合关系,得到高分辨率振幅信息,同时利用克里金插值方法随机生成了高分辨率相位信息,经过傅里叶逆变换,选取蒙古地区,实现大尺度土壤水分降尺度方法,并分析了插值方法的数据局限性,从而提出了结合高分辨率数据的必要性和方法大致思想。
(3)基于蒸散原理土壤水分相关参量响应机制分析
利用被动微波辐射计AMSR-E土壤水分和MODIS地表温度LST、归一化植被指数NDVI与反照率Albedo产品,以基于Ts-NDVI三角形特征空间的蒸散理论为基础,采用多元线性回归的统计方法对研究区内长时间序列中,被动微波辐射计空间尺度下土壤水分对地表温度、植被指数与地表反照率等光学遥感参量的响应机制进行分析,从而得到土壤水分数据与遥感获取的以上光学因子之间的经验关系。
(4)基于高分辨率相位信息频谱降尺度方法提出、实现与研究区验证
利用前文得到的AMSR-E土壤水分和MODIS地表温度LST、归一化植被指数NDVI与地表反照率Albedo等光学因子之间的经验关系式,由MODIS数据推导得到高分辨率相位信息,并代入替换土壤水分数据中的低分辨率相位信息。之后通过傅里叶逆变换算法,即可得到空间分辨率优于被动微波数据分辨率的降尺度土壤水分数据。
通过与蒙古实验区及青藏高原实验区内CEOP计划地面实测数据的比较表明,使用本文方法获取的高分辨率土壤水分数据的数值与变化取值与实测数据吻合较好,证实论文提出的降尺度方法的准确性及可信度较高。
Soil moisture is a key parameter in regulating the hydrothermal energy exchange between land and atmosphere, as well as an important part of the hydrologic cycle of the ecological system on ground surface and an important material source for terrestrial plants and soil organisms. Soil moisture is also an important input parameter of many models, such as climate model, hydrological model, ecological model, land surface model etc. The change of soil moisture will affect the hydrothermal process, then change the surface parameters and impact the climate. Therefore the quantitative monitoring of accurate soil moisture value is of great significance to agricultural production, global change strategy, ecological and environmental protection and many other fields.
Different types of remote sensing platforms are currently used to infer soil moisture at different spatial and temporal scales, each with its specific characteristics and limitations. Compared to thermal infrared and active radar data, passive microwave data gives more accurate inversion of ground surface soil moisture. The passive microwave data, being sensitive to soil moisture information and having a repeat cycle of1to3days, can capture the temporal variation information of soil moisture, although its relatively low spatial resolution (25km-40km) makes it generally suitable for large-scale research. Average soil moisture information of large-scale grid can be obtained from a variety of active and passive microwave remote sensor platform with much ease. While the satellite-borne visible light and thermal infrared data can achieve a medium to high level of resolution (100m-lkm), it's likely to be affected by weather conditions and has a less-than-ideal sensitivity to soil moisture. To overcome these issues, some of the current researches incorporate passive microwave data and optical data, with the purpose of improving both temporal and spatial resolution while achieving reliable soil moisture information.
Choosing Mongolia and the Qinghai-Tibet Plateau region as the study area, this paper improves the algorithm of decomposing large-scale soil moisture via passive microwave radiometer AMSR-E soil moisture data and MODIS surface temperature, NDVI and albedo products.
The four main study points of this paper are shown as follows:
I. Retrieving soil moisture from passive microwave radiometer AMSR-E
The author calculates the25km AMSR-E soil moisture data from passive microwave radiometer AMSR-E data through an improved version of the Qp model. Based on Jackson's single channel algorithm, the author uses37GHz V polarized brightness temperature to calculate the surface temperature, and get vegetation optical thickness from the experience relationship of vegetation water content and NDVI, in order to eliminate the influence of vegetation on the microwave signal. Finally, based on the dual-channel algorithm developed from Qp model, this paper eliminates the impact of surface roughness and obtains the25km soil moisture directly.
II. Decomposing large-scale soil moisture utilizing space spectrum method
This paper uses the method of space spectrum downscaling, which is improved from M. Montopoli's algorithm, to decompose AMSR-E soil moisture data. By breaking down the gray-scale changes function of the soil moisture data into a series of superimposed periodic function using the Fourier transform, the author converts the soil moisture data to frequency domain images of the amplitude and phase through statistical conversion on periodic functions. Due to the fixed relationship between the soil moisture and the spatial frequency of the power spectral density in different spatial resolution images, the exponent relationship of the power spectral density and spatial frequency can be created.
III. Analyzation of the empirical relationship of soil moisture parameters based on the evapotranspiration theory
The paper analyzes the long sequence response mechanism between soil moisture and optical remote sensing parameters, such as surface temperature, vegetation index and surface albedo. This method is based on the triangle Ts-NDVI feature space learning from the evapotranspiration theory and statistical method of multiple linear regression analysis, from which the author gets the empirical relationship between soil moisture and the remote sensing optical factors above.
IV. The method of space spectrum downscaling via high-resolution phase information with its application and field verification
The author retrieves high-resolution phase information from MODIS data via the relationship between AMSR-E soil moisture and MODIS surface temperature, NDVI and albedo, and then substitution to replace soil moisture data the low-resolution phase information. Then by the Fourier inverse transform, spatial resolution better than the resolution of passive microwave data can be obtained by downscaling soil moisture data.
By verifying the results with the CEOP ground data in Mongolia and Qinghai-Tibet Plateau study area, it shows that the high-resolution soil moisture data obtained using the downscaling method introduced in this paper matches the measured data in good agreement. Thus the high accuracy and credibility of the method is confirmed.
引文
1. Agam N., Kustas W.P., Anderson M.C., Li F. and Neale C.M.U. (2007). A vegetation index based technique for spatial sharpening of thermal imagery [J]. Remote Sensing of Environment, Vol.107, 545-558.
2. Albergel, C., Rudiger, C., Carrer, D., Calvet, J.-C, Fritz, N., Naeimi, V., et al. (2009). An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France [J]. Hydrology and Earth System Sciences, Vol.13 (12),115-124.
3. Asner G. P., Lobell D. B. (2000). A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation [J]. Remote Sensing of Environment,2000, Vol.74,99-112.
4. Bajjouk T., Populus J., Guillaumont B. (1998).Quantification of Subpixel Cover Fractions Using Principal Component Analysis and a Linear Programming Method:Application to the Coastal Zone of Roscoff (France).Remote Sensing of Environment [J], Vol.64 (22),153-165.
5. Basharinov, A. and Shutko A., Simulation studies of the SHF radiation characteristics of soils under moist conditions, in NASA Tech. Trans11975, NASA.
6. Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M.(1998). A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL)-2.Validation. Journal of Hydrology [J], Vol.213,198-212.
7. Beckmann P. and Spizzichino A. (1963). The Scattering of Electromagnetic Waves from Rough Surfaces. New York:MacMillan.
8. Bindlish, R., Crow, W. T., Jackson, T. J. (2009). Role of passive microwave remote sensing in improving flood forecasts. IEEE Geoscience and Remote Sensing Letters [J], Vol.6 (1),112-116.
9. Bindlish, R., & Barros, A. P. (2002). Sub-pixel variability of remotely sensed soil moisture:an inter-comparison study of SAR and ESTAR.IEEE Transactions on Geoscience and Remote Sensing [J], Vol.40,326-337.
10. Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., et al. (2011a). Soil moisture estimation through ASCAT and AMSR-E sensors:An intercomparison and validation study across Europe. Remote Sensing of Environment [J], Vol.115 (12),3390-3408.
11. Brocca, L., Melone, F., Moramarco, T., Wagner, W., & Hasenauer, S. (2010a). ASCAT soil wetness index validation through in situ and modeled soil moisture data in central Italy. Remote Sensing of Environment [J], Vol.114 (11),2745-2755.
12. Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., et al. (2010b).Improving runoff prediction through the assimilation of the ASCAT soil moisture product. Hydrology and Earth System Sciences [J], Vol.14 (10),1881-1893.
13. Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S., & Hahn, S.(2011b). Assimilation of surface and root-zone ASCAT soil moisture products into rainfall-runoff modelling. IEEE Transactions on Geoscience and Remote Sensing [J], Vol.99,1-14.
14. Carlson, T.N., Dodd, J.K., Benjamin, S.G., Cooper, J.N. (1981). Remote Estimation of Surface Energy Balance, Moisture Availability and Thermal Inertia. J. Appl. Meteor. [J], Vol.20,67-87.
15. Carlson, T.N., Rose, F.G., Perry, E.M. (1984). Regional Scale Estimates of Surface Moisture Availability from GOES Satellite. Agron. J. [J], Vol.76,972-979.
16. Carlson T.N., Gillies R.R., Perry E.M.(1994). A Method to Make Use of Thermal Infrared Temperature and NDVI measurements to Infer Surface Soil Water Content and Fractional Vegetation Cover. Remote Sensing Reviews [J], Vol.9,161-173.
17. Carlson T.N., Gillies R.R., Schmugge T.J. (1995).An Interpretation of Methodologies for Indirect Measurement of Soil-Water Content. Agricultural and Forest Meteorology [J], Vol.77,191-205.
18. Carlson, T.N., Ripley, D.A.J. (1997). On the Relationship Between NDVI, Fractional Vegetation Cover and Leaf Area Index. Remote Sensing Environment [J], Vol.62,241-252.
19. Carlson, T., (1998), Personal communication.
20. Carlson, T. (2007). An overview of the "triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors [J], Vol.7 (8),1612-1629.
21. Chanzy A., Schmugge T. J., Calvet J. C., Kerr Y., Van Oevelen P., Grosjean O., and Wang J. R. (1997). Airborne microwave radiometry on a semi-arid area during HAPEX-Sahel. Journal of Hydrology [J], Vol.188,285-309.
22. Chauhan N. S., Miller S., Ardanuy P. (2003).Spaceborne soil moisture estimation at high resolution:a microwave optical/IR synergistic approach. International Journal of Remote Sensing [J], Vol.24, 4599-4622.
23. Chauhan N. S., Le Vine D. M., and Lang R. H. (1994).Discrete scatter model for microwave radar and radiometer responseto corn:comparison of theory and data. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.32,416-426.
24. Chavez, P.S. Jr., Sides, S.C., Anderson, J.A. (1991).Comparison of three different methods to merge multi-resolution and multi-spectral data:LANDSAT TM and SPOT panchromatic. Photogrammetric Engineering & Remote Sensing[J], Vol.57 (3),295-303.
25. Choi M., Jacobs J. M. (2008a). Temporal variability corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) surface soil moisture:Case study in Little River Region, Georgia, U.S. Sensors[J], Vol.8,2617-2627.
26. Choi M., Jacobs J. M., Bosch D. D. (2008b). Remote sensing observatory validation of surface soil moisture using Advanced Microwave Scanning Radiometer E, Common Land Model, and ground based data:Case study in SMEX03 Little River Region.Georgia, U.S.. Water Resources Research[J], Vol.44, W08421.doi:10.1029/2006WR005578.
27. Choi, M. (2012). Evaluation of multiple surface soil moisture for Korean regional flux monitoring network sites:Advanced Microwave Scanning Radiometer E, land surface model, and ground measurements. Hydrological Processes[J], Vol.26 (4),597-603.
28. Cracknell, A., Xue, Y. Thermal inertia determination from space-a tutorial review (1996), International Journal of Remote Sensing[J], Vol.17 (3),431-461.
29. De Jeu, R. A. M., Wagner,W., Holmes, T. R. H., Dolman, A. J., Van De Giesen, N. C., & Friesen, J. (2008). Global soil moisture patterns observed by space borne microwave radiometers and scatterometers. Surveys in Geophysics[J], Vol.29,399-420.
30. De Jeu, R.A.M. and M. Owe (2003). Further validation of a new methodology for surface moisture and vegetation optical depth retrieval. International Journal of Remote Sensing [J], Vol.24 (22),4559-4578.
31. Deardorff, J. Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation (1978). Journal of Geophysical Research[J], Vol.83 (4),1889-1903.
32. DeFries R., Hansen M., Steininger M., Dubayah R., Sohlberg R., Townshend J. (1997). Subpixel forest cover in central Africa from multisensor, multitemporal data. Remote Sensing of Environment[J], Vol.60 (3),228-246.
33. DeFries R. S., Hansen M. C., Townshend J. R. G., Janetos A. C., Loveland T. R. (2000). A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biology[J], Vol.6(2), 247-254.
34. Dobson M. C., Ulaby F. T., Hallikainen M. T., and El-Rayes M. A (1985), Microwave dielectric behavior of wet soil-Part Ⅱ:Dielectric mixing models, IEEE Transactions on Geoscience and Remote Sensing[J], pp.35-46.
35. Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., et al. (2010). Error characterisation of global active and passive microwave soil moisture datasets. Hydrology and Earth System Sciences[J], Vol.14 (12),2605-2616.
36. Doubkova, M., Sabel, D., Pathe, C., Hasenauer, S., Bartsch, A., & Wagner, W. (2008). High resolution soil moisture from radar instruments:Study area of northeast Austria and southeast Czech Republic. HydroPredict[J],pp.89-92.
37. Erkin Sidick. Power Spectral Density Specification and Analysis of Large Optical Surfaces. Proc. SPIE 7390, Modeling Aspects in Optical Metrology Ⅱ,73900L (June 15,2009); doi:10.1117/12.823844.
38. Falkenmark, M., & Rockstrom, J. (2004). Balancing water for humans and nature:The new approach in ecohydrology. U.K:Earthscan.
39. Ferrazzoli P., Guerriero L., Paloscia S., Pampaloni P., and Solimini D. (1992). Modeling polarization properties of emission from soil covered with vegetation. IEEE Transactions on Geoscience and Remote Sensing[J], vol.30,157-165.
40. Fischer A. (1994a). A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sensing of Environment[J],Vol.48 (2),220-230.
41. Fischer A. (1994b). A simple model for the temporal variations of NDVI at regional scale over agricultural countries. Validation with ground radiometric measurements. International Journal of Remote Sensing[J], Vol.15 (7),1421-1446.
42. Fung A. K., Theory of cross-polarized power returned from a random surface (1967), Applied Science Research[J], Vol.18,50-60.
43. Fung A. K., L. Zong Qian, et al. (1992). Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.30,356-369.
44. Gaskin G.J., Miller J.D. (1996).Measurement of soil water content using a simplified impedance measuring technique. Journal of Agricultural Engineering Research[J], Vol.63 (2),153-159.
45. Gillies R.R., Carlson T.N. (1995).Thermal Remote Sensing of Surface Soil Water Content with Partial Vegetation Cover for Incorporation into Climate Models. Journal of Applied Meteorology[J],Vol.34, 745-756.
46. Gillies R.R., Carlson T.N.,Cui J, Kustas W.P., Humes K. S. (1997).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. International Journal of Remote Sensing[J], Vol.18,3145-3166.
47. Giorgi, Filippo(1990). Simulation of Regional Climate Using a Limited Area Model Nested in a General Circulation Model. Journal of Climate[J], Vol.3(9):941-964.
48. Gruhier C., de Rosnay P., Kerr Y., Mougin E., Ceschia E., Calvet J. C. (2008). Evaluation of AMSR-E soil moisture product based on ground measurements over temperate and semi-arid regions. Geophysical Research Letters[J], Vol.35, L10405. doi:10.1029/2008GL033330.
49. Gruhier C., de Rosnay P., Hasenauer S., Holmes T., de Jeu R., Kerr Y., et al. (2010). Soil moisture active and passive microwave products:intercomparison and evaluation over a Sahelian site. Hydrology and Earth System Sciences[J], Vol.14,141-156.
50. Hemakumara M., Kalma J., Walker J., Willgoose G. (2004). Downscaling of low resolution passive microwave soil moisture observations. In A. J. Teuling, H.55.Leijnse, P. A. Troch, J. Sheffield, & E. F. Wood (Eds.), Proceedings of the 2nd International CAHMDA Workshop on:The Terrestrial Water Cycle:Modeling and Data Assimilation Across Catchment Scales (pp.25-27). Princeton, NJ, October.
51. Holmes, T.R.H., et al. (2009), Land surface temperature from Ka band (37 GHz) passive microwave observations. Journal of Geophysical Research-Atmospheres[J], Vol.114.
52. Hornbuckle B. K., England A. W., De Roo R. D., Fischman M. A., and Boprie D. L. (2003), Vegetation canopy anisotropy at 1.4 GHz, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.41, 2211-2223.
53. Hossain, A. K. M. A., & Easson, G. (2008). Evaluating the potential of vi-lst triangle model for quantitative estimation of soil moisture using optical imagery.2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings,3. (pp. Ⅲ879-Ⅲ882).
54. Hughes J. P., Guttorp P. (1994).A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. WATER RESOURCES RESEARCH[J], Vol.30 (5),1535-1546.
55. Idso, S., Jackson, R., Reginato, R. Compensating for environmental variability in the thermal inertia approach to remote sensing of soil moisture (1976). Journal of Applied Meteorology[J], Vol.15 (8) 811-817.
56. Immerzeel W.W., Rutten M, Droogers P (2009). Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula, Remote Sensing of Environment[J]. Vol.113 (2), 362-370.
57. Jackson.R.D., Slaler.P.N., and Pinter.P.J (1983), Discrimination of growth and water stress in wheat by various Vegetation indices through dear and turbid atmosphere, Remote Sensing of Environment[J], Vol.13,187-208.
58. Jackson, T. J. (1993). Ⅲ. Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes[J], Vol.7 (2),139-152.
59. Jackson, T. J., Cosh, M. H., Bindlish, R., Starks, P. J., Bosch, D. D., Seyfried, M., et al. (2010) Validation of advanced microwave scanning radiometer soil moisture products. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.48 (12),4256-4272.
60. Jackson, T. J., & Schmugge, T. J. (1995). Surface soil moisture measurement with microwave radiometry. Acta Astronautica[J], Vol.35 (7),477-482.
61. Jackson, T.J. and Schmugge T.J. (1991), Vegetation Effects on the Microwave Emission of Soils. Remote Sensing of Environment[J], Vol.36 (3),203-212.
62. Jackson, T.J., Schmugge T.J., and Wang J.R. (1982), Passive Microwave Sensing of Soil Moisture under Vegetation Canopies. Water Resources Research[J], Vol.18 (4),1137-1142.
63. Jackson, T.J., et al. (1999), Soil moisture mapping at regional scales using microwave radiometry:The Southern Great Plains Hydrology Experiment. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.37 (5),2136-2151.
64. Jiang L., Islam S. (1999). A Methodology for Estimation of Surface Evapotranspiration over Large Areas using Remote Sensing Observations. Geophysical Research Letters[J], Vol.26,2773-2776.
65. Jiang L., Islam S. (2001). Estimation of Surface Evaporation Map over Southern Great Plains Using Remote Sensing Data. Water Resources Research[J], Vol.37,329-340.
66. Jiang L., Islam S. (2003). An Intercomparison of Regional Latent Heat Flux Estimation using Remote Sensing Data. International Journal of Remote Sensing[J], Vol.24,2221-2236.
67. Karam M. A. and Fung A. K. (1988), Electromagnetic scattering from a layer of finite length, randomly oriented, dielectric, circular cylinders over a rough interface with application to vegetation, International Journal of Remote Sensing[J], Vol.9,1109-1134.
68. Karam M. A. and Fung A. K. (1989), Leaf-shape effects in electromagnetic wave scattering from vegetation," IEEE Transactions on Geoscience and Remote Sensing[J], Vol.27,687-697.
69. Karam M. A., Aerojet G. C. and Azusa C. A. (1997), A physical model for microwave radiometry of vegetation, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.35,1045-1058.
70. Kim G., Barros A. P. (2002) Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data. Remote Sensing of Environment[J], Vol.83 (3),400-413.
71. Kim, J., & Hogue, T. S. (2012). Improving spatial soil moisture representation though integration of AMSR-E and MODIS products. IEEE Transactions on Geoscience and Remote Sensing[J],50(2),446-460.
72. Kirdiashev, K., Chukhlantsev A., Shutko A. (1979), Microwave radiation of the earth's surface in the presence of vegetation cover. Radio Eng. Electron. Engl. Transl[J],Vol.24,256-264.
73. Kogan F.N. (1997). Global drought watch from space, Bulletin of the American Meteorological Society[J], Vol.78,621-636.
74. Koike, T., et al., Soil moisture algorithm development and validation for the ADEOS-II/AMSR. Igarss 2000:IEEE 2000 International Geoscience and Remote Sensing Symposium, Vol I-Vi, Proceedings, 2000:p.1253-1255.
75. Koster R. and Suarez M., Energy and water balance calculations in the MOSAIC LSM, Goddard Space Flight Center, Greenbelt, MD, NASA Tech. Memo 104606,1996, vol.9.
76. Kustas W.P. et al.(1993), Relationships between Evaporative Fraction and Remotely-Sensed Vegetation Index and Microwave Brightness Temperature for Semiarid Range lands. Journal of Applied Meteorology[J], Vol.32 (12),1781-1790.
77. Kustas W.P. (1990), Estimates of Evapotranspiration with a One- and Two-layer Model of Heat Transfer Over Partial Canopy Cover. J. Appl., Meteor.[J], Vol.29,205-223.
78. Kustas W.P., Norman J.M. (1996), Use of Remote Sensing for Evapotranspiration Monitoring Over Land Surfaces. Hydrological Sciences Journal[J], Vol.41,495-516.
79. Lakhankar T., Krakauer N., Khanbilvardi R. (2009). Application of microwave remote sensing of soil moisture for agricultural applications. International Journal of Terraspace Science and Engineering[J], Vol.2 (1),81-91.
80. Lambin E.G.,Ehrlich D. (1996). The Surface Temperature-vegetation Index Space for Land Cover and Land-cover Change Analysis. International Journal of Remote Sensing[J], Vol.17,463-487.
81. Le Vine D. M., Karam M. A., Center N. and Greenbelt M. D. (1996), Dependence of attenuation in a vegetation canopy on frequency and plant water content, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.34,1090-1096.
82. Liang X., Wood E., and Lettenmaier D.(1996), Surface and soil moisture parameterization of the VIC-2L model:Evaluation and modifications, Global Planet. Change[J], Vol.13,195-206.
83. Margulis S., McLaughlin D., Entekhabi D., and Dunne S. (2002), Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 field experiment, Water Resour Res.[J], Vol.38,1299, DOI:10.1029/2001WR001114 2002.
84. Mario Montopoli, Nazzareno Pierdicca, and Frank S. Marzano. Spectral Downscaling of Integrated Water Vapor Fields from Satellite Infrared Observations. IEEE Transactions on Geoscience and Remote Sensing [J], Vol.50,415-428,2012.
85. Matzler C. and Sume A. (1989), Microwave radiometry of leaves, Microwave Radiometry and Remote Sensing Applications[J],133-148.
86. McDonald K. C., Dobson M. C., and Ulaby F. T. (1990), Using MIMICS to model L-band multiangle and multitemporal backscatter from a walnut orchard, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.28,477-491.
87. Marina Stathopoulou, Constantinos Cartalis (2009).Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation. Remote Sensing of Environment[J],Vol.113 (12),2592-2605.
88. Mearns L. O., Rosenzweig C., Goldberg R. (1996).The effect of changes in daily and interannual climatic variability on CERES-Wheat:A sensitivity study. CLIMATIC CHANGE[J],Vol.32 (3),257-292.
89. Mecikalski J.R., Diak G.R., Anderson M.C., Norman J.M. (1999), Estimating Fluxes on Continental Scales using Remotely Sensed Data in an Atmospheric-land Exchange Model. J. Appl. Meteor.[J],Vol. 38,1352-1369.
90. Merlin O., Chehbouni A.G., Kerr Y.H., Njoku E.G., Entekhabi D. (2005).A combined modeling and multispectral/multiresolution remote sensing approach for disaggregation of surface soil moisture: application to SMOS configuration. Geoscience and Remote Sensing, IEEE Transactions [J], Vol.43(9), 2036-2050.
91. Merlin O., Chehbouni A., Kerr Y.H., Goodrich D.C. (2006).A downscaling method for distributing surface soil moisture within a microwave pixel:Application to the Monsoon '90 data. Remote Sensing of Environment, Vol.101 (3),379-389.
92. Merlin, O., Walker, J. P., Chehbouni, A., & Kerr, Y. (2008). Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency.Remote Sensing of Environment[J], Vol.112 (10),3935-3946.
93. Milly, P. C. D., Dunne, K. A., & Vecchia, A. V. (2005). Global pattern of trends in stream flow and water availability in a changing climate. Nature[J], Vol.438 (7066),347-350.
94. Mitchell K., Lin Y., Rogers E., Marshall C., M. Ek, J. Lohmann D., Schaake D., Tarpley P., Grunmann G., Manikin Q., and Duan V., Recent GCIP-sponsored advancements in coupled landsurface modeling and data assimilation in the NCEP Eta mesoscale model, in Proc.15th AMS Conf. Hydrol, Amer. Meteor. Soc., Long Beach, CA,2000, pp.180-183, Preprints.
95. Mo T., Choudhury B. J., Schmugge T. J., Wang J. R. and Jackson T. J. (1982), A model for microwave emission from vegetation-covered fields, Journal of Geophysical Research[J], Vol.87,11229-11237.
96. Mo T. and Schmugge T. J. (1987), A parameterization of the effect of surface roughness on microwave emission, IEEE Transactions on Geoscience and Remote Sensing[J],481-486.
97. Nemani R.R., Running S.W. (1989), Estimation of Regional Surface-Resistance to Evapotranspiration from NDVI and Thermal-IR AVHRR Data. Journal of Applied Meteorology[J],Vol.28,276-284.
98. Njoku, E.G. and Kong J.-A.(1977), Theory for Passive Microwave Remote Sensing of Near-Surface Soil Moisture. J. Geophys.Res.[J], Vol.82 (20),3108-3118.
99. Njoku, E.G. and Entekhabi D. (1996), Passive microwave remote sensing of soil moisture. Journal of Hydrology[J],Vol.184 (1-2),101-129.
100. Njoku E.G. and Chan S.K. (2006), Vegetation and surface roughness effects on AMSR-E land observations. Remote Sensing of Environment[J], Vol.100 (2),190-199.
101. Owe, M., De Jeu, R., & Walker, J. (2001). A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.39 (8),1643-1654.
102. Owe M. and Van De Griend A.A. (2001), On the relationship between thermodynamic surface temperature and high-frequency (37 GHz) vertically polarized brightness temperature under semi-arid conditions. International Journal of Remote Sensing[J], Vol.22 (17),3521-3532.
103. Owe M., de Jeu R. and Walker J. (2001), A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.39 (8):p.1643-1654.
104. Owen T.W., Carlson T.N.,Gillies R.R. (1998). An Assessment of satellite Remotely-sensed Land Cover Parameters in Quantitatively Describing the Climatic Effect of Urbanization. International Journal of Remote Sensing[J], Vol.19,1663-1681.
105. Pampaloni P. and Paloscia S. (1986), Microwave emission and plant water content:A comparison between field measurements and theory, IEEE Transactions on Geoscience and Remote Sensing[J], 900-905.
106. Pellenq J., Kalma J., Boulet G., Saulnier G. M., Wooldridge S., Kerr Y., Chehbouni A. (2003). A disaggregation scheme for soil moisture based on topography and soil depth. Journal of Hydrology[J], Vol.276,112-127.
107. Petropoulis G.P., Wooster M.J., Drake N.J. (2006), Investigating the Performance of a Coupled SVAT/Model Remote Sensing Method to Derive Spatially Explicit Maps of Land Atmosphere Energy Fluxes. Geophysical Research Abstracts,0634, European Geosciences Union, SRef ID:1607-7962.
108. Piles M., Camps A., Vall-Llossera M., Corbella I., Panciera R., Rudiger C., et al. (2011).Downscaling SMOS-derived soil moisture using MODIS visible/infrared data. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.49 (9),3156-3166.
109. Pratt,W.K. Digital Image Processing,Wiley,1991.Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.,1996. Numerical Recipesin C. Cambridge University Press,641 pp.
110. Price, J. Thermal inertia mapping:a new view of the earth (1977). Journal of Geophysical ResearchfJ], Vol.82 (18),2582-2590.
111. Price J.C. (1980), The Potential of Remotely Sensed Thermal Infrared Data to Infer Surface Soil Moisture and Evaporation. Water Resources Research[J], Vol.16,787-795.
112. Price J.C. (1982), Estimation of Regional Scale Evapotranspiration through Analysis of Satellite thermal-infrared Data. IEEE Transactions on Geoscience and Remote Sensing [J], Vol.GE-20,286-292.
113. Price J.C. (1985). On the analysis of thermal infrared imagery:The limited utility of apparent thermal inertia. Remote Sensing of Environment J], Vol.18,59-73.
114. Price J.C. (1990).Using Spatial Context in Satellite Data to Infer Regional Scale Evapotranspiration. IEEE Transactions on Geoscience and Remote Sensing[J].Vol.28,940-948.
115. Price, J.C. (1999). Combining multispectral data of differing spatial resolution. IEEE Transactions on Geoscience and Remote Sensing[J],Vol.37 (3),1199-1203.
116. Quarmby N. A., Milnes M., Hindle T. L., Silleos N. (1993). The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International Journal of Remote Sensing[J], Vol.14 (2),199-210.
117. Ray R. L., Jacobs J. M., & Cosh M. H. (2010). Landslide susceptibility mapping using downscaled AMSR-E soil moisture:A case study from Cleveland Corral, California, US. Remote Sensing of Environment[J], Vol.114 (11),2624-2636.
118. Reichle R. H., McLaughlin D. B., and Entekhabi D. (2002), Hydrologic data assimilation with the ensemble Kalman filter, Monthly Weather Rev.[J], Vol.130,103-114.
119. Rudiger C., Calvet J. C., Gruhier C., Holmes T. R. H., de Jeu R. A. M., & Wagner W. (2009). An intercomparison of ERS-Scat and AMSR-E soil moisture observations with model simulations over France. Journal of Hydrometeorology[J], Vol.10 (2),431-447.
120. Sahoo A. K., Houser P. R., Ferguson C.,Wood E. F., Dirmeyer P. A., Kafatos M. (2008).Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River experimental Watershed, Georgia. Remote Sensing of Environment[J],Vo.112,3142-3152.
121. Sandholt I., Rasmussen K., Andersen J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment[J], Vol.79 (22),213-224.
122. Sancer M. I. (1969), Shadow-corrected electromagnetic scattering from a randomly rough surface, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.17,577-589.
123. Schmugge T. (1983). Remote sensing of soil moisture with microwave radiometers, Transactions of the American Society of Agricultural Engineers[J], Vol.26 (1),51-61.
124. Schmugge T. J., Kustas W. P., Ritchie J., Jackson T. J., Rango A. (2002).Remote sensing in hydrology,Advances in Water Resources[J], Vol.25,1367-1385.
125. Seguin B., Itier B. (1983), Using Midday Surface-Temperature to Estimate Daily Evaporation from Satellite Thermal Ir Data, International Journal of Remote Sensing[J], Vol.4,371-383.
126. Shengli, W., & Xiaoxiang, Z. (2010). Using AMSR-E land product to monitor the drought process in China.2010 30th IEEE International Geoscience and Remote Sensing Symposium, (pp.3894-3897)
127. Shi J.C., et al. (2005), A parameterized multifrequency-polarization surface emission model, IEEE Transactions on Geoscience and Remote Sensing[J],Vol.43 (12),2831-2841.
128. Shi J.C., et al. (2006), Physically based estimation of bare-surface soil moisture with the passive radiometers, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.44 (11),3145-3153.
129. Shimshi, D. Effect of soil moisture and phenylmercuric acetate upon stomatal aperture, transpiration, and photosynthesis (1963). Plant Physiology[J], Vol.38 (6),713-721.
130. Smith, S. W. (1997). The Scientist and Engineer's Guide to Digital Signal Processing [M]. California Technical Publishing. ISBN 0-9660176-3-3.
131.Soer G.J.R. (1980), Estimation of Regional Evapotranspiration and Soil Moisture Conditions using Remotely Sensed Crop Surface Temperatures, Remote Sensing Environment J], Vol.9,27-45.
132. Stewart. J.B., Kustas W.P., Humes K.S., Nichols W.D., Moran M.S., De Bruin H.A.R. (1994),Sensible Heat Flux-Radiant Surface Temperature Relationship for Semiarid Areas. J. Appl.Meteor.[J], Vol.33, 1110-1117.
133. Townshend J. R. G., Justice C. O.(1988). Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations. International Journal of Remote Sensing[J], Vol.9 (2), 187-236.
134. Tsang L., J. Kong A., and Shin R. T., Theory of microwave remote sensing:Wiley New York,1985.
135. Ulaby F. T., Moore R. K., and Fung A. K., Microwave Remote Sensing vol.2. Dedham, MA:Artech House,1982.
136. Ulaby F. T., Razani M., and Dobson M. C. (1983), Effects of vegetation cover on the microwave radiometric sensitivity to soil moisture, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.GE-21 (1),51-61.
137. Ulaby F. T., Sarabandi K., McDonald K., Whitt M., and Dobson M. C. (1990), Michigan microwave canopy scattering model, International Journal of Remote Sensing[J], Vol.11,1223-1253.
138. Vachaud, G., Passerat De Silans, A., Balabanis, P., & Vauclin, M. (1985). Temporal stability of spatially measured soil water probability density function. Soil Science Society of America Journal[J], Vol.49 (4),822-828.
139. Valenzuela G. R. (1967), Depolarization of EM waves by slightly rough surfaces, IEEE Transactions on Geoscience and Remote Sensing[J],Vol.15,552-557.
140. Van de Griend A. A., Owe M., De Ruiter J., and Gouweleeuw B. T. (1996), Measurement and behavior of dual-polarization vegetation optical depth and single scattering albedo at 1.4-and 5-GHz microwave frequencies, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.34,957-965.
141. Van de Griend A. A. and Wigneron J. P. (2004), The b-factor as a function of frequency and canopy type at H-polarization, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.42,786-794.
142. Wagner, W., Lemoine, G., & Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment[J], Vol.70 (2),191-207.
143. Wagner, W., Pathe, C., Doubkova, M., Sabel, D., Bartsch, A., Hasenauer, S., et al. (2008).Temporal stability of soil moisture and radar backscatter observed by the Advanced Synthetic Aperture Radar (ASAR). Sensors[J],Vol.8 (2),1174-1197.
144. Walker, J. P., & Houser, P. R. (2004). Requirements of a global near-surface soil moisture satellite mission:Accuracy, repeat time, and spatial resolution. Advances in Water Resources[J],Vol.27 (8),785-801.
145. Wang J. R. and Choudhury B. J. (1981), Remote sensing of soil moisture content over bare fields at 1.4 GHz frequency, Journal of Geophysical Research[J], Vol.86 (C6),5277-5282.
146. Wang J. R., P. E. O'Neill, Jackson T. J., Engman E. T. (1983), Multifrequency measurements of the effects of soil moisture, soil texture, and surface roughness, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.GE-21 (1),44-51.
147. Watson, K. (1971), Applications of Thermal Modeling in the Geological Interpretation of IR Images. Proceedings of the Seventh Int. Symposium of Remote Sensing of Environment. ERIM. Ann Arbor, MI. 2017-2041.
148. Wegmuller U. and Matzler C. (1999), Rough bare soil reflectivity model, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.37 (3),1391-1395.
149. Wetzel, P., Atlas, D., Woodward, R. (1983), Determining Soil Moisture from Study, J. Climate and Applied Meteor[J]. Vol.23,375-391.
150. Wigneron J. P., Parde M., Waldteufel P., Chanzy A., Kerr Y., Schmidl S., and Skou N. (2004) Characterizing the dependence of vegetation model parameters on crop structure, incidence angle, and polarization at L-band, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.42,416-425.
151. Wigneron J. P., Calvet J. C., Chanzy A., Grosjean O., and Laguerre L. (1995), A composite discrete-continuous approach to model the microwaveemission of vegetation, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.33,201-211.
152. Wigneron, J.P. et al. (1998), Use of passive microwave remote sensing to monitor soil moisture. Agronomie[J], Vol.18 (1),27-43.
153. Wilby R.L.,Wigley T.M.L. (1997). Downscaling general circulation model output:a review of methods and limitations. Progress in Physical Geography [J], Vol.21 (4),530-548.
154. Woodcock C. E., Strahler A. H. (1987). The factor of scale in remote sensing. Remote Sensing of Environment[J],Vol.21 (3),311-332.
155. Yu, G., Di, L., & Yang, W. (2008). Downscaling of global soil moisture using auxiliary data.2008 IEEE International Geoscience and Remote Sensing Symposium—Proceedings,3. (pp. Ⅲ230-Ⅲ233) Boston, MA.
156. Zhan X., Houser P. R., Walker J. P., Crow W. T. (2006).A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations. IEEE Transactions on Geoscience and Remote Sensing [J], Vol.44 (6),1534-1544.
157. Zhan, X., Miller, S., Chauhan, N., Di, L., & Ardanuy, P. (2002). Soil moisture visible/infrared radiometer suite algorithm theoretical basis document. Lanham, MD:Raytheon Syst.Company.
158. Zheng, W., Liu, C., Xin, Z., & Wang, Z. (2008). Flood and waterlogging monitoring over Huaihe River basin by AMSR-E data analysis. Chinese Geographical Science[J], Vol.18(3),262-267.
159. Zhukov B., Oertel D., Lanzl, F., Reinhackel G. (1999). Unmixing-based multisensor multiresolution image fusion. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.37 (3),1212-1226.
160.白厚义.回归设计及多元统计分析[M].广西科学技术出版社.11,2003.
161.陈亮.SMOS土壤水分反演算法研究[D].中国科学院博士学位论文,2009.
162.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,环境遥感,9(2)：106-112,1994.
163.丁一.基于MODIS数据和气象数据的植被水监测研究[D].首都师范大学博士学位论文,2011.
164.杜今阳,多极化雷达反演植被覆盖地表土壤水分研究[D],中国科学院博士学位论文,2006.
165.江学顶,黄锦灿,郭泺等.生态边界层水热交换模型及模拟研究进展[J].中山大学学报(自然科学版).S1,2005.
166.刘明亮,唐先明,刘纪远等.基于1km的空间网格数据尺度效应研究.遥感学报,5(3)：183-190,2001.
167.李小文,王锦地,Strahler A.H. (2000)尺度效应及几何光学模型用于尺度纠正.中国科学(E辑),30(增刊)：12-17.
168.刘昌明,孙睿.水循环的生态学方面：土壤-植被-大气系统水分能量平衡研究进展[J].水科学进展.3,1999.
169.马耀明,姚檀栋,王介民等.青藏高原复杂地表能量通量研究.地球科学进展,21(12)：1215-1223,2006.
170.施建成,杜阳,杜今阳等.微波遥感地表参数反演进展.中国科学：地球科学,42(6)：814-842.2012.
171.田国良,杨希华,东小麦旱情遥感监测模型研究,环境遥感,7(2)：83-89,1992.
172.武胜利.基于TRMM的主被动微波遥感结合反演土壤水分算法研究[D].中国科学院博士学位论文,2006.
173.肖斌,沙晋明.土壤水分遥感反演方法概述[J].遥感信息.12,2007.
174.辛晓洲.用定量遥感方法计算地表蒸散[D].中国科学院博士学位论文,2003.
175.徐金鸿,徐瑞松,夏斌等.土壤遥感监测研究进展[J].水土保持研究.4,2006.
176.王万同.基于遥感技术的区域地表蒸散估算研究[D].河南大学博士学位论文,2011.
177.赵逸舟,马耀明,马伟强等.藏北高原土壤温湿变化特征分析.冰川冻土.29(4)：578-583,2007.
178.扎西德赞.西藏气候概述[EB/OL].中国西藏新闻网.http://www.chinatibetnews.com/dili/2002-08/13/content_339460.htm.8,2001.
179.朱新国,林方存,高湖滨.墒情监测研究进展综述[J].节水灌溉.11,2011.
2. Albergel, C., Rudiger, C., Carrer, D., Calvet, J.-C, Fritz, N., Naeimi, V., et al. (2009). An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France [J]. Hydrology and Earth System Sciences, Vol.13 (12),115-124.
3. Asner G. P., Lobell D. B. (2000). A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation [J]. Remote Sensing of Environment,2000, Vol.74,99-112.
4. Bajjouk T., Populus J., Guillaumont B. (1998).Quantification of Subpixel Cover Fractions Using Principal Component Analysis and a Linear Programming Method:Application to the Coastal Zone of Roscoff (France).Remote Sensing of Environment [J], Vol.64 (22),153-165.
5. Basharinov, A. and Shutko A., Simulation studies of the SHF radiation characteristics of soils under moist conditions, in NASA Tech. Trans11975, NASA.
6. Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M.(1998). A Remote Sensing Surface Energy Balance Algorithm for Land (SEBAL)-2.Validation. Journal of Hydrology [J], Vol.213,198-212.
7. Beckmann P. and Spizzichino A. (1963). The Scattering of Electromagnetic Waves from Rough Surfaces. New York:MacMillan.
8. Bindlish, R., Crow, W. T., Jackson, T. J. (2009). Role of passive microwave remote sensing in improving flood forecasts. IEEE Geoscience and Remote Sensing Letters [J], Vol.6 (1),112-116.
9. Bindlish, R., & Barros, A. P. (2002). Sub-pixel variability of remotely sensed soil moisture:an inter-comparison study of SAR and ESTAR.IEEE Transactions on Geoscience and Remote Sensing [J], Vol.40,326-337.
10. Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., et al. (2011a). Soil moisture estimation through ASCAT and AMSR-E sensors:An intercomparison and validation study across Europe. Remote Sensing of Environment [J], Vol.115 (12),3390-3408.
11. Brocca, L., Melone, F., Moramarco, T., Wagner, W., & Hasenauer, S. (2010a). ASCAT soil wetness index validation through in situ and modeled soil moisture data in central Italy. Remote Sensing of Environment [J], Vol.114 (11),2745-2755.
12. Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., et al. (2010b).Improving runoff prediction through the assimilation of the ASCAT soil moisture product. Hydrology and Earth System Sciences [J], Vol.14 (10),1881-1893.
13. Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S., & Hahn, S.(2011b). Assimilation of surface and root-zone ASCAT soil moisture products into rainfall-runoff modelling. IEEE Transactions on Geoscience and Remote Sensing [J], Vol.99,1-14.
14. Carlson, T.N., Dodd, J.K., Benjamin, S.G., Cooper, J.N. (1981). Remote Estimation of Surface Energy Balance, Moisture Availability and Thermal Inertia. J. Appl. Meteor. [J], Vol.20,67-87.
15. Carlson, T.N., Rose, F.G., Perry, E.M. (1984). Regional Scale Estimates of Surface Moisture Availability from GOES Satellite. Agron. J. [J], Vol.76,972-979.
16. Carlson T.N., Gillies R.R., Perry E.M.(1994). A Method to Make Use of Thermal Infrared Temperature and NDVI measurements to Infer Surface Soil Water Content and Fractional Vegetation Cover. Remote Sensing Reviews [J], Vol.9,161-173.
17. Carlson T.N., Gillies R.R., Schmugge T.J. (1995).An Interpretation of Methodologies for Indirect Measurement of Soil-Water Content. Agricultural and Forest Meteorology [J], Vol.77,191-205.
18. Carlson, T.N., Ripley, D.A.J. (1997). On the Relationship Between NDVI, Fractional Vegetation Cover and Leaf Area Index. Remote Sensing Environment [J], Vol.62,241-252.
19. Carlson, T., (1998), Personal communication.
20. Carlson, T. (2007). An overview of the "triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors [J], Vol.7 (8),1612-1629.
21. Chanzy A., Schmugge T. J., Calvet J. C., Kerr Y., Van Oevelen P., Grosjean O., and Wang J. R. (1997). Airborne microwave radiometry on a semi-arid area during HAPEX-Sahel. Journal of Hydrology [J], Vol.188,285-309.
22. Chauhan N. S., Miller S., Ardanuy P. (2003).Spaceborne soil moisture estimation at high resolution:a microwave optical/IR synergistic approach. International Journal of Remote Sensing [J], Vol.24, 4599-4622.
23. Chauhan N. S., Le Vine D. M., and Lang R. H. (1994).Discrete scatter model for microwave radar and radiometer responseto corn:comparison of theory and data. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.32,416-426.
24. Chavez, P.S. Jr., Sides, S.C., Anderson, J.A. (1991).Comparison of three different methods to merge multi-resolution and multi-spectral data:LANDSAT TM and SPOT panchromatic. Photogrammetric Engineering & Remote Sensing[J], Vol.57 (3),295-303.
25. Choi M., Jacobs J. M. (2008a). Temporal variability corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) surface soil moisture:Case study in Little River Region, Georgia, U.S. Sensors[J], Vol.8,2617-2627.
26. Choi M., Jacobs J. M., Bosch D. D. (2008b). Remote sensing observatory validation of surface soil moisture using Advanced Microwave Scanning Radiometer E, Common Land Model, and ground based data:Case study in SMEX03 Little River Region.Georgia, U.S.. Water Resources Research[J], Vol.44, W08421.doi:10.1029/2006WR005578.
27. Choi, M. (2012). Evaluation of multiple surface soil moisture for Korean regional flux monitoring network sites:Advanced Microwave Scanning Radiometer E, land surface model, and ground measurements. Hydrological Processes[J], Vol.26 (4),597-603.
28. Cracknell, A., Xue, Y. Thermal inertia determination from space-a tutorial review (1996), International Journal of Remote Sensing[J], Vol.17 (3),431-461.
29. De Jeu, R. A. M., Wagner,W., Holmes, T. R. H., Dolman, A. J., Van De Giesen, N. C., & Friesen, J. (2008). Global soil moisture patterns observed by space borne microwave radiometers and scatterometers. Surveys in Geophysics[J], Vol.29,399-420.
30. De Jeu, R.A.M. and M. Owe (2003). Further validation of a new methodology for surface moisture and vegetation optical depth retrieval. International Journal of Remote Sensing [J], Vol.24 (22),4559-4578.
31. Deardorff, J. Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation (1978). Journal of Geophysical Research[J], Vol.83 (4),1889-1903.
32. DeFries R., Hansen M., Steininger M., Dubayah R., Sohlberg R., Townshend J. (1997). Subpixel forest cover in central Africa from multisensor, multitemporal data. Remote Sensing of Environment[J], Vol.60 (3),228-246.
33. DeFries R. S., Hansen M. C., Townshend J. R. G., Janetos A. C., Loveland T. R. (2000). A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biology[J], Vol.6(2), 247-254.
34. Dobson M. C., Ulaby F. T., Hallikainen M. T., and El-Rayes M. A (1985), Microwave dielectric behavior of wet soil-Part Ⅱ:Dielectric mixing models, IEEE Transactions on Geoscience and Remote Sensing[J], pp.35-46.
35. Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., et al. (2010). Error characterisation of global active and passive microwave soil moisture datasets. Hydrology and Earth System Sciences[J], Vol.14 (12),2605-2616.
36. Doubkova, M., Sabel, D., Pathe, C., Hasenauer, S., Bartsch, A., & Wagner, W. (2008). High resolution soil moisture from radar instruments:Study area of northeast Austria and southeast Czech Republic. HydroPredict[J],pp.89-92.
37. Erkin Sidick. Power Spectral Density Specification and Analysis of Large Optical Surfaces. Proc. SPIE 7390, Modeling Aspects in Optical Metrology Ⅱ,73900L (June 15,2009); doi:10.1117/12.823844.
38. Falkenmark, M., & Rockstrom, J. (2004). Balancing water for humans and nature:The new approach in ecohydrology. U.K:Earthscan.
39. Ferrazzoli P., Guerriero L., Paloscia S., Pampaloni P., and Solimini D. (1992). Modeling polarization properties of emission from soil covered with vegetation. IEEE Transactions on Geoscience and Remote Sensing[J], vol.30,157-165.
40. Fischer A. (1994a). A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sensing of Environment[J],Vol.48 (2),220-230.
41. Fischer A. (1994b). A simple model for the temporal variations of NDVI at regional scale over agricultural countries. Validation with ground radiometric measurements. International Journal of Remote Sensing[J], Vol.15 (7),1421-1446.
42. Fung A. K., Theory of cross-polarized power returned from a random surface (1967), Applied Science Research[J], Vol.18,50-60.
43. Fung A. K., L. Zong Qian, et al. (1992). Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.30,356-369.
44. Gaskin G.J., Miller J.D. (1996).Measurement of soil water content using a simplified impedance measuring technique. Journal of Agricultural Engineering Research[J], Vol.63 (2),153-159.
45. Gillies R.R., Carlson T.N. (1995).Thermal Remote Sensing of Surface Soil Water Content with Partial Vegetation Cover for Incorporation into Climate Models. Journal of Applied Meteorology[J],Vol.34, 745-756.
46. Gillies R.R., Carlson T.N.,Cui J, Kustas W.P., Humes K. S. (1997).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. International Journal of Remote Sensing[J], Vol.18,3145-3166.
47. Giorgi, Filippo(1990). Simulation of Regional Climate Using a Limited Area Model Nested in a General Circulation Model. Journal of Climate[J], Vol.3(9):941-964.
48. Gruhier C., de Rosnay P., Kerr Y., Mougin E., Ceschia E., Calvet J. C. (2008). Evaluation of AMSR-E soil moisture product based on ground measurements over temperate and semi-arid regions. Geophysical Research Letters[J], Vol.35, L10405. doi:10.1029/2008GL033330.
49. Gruhier C., de Rosnay P., Hasenauer S., Holmes T., de Jeu R., Kerr Y., et al. (2010). Soil moisture active and passive microwave products:intercomparison and evaluation over a Sahelian site. Hydrology and Earth System Sciences[J], Vol.14,141-156.
50. Hemakumara M., Kalma J., Walker J., Willgoose G. (2004). Downscaling of low resolution passive microwave soil moisture observations. In A. J. Teuling, H.55.Leijnse, P. A. Troch, J. Sheffield, & E. F. Wood (Eds.), Proceedings of the 2nd International CAHMDA Workshop on:The Terrestrial Water Cycle:Modeling and Data Assimilation Across Catchment Scales (pp.25-27). Princeton, NJ, October.
51. Holmes, T.R.H., et al. (2009), Land surface temperature from Ka band (37 GHz) passive microwave observations. Journal of Geophysical Research-Atmospheres[J], Vol.114.
52. Hornbuckle B. K., England A. W., De Roo R. D., Fischman M. A., and Boprie D. L. (2003), Vegetation canopy anisotropy at 1.4 GHz, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.41, 2211-2223.
53. Hossain, A. K. M. A., & Easson, G. (2008). Evaluating the potential of vi-lst triangle model for quantitative estimation of soil moisture using optical imagery.2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings,3. (pp. Ⅲ879-Ⅲ882).
54. Hughes J. P., Guttorp P. (1994).A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. WATER RESOURCES RESEARCH[J], Vol.30 (5),1535-1546.
55. Idso, S., Jackson, R., Reginato, R. Compensating for environmental variability in the thermal inertia approach to remote sensing of soil moisture (1976). Journal of Applied Meteorology[J], Vol.15 (8) 811-817.
56. Immerzeel W.W., Rutten M, Droogers P (2009). Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula, Remote Sensing of Environment[J]. Vol.113 (2), 362-370.
57. Jackson.R.D., Slaler.P.N., and Pinter.P.J (1983), Discrimination of growth and water stress in wheat by various Vegetation indices through dear and turbid atmosphere, Remote Sensing of Environment[J], Vol.13,187-208.
58. Jackson, T. J. (1993). Ⅲ. Measuring surface soil moisture using passive microwave remote sensing. Hydrological Processes[J], Vol.7 (2),139-152.
59. Jackson, T. J., Cosh, M. H., Bindlish, R., Starks, P. J., Bosch, D. D., Seyfried, M., et al. (2010) Validation of advanced microwave scanning radiometer soil moisture products. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.48 (12),4256-4272.
60. Jackson, T. J., & Schmugge, T. J. (1995). Surface soil moisture measurement with microwave radiometry. Acta Astronautica[J], Vol.35 (7),477-482.
61. Jackson, T.J. and Schmugge T.J. (1991), Vegetation Effects on the Microwave Emission of Soils. Remote Sensing of Environment[J], Vol.36 (3),203-212.
62. Jackson, T.J., Schmugge T.J., and Wang J.R. (1982), Passive Microwave Sensing of Soil Moisture under Vegetation Canopies. Water Resources Research[J], Vol.18 (4),1137-1142.
63. Jackson, T.J., et al. (1999), Soil moisture mapping at regional scales using microwave radiometry:The Southern Great Plains Hydrology Experiment. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.37 (5),2136-2151.
64. Jiang L., Islam S. (1999). A Methodology for Estimation of Surface Evapotranspiration over Large Areas using Remote Sensing Observations. Geophysical Research Letters[J], Vol.26,2773-2776.
65. Jiang L., Islam S. (2001). Estimation of Surface Evaporation Map over Southern Great Plains Using Remote Sensing Data. Water Resources Research[J], Vol.37,329-340.
66. Jiang L., Islam S. (2003). An Intercomparison of Regional Latent Heat Flux Estimation using Remote Sensing Data. International Journal of Remote Sensing[J], Vol.24,2221-2236.
67. Karam M. A. and Fung A. K. (1988), Electromagnetic scattering from a layer of finite length, randomly oriented, dielectric, circular cylinders over a rough interface with application to vegetation, International Journal of Remote Sensing[J], Vol.9,1109-1134.
68. Karam M. A. and Fung A. K. (1989), Leaf-shape effects in electromagnetic wave scattering from vegetation," IEEE Transactions on Geoscience and Remote Sensing[J], Vol.27,687-697.
69. Karam M. A., Aerojet G. C. and Azusa C. A. (1997), A physical model for microwave radiometry of vegetation, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.35,1045-1058.
70. Kim G., Barros A. P. (2002) Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data. Remote Sensing of Environment[J], Vol.83 (3),400-413.
71. Kim, J., & Hogue, T. S. (2012). Improving spatial soil moisture representation though integration of AMSR-E and MODIS products. IEEE Transactions on Geoscience and Remote Sensing[J],50(2),446-460.
72. Kirdiashev, K., Chukhlantsev A., Shutko A. (1979), Microwave radiation of the earth's surface in the presence of vegetation cover. Radio Eng. Electron. Engl. Transl[J],Vol.24,256-264.
73. Kogan F.N. (1997). Global drought watch from space, Bulletin of the American Meteorological Society[J], Vol.78,621-636.
74. Koike, T., et al., Soil moisture algorithm development and validation for the ADEOS-II/AMSR. Igarss 2000:IEEE 2000 International Geoscience and Remote Sensing Symposium, Vol I-Vi, Proceedings, 2000:p.1253-1255.
75. Koster R. and Suarez M., Energy and water balance calculations in the MOSAIC LSM, Goddard Space Flight Center, Greenbelt, MD, NASA Tech. Memo 104606,1996, vol.9.
76. Kustas W.P. et al.(1993), Relationships between Evaporative Fraction and Remotely-Sensed Vegetation Index and Microwave Brightness Temperature for Semiarid Range lands. Journal of Applied Meteorology[J], Vol.32 (12),1781-1790.
77. Kustas W.P. (1990), Estimates of Evapotranspiration with a One- and Two-layer Model of Heat Transfer Over Partial Canopy Cover. J. Appl., Meteor.[J], Vol.29,205-223.
78. Kustas W.P., Norman J.M. (1996), Use of Remote Sensing for Evapotranspiration Monitoring Over Land Surfaces. Hydrological Sciences Journal[J], Vol.41,495-516.
79. Lakhankar T., Krakauer N., Khanbilvardi R. (2009). Application of microwave remote sensing of soil moisture for agricultural applications. International Journal of Terraspace Science and Engineering[J], Vol.2 (1),81-91.
80. Lambin E.G.,Ehrlich D. (1996). The Surface Temperature-vegetation Index Space for Land Cover and Land-cover Change Analysis. International Journal of Remote Sensing[J], Vol.17,463-487.
81. Le Vine D. M., Karam M. A., Center N. and Greenbelt M. D. (1996), Dependence of attenuation in a vegetation canopy on frequency and plant water content, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.34,1090-1096.
82. Liang X., Wood E., and Lettenmaier D.(1996), Surface and soil moisture parameterization of the VIC-2L model:Evaluation and modifications, Global Planet. Change[J], Vol.13,195-206.
83. Margulis S., McLaughlin D., Entekhabi D., and Dunne S. (2002), Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 field experiment, Water Resour Res.[J], Vol.38,1299, DOI:10.1029/2001WR001114 2002.
84. Mario Montopoli, Nazzareno Pierdicca, and Frank S. Marzano. Spectral Downscaling of Integrated Water Vapor Fields from Satellite Infrared Observations. IEEE Transactions on Geoscience and Remote Sensing [J], Vol.50,415-428,2012.
85. Matzler C. and Sume A. (1989), Microwave radiometry of leaves, Microwave Radiometry and Remote Sensing Applications[J],133-148.
86. McDonald K. C., Dobson M. C., and Ulaby F. T. (1990), Using MIMICS to model L-band multiangle and multitemporal backscatter from a walnut orchard, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.28,477-491.
87. Marina Stathopoulou, Constantinos Cartalis (2009).Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation. Remote Sensing of Environment[J],Vol.113 (12),2592-2605.
88. Mearns L. O., Rosenzweig C., Goldberg R. (1996).The effect of changes in daily and interannual climatic variability on CERES-Wheat:A sensitivity study. CLIMATIC CHANGE[J],Vol.32 (3),257-292.
89. Mecikalski J.R., Diak G.R., Anderson M.C., Norman J.M. (1999), Estimating Fluxes on Continental Scales using Remotely Sensed Data in an Atmospheric-land Exchange Model. J. Appl. Meteor.[J],Vol. 38,1352-1369.
90. Merlin O., Chehbouni A.G., Kerr Y.H., Njoku E.G., Entekhabi D. (2005).A combined modeling and multispectral/multiresolution remote sensing approach for disaggregation of surface soil moisture: application to SMOS configuration. Geoscience and Remote Sensing, IEEE Transactions [J], Vol.43(9), 2036-2050.
91. Merlin O., Chehbouni A., Kerr Y.H., Goodrich D.C. (2006).A downscaling method for distributing surface soil moisture within a microwave pixel:Application to the Monsoon '90 data. Remote Sensing of Environment, Vol.101 (3),379-389.
92. Merlin, O., Walker, J. P., Chehbouni, A., & Kerr, Y. (2008). Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency.Remote Sensing of Environment[J], Vol.112 (10),3935-3946.
93. Milly, P. C. D., Dunne, K. A., & Vecchia, A. V. (2005). Global pattern of trends in stream flow and water availability in a changing climate. Nature[J], Vol.438 (7066),347-350.
94. Mitchell K., Lin Y., Rogers E., Marshall C., M. Ek, J. Lohmann D., Schaake D., Tarpley P., Grunmann G., Manikin Q., and Duan V., Recent GCIP-sponsored advancements in coupled landsurface modeling and data assimilation in the NCEP Eta mesoscale model, in Proc.15th AMS Conf. Hydrol, Amer. Meteor. Soc., Long Beach, CA,2000, pp.180-183, Preprints.
95. Mo T., Choudhury B. J., Schmugge T. J., Wang J. R. and Jackson T. J. (1982), A model for microwave emission from vegetation-covered fields, Journal of Geophysical Research[J], Vol.87,11229-11237.
96. Mo T. and Schmugge T. J. (1987), A parameterization of the effect of surface roughness on microwave emission, IEEE Transactions on Geoscience and Remote Sensing[J],481-486.
97. Nemani R.R., Running S.W. (1989), Estimation of Regional Surface-Resistance to Evapotranspiration from NDVI and Thermal-IR AVHRR Data. Journal of Applied Meteorology[J],Vol.28,276-284.
98. Njoku, E.G. and Kong J.-A.(1977), Theory for Passive Microwave Remote Sensing of Near-Surface Soil Moisture. J. Geophys.Res.[J], Vol.82 (20),3108-3118.
99. Njoku, E.G. and Entekhabi D. (1996), Passive microwave remote sensing of soil moisture. Journal of Hydrology[J],Vol.184 (1-2),101-129.
100. Njoku E.G. and Chan S.K. (2006), Vegetation and surface roughness effects on AMSR-E land observations. Remote Sensing of Environment[J], Vol.100 (2),190-199.
101. Owe, M., De Jeu, R., & Walker, J. (2001). A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.39 (8),1643-1654.
102. Owe M. and Van De Griend A.A. (2001), On the relationship between thermodynamic surface temperature and high-frequency (37 GHz) vertically polarized brightness temperature under semi-arid conditions. International Journal of Remote Sensing[J], Vol.22 (17),3521-3532.
103. Owe M., de Jeu R. and Walker J. (2001), A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.39 (8):p.1643-1654.
104. Owen T.W., Carlson T.N.,Gillies R.R. (1998). An Assessment of satellite Remotely-sensed Land Cover Parameters in Quantitatively Describing the Climatic Effect of Urbanization. International Journal of Remote Sensing[J], Vol.19,1663-1681.
105. Pampaloni P. and Paloscia S. (1986), Microwave emission and plant water content:A comparison between field measurements and theory, IEEE Transactions on Geoscience and Remote Sensing[J], 900-905.
106. Pellenq J., Kalma J., Boulet G., Saulnier G. M., Wooldridge S., Kerr Y., Chehbouni A. (2003). A disaggregation scheme for soil moisture based on topography and soil depth. Journal of Hydrology[J], Vol.276,112-127.
107. Petropoulis G.P., Wooster M.J., Drake N.J. (2006), Investigating the Performance of a Coupled SVAT/Model Remote Sensing Method to Derive Spatially Explicit Maps of Land Atmosphere Energy Fluxes. Geophysical Research Abstracts,0634, European Geosciences Union, SRef ID:1607-7962.
108. Piles M., Camps A., Vall-Llossera M., Corbella I., Panciera R., Rudiger C., et al. (2011).Downscaling SMOS-derived soil moisture using MODIS visible/infrared data. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.49 (9),3156-3166.
109. Pratt,W.K. Digital Image Processing,Wiley,1991.Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.,1996. Numerical Recipesin C. Cambridge University Press,641 pp.
110. Price, J. Thermal inertia mapping:a new view of the earth (1977). Journal of Geophysical ResearchfJ], Vol.82 (18),2582-2590.
111. Price J.C. (1980), The Potential of Remotely Sensed Thermal Infrared Data to Infer Surface Soil Moisture and Evaporation. Water Resources Research[J], Vol.16,787-795.
112. Price J.C. (1982), Estimation of Regional Scale Evapotranspiration through Analysis of Satellite thermal-infrared Data. IEEE Transactions on Geoscience and Remote Sensing [J], Vol.GE-20,286-292.
113. Price J.C. (1985). On the analysis of thermal infrared imagery:The limited utility of apparent thermal inertia. Remote Sensing of Environment J], Vol.18,59-73.
114. Price J.C. (1990).Using Spatial Context in Satellite Data to Infer Regional Scale Evapotranspiration. IEEE Transactions on Geoscience and Remote Sensing[J].Vol.28,940-948.
115. Price, J.C. (1999). Combining multispectral data of differing spatial resolution. IEEE Transactions on Geoscience and Remote Sensing[J],Vol.37 (3),1199-1203.
116. Quarmby N. A., Milnes M., Hindle T. L., Silleos N. (1993). The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International Journal of Remote Sensing[J], Vol.14 (2),199-210.
117. Ray R. L., Jacobs J. M., & Cosh M. H. (2010). Landslide susceptibility mapping using downscaled AMSR-E soil moisture:A case study from Cleveland Corral, California, US. Remote Sensing of Environment[J], Vol.114 (11),2624-2636.
118. Reichle R. H., McLaughlin D. B., and Entekhabi D. (2002), Hydrologic data assimilation with the ensemble Kalman filter, Monthly Weather Rev.[J], Vol.130,103-114.
119. Rudiger C., Calvet J. C., Gruhier C., Holmes T. R. H., de Jeu R. A. M., & Wagner W. (2009). An intercomparison of ERS-Scat and AMSR-E soil moisture observations with model simulations over France. Journal of Hydrometeorology[J], Vol.10 (2),431-447.
120. Sahoo A. K., Houser P. R., Ferguson C.,Wood E. F., Dirmeyer P. A., Kafatos M. (2008).Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River experimental Watershed, Georgia. Remote Sensing of Environment[J],Vo.112,3142-3152.
121. Sandholt I., Rasmussen K., Andersen J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment[J], Vol.79 (22),213-224.
122. Sancer M. I. (1969), Shadow-corrected electromagnetic scattering from a randomly rough surface, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.17,577-589.
123. Schmugge T. (1983). Remote sensing of soil moisture with microwave radiometers, Transactions of the American Society of Agricultural Engineers[J], Vol.26 (1),51-61.
124. Schmugge T. J., Kustas W. P., Ritchie J., Jackson T. J., Rango A. (2002).Remote sensing in hydrology,Advances in Water Resources[J], Vol.25,1367-1385.
125. Seguin B., Itier B. (1983), Using Midday Surface-Temperature to Estimate Daily Evaporation from Satellite Thermal Ir Data, International Journal of Remote Sensing[J], Vol.4,371-383.
126. Shengli, W., & Xiaoxiang, Z. (2010). Using AMSR-E land product to monitor the drought process in China.2010 30th IEEE International Geoscience and Remote Sensing Symposium, (pp.3894-3897)
127. Shi J.C., et al. (2005), A parameterized multifrequency-polarization surface emission model, IEEE Transactions on Geoscience and Remote Sensing[J],Vol.43 (12),2831-2841.
128. Shi J.C., et al. (2006), Physically based estimation of bare-surface soil moisture with the passive radiometers, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.44 (11),3145-3153.
129. Shimshi, D. Effect of soil moisture and phenylmercuric acetate upon stomatal aperture, transpiration, and photosynthesis (1963). Plant Physiology[J], Vol.38 (6),713-721.
130. Smith, S. W. (1997). The Scientist and Engineer's Guide to Digital Signal Processing [M]. California Technical Publishing. ISBN 0-9660176-3-3.
131.Soer G.J.R. (1980), Estimation of Regional Evapotranspiration and Soil Moisture Conditions using Remotely Sensed Crop Surface Temperatures, Remote Sensing Environment J], Vol.9,27-45.
132. Stewart. J.B., Kustas W.P., Humes K.S., Nichols W.D., Moran M.S., De Bruin H.A.R. (1994),Sensible Heat Flux-Radiant Surface Temperature Relationship for Semiarid Areas. J. Appl.Meteor.[J], Vol.33, 1110-1117.
133. Townshend J. R. G., Justice C. O.(1988). Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations. International Journal of Remote Sensing[J], Vol.9 (2), 187-236.
134. Tsang L., J. Kong A., and Shin R. T., Theory of microwave remote sensing:Wiley New York,1985.
135. Ulaby F. T., Moore R. K., and Fung A. K., Microwave Remote Sensing vol.2. Dedham, MA:Artech House,1982.
136. Ulaby F. T., Razani M., and Dobson M. C. (1983), Effects of vegetation cover on the microwave radiometric sensitivity to soil moisture, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.GE-21 (1),51-61.
137. Ulaby F. T., Sarabandi K., McDonald K., Whitt M., and Dobson M. C. (1990), Michigan microwave canopy scattering model, International Journal of Remote Sensing[J], Vol.11,1223-1253.
138. Vachaud, G., Passerat De Silans, A., Balabanis, P., & Vauclin, M. (1985). Temporal stability of spatially measured soil water probability density function. Soil Science Society of America Journal[J], Vol.49 (4),822-828.
139. Valenzuela G. R. (1967), Depolarization of EM waves by slightly rough surfaces, IEEE Transactions on Geoscience and Remote Sensing[J],Vol.15,552-557.
140. Van de Griend A. A., Owe M., De Ruiter J., and Gouweleeuw B. T. (1996), Measurement and behavior of dual-polarization vegetation optical depth and single scattering albedo at 1.4-and 5-GHz microwave frequencies, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.34,957-965.
141. Van de Griend A. A. and Wigneron J. P. (2004), The b-factor as a function of frequency and canopy type at H-polarization, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.42,786-794.
142. Wagner, W., Lemoine, G., & Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment[J], Vol.70 (2),191-207.
143. Wagner, W., Pathe, C., Doubkova, M., Sabel, D., Bartsch, A., Hasenauer, S., et al. (2008).Temporal stability of soil moisture and radar backscatter observed by the Advanced Synthetic Aperture Radar (ASAR). Sensors[J],Vol.8 (2),1174-1197.
144. Walker, J. P., & Houser, P. R. (2004). Requirements of a global near-surface soil moisture satellite mission:Accuracy, repeat time, and spatial resolution. Advances in Water Resources[J],Vol.27 (8),785-801.
145. Wang J. R. and Choudhury B. J. (1981), Remote sensing of soil moisture content over bare fields at 1.4 GHz frequency, Journal of Geophysical Research[J], Vol.86 (C6),5277-5282.
146. Wang J. R., P. E. O'Neill, Jackson T. J., Engman E. T. (1983), Multifrequency measurements of the effects of soil moisture, soil texture, and surface roughness, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.GE-21 (1),44-51.
147. Watson, K. (1971), Applications of Thermal Modeling in the Geological Interpretation of IR Images. Proceedings of the Seventh Int. Symposium of Remote Sensing of Environment. ERIM. Ann Arbor, MI. 2017-2041.
148. Wegmuller U. and Matzler C. (1999), Rough bare soil reflectivity model, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.37 (3),1391-1395.
149. Wetzel, P., Atlas, D., Woodward, R. (1983), Determining Soil Moisture from Study, J. Climate and Applied Meteor[J]. Vol.23,375-391.
150. Wigneron J. P., Parde M., Waldteufel P., Chanzy A., Kerr Y., Schmidl S., and Skou N. (2004) Characterizing the dependence of vegetation model parameters on crop structure, incidence angle, and polarization at L-band, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.42,416-425.
151. Wigneron J. P., Calvet J. C., Chanzy A., Grosjean O., and Laguerre L. (1995), A composite discrete-continuous approach to model the microwaveemission of vegetation, IEEE Transactions on Geoscience and Remote Sensing[J], Vol.33,201-211.
152. Wigneron, J.P. et al. (1998), Use of passive microwave remote sensing to monitor soil moisture. Agronomie[J], Vol.18 (1),27-43.
153. Wilby R.L.,Wigley T.M.L. (1997). Downscaling general circulation model output:a review of methods and limitations. Progress in Physical Geography [J], Vol.21 (4),530-548.
154. Woodcock C. E., Strahler A. H. (1987). The factor of scale in remote sensing. Remote Sensing of Environment[J],Vol.21 (3),311-332.
155. Yu, G., Di, L., & Yang, W. (2008). Downscaling of global soil moisture using auxiliary data.2008 IEEE International Geoscience and Remote Sensing Symposium—Proceedings,3. (pp. Ⅲ230-Ⅲ233) Boston, MA.
156. Zhan X., Houser P. R., Walker J. P., Crow W. T. (2006).A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations. IEEE Transactions on Geoscience and Remote Sensing [J], Vol.44 (6),1534-1544.
157. Zhan, X., Miller, S., Chauhan, N., Di, L., & Ardanuy, P. (2002). Soil moisture visible/infrared radiometer suite algorithm theoretical basis document. Lanham, MD:Raytheon Syst.Company.
158. Zheng, W., Liu, C., Xin, Z., & Wang, Z. (2008). Flood and waterlogging monitoring over Huaihe River basin by AMSR-E data analysis. Chinese Geographical Science[J], Vol.18(3),262-267.
159. Zhukov B., Oertel D., Lanzl, F., Reinhackel G. (1999). Unmixing-based multisensor multiresolution image fusion. IEEE Transactions on Geoscience and Remote Sensing[J], Vol.37 (3),1212-1226.
160.白厚义.回归设计及多元统计分析[M].广西科学技术出版社.11,2003.
161.陈亮.SMOS土壤水分反演算法研究[D].中国科学院博士学位论文,2009.
162.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,环境遥感,9(2)：106-112,1994.
163.丁一.基于MODIS数据和气象数据的植被水监测研究[D].首都师范大学博士学位论文,2011.
164.杜今阳,多极化雷达反演植被覆盖地表土壤水分研究[D],中国科学院博士学位论文,2006.
165.江学顶,黄锦灿,郭泺等.生态边界层水热交换模型及模拟研究进展[J].中山大学学报(自然科学版).S1,2005.
166.刘明亮,唐先明,刘纪远等.基于1km的空间网格数据尺度效应研究.遥感学报,5(3)：183-190,2001.
167.李小文,王锦地,Strahler A.H. (2000)尺度效应及几何光学模型用于尺度纠正.中国科学(E辑),30(增刊)：12-17.
168.刘昌明,孙睿.水循环的生态学方面：土壤-植被-大气系统水分能量平衡研究进展[J].水科学进展.3,1999.
169.马耀明,姚檀栋,王介民等.青藏高原复杂地表能量通量研究.地球科学进展,21(12)：1215-1223,2006.
170.施建成,杜阳,杜今阳等.微波遥感地表参数反演进展.中国科学：地球科学,42(6)：814-842.2012.
171.田国良,杨希华,东小麦旱情遥感监测模型研究,环境遥感,7(2)：83-89,1992.
172.武胜利.基于TRMM的主被动微波遥感结合反演土壤水分算法研究[D].中国科学院博士学位论文,2006.
173.肖斌,沙晋明.土壤水分遥感反演方法概述[J].遥感信息.12,2007.
174.辛晓洲.用定量遥感方法计算地表蒸散[D].中国科学院博士学位论文,2003.
175.徐金鸿,徐瑞松,夏斌等.土壤遥感监测研究进展[J].水土保持研究.4,2006.
176.王万同.基于遥感技术的区域地表蒸散估算研究[D].河南大学博士学位论文,2011.
177.赵逸舟,马耀明,马伟强等.藏北高原土壤温湿变化特征分析.冰川冻土.29(4)：578-583,2007.
178.扎西德赞.西藏气候概述[EB/OL].中国西藏新闻网.http://www.chinatibetnews.com/dili/2002-08/13/content_339460.htm.8,2001.
179.朱新国,林方存,高湖滨.墒情监测研究进展综述[J].节水灌溉.11,2011.
© 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 |