Assimilating ASAR Data for Estimating Soil Moisture Profile Using an En-semble Kalman Filter

YU Fan; LI Haitao; GU Haiyan; HAN Yanshun

doi:10.1007/s11769-013-0623-8

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YU Fan, LI Haitao, GU Haiyan, HAN Yanshun. Assimilating ASAR Data for Estimating Soil Moisture Profile Using an En-semble Kalman Filter[J]. Chinese Geographical Science, 2013, 23(6): 666-679. doi: 10.1007/s11769-013-0623-8

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YU Fan, LI Haitao, GU Haiyan, HAN Yanshun. Assimilating ASAR Data for Estimating Soil Moisture Profile Using an En-semble Kalman Filter[J]. Chinese Geographical Science, 2013, 23(6): 666-679. doi: 10.1007/s11769-013-0623-8

Assimilating ASAR Data for Estimating Soil Moisture Profile Using an En-semble Kalman Filter

doi: 10.1007/s11769-013-0623-8

Key Laboratory of Geo-Informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, Beijing 100830, China

Funds: Under the auspices of National Natural Science Foundation for Young Scientists of China (No. 41101321), Major State Basic Research Development Program of China (No. 2007CB714407), Key Projects in the National Science & Technology Pillar Program (No. 2009BAG18B01, 2012BAH28B03)

More Information

Corresponding author: YU Fan. E-mail: yufan@casm.ac.cn
Received Date: 2012-07-23
Rev Recd Date: 2013-01-09
Publish Date: 2013-11-10

Abstract

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar (ASAR) observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter (EnKF), the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model (DHSVM) was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important.
- assimilation,
- ensemble Kalman filter (EnKF),
- soil moisture,
- hydrological model,
- Advanced Synthetic Aperture Radar (ASAR)

References

[1]	Brubaker K L, Entekhabi D, 1996. Analysis of feedback mechan-isms in land-atmosphere interaction. Water Resources Research, 32(5): 1343-1357. doi: 10.1029/96WR00005
[2]	Burgers G, Leeuwen P J, Evensen G, 1998. Analysis scheme in the ensemble Kalman filter. Monthly Weather Review, 126(6): 1719-1724. doi: 10.1175/1520-0493(1998)126
[3]	Crow W T, Berg M J, 2010. An improved approach for estimating observation and model error parameters in soil moisture data assimilation. Water Resources Research, 46(12): 12-51. doi: 10.1029/2010WR009402.
[4]	Delworth T, Manabe S, 1988. The influence of potential evapora-tion on the variabilities of the simulated soil wetness and climate. Journal of Climate, 1(5): 523-547. doi: 10.1175/1520-0442(1988)001
[5]	Dobson M C, Ulaby F T, 1986. Active microwave soil moisture research. IEEE Transactions on Geoscience and Remote Sensing, 24(1): 23-36. doi: 10.1109/TGRS.1986.289585
[6]	Dobson M C, Ulaby F T, Hallikainen M T, 1985. Microwave dielectric behavior of wet soil-Part II: Dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing, 23(1): 35-46. doi: 10.1109/TGRS.1985.289498
[7]	England A W, Galantowicz J F, Schretter M S, 1992. The radio-brightness thermal inertia measure of soil moisture. IEEE Transactions on Geoscience and Remote Sensing, 30(1): 132-139. doi: 10.1109/36.124223
[8]	Entekhabi D, Galantowicz J F, Njoku E G, 1994. Solving the in-verse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed ob-servations. IEEE Transactions on Geoscience and Remote Sensing, 32(2): 438-448. doi: 10.1109/36.295058
[9]	Etienne H, Dombrowsky E, 2003. Estimation of the optimal in-terpolation parameters in a quasi-geostrophic model of the Northeast Atlantic using ensemble methods. Journal of Marine System, 40(4): 317-339. doi.org/10.1016/S0924-7963 (03)00023-X
[10]	Evensen G, 1994. Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5): 10143-10162. doi: 10.1029/94JC00572
[11]	Fung A K, Lee Z, Chen K S, 1992. Backscattering from a ran-domly rough dielectric surface. IEEE Transactions on Geos-cience and Remote Sensing, 30(2): 356-369. doi: 10.1109/36. 134085
[12]	Galantowicz J F, Entekhabi D, Njoku E G, 1999. Tests of sequen-tial data assimilation for retrieving profile soil moisture and temperature from observed L-band radiobrightness. IEEE Transactions on Geoscience and Remote Sensing, 37(4): 1860-1870. doi: 10.1109/36.774699
[13]	Haugen E J, Evensen G, 2002. Assimilation of SLA and SST data into an OGCM for the Indian Ocean. Ocean Dynamics, 52(3): 133-151. doi: 10.1007/s10236-002-0014-7
[14]	Heathman G C, Starks P J, Ahuja L R et al., 2003. ASsimilation of surface soil moisture to estimate profile soil water content. Journal of Hydrology, 27(1): 1-17. doi: 10.1016/S0022-1694 (03)00088-X
[15]	Houser P R, Shuttleworth W J, Gupta H V, 1998. Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resource Research, 34(12): 3405- 3420. doi: 10.1029/1998WR900001
[16]	Huang C H, Li X, Lu L et al., 2008. Experiments of one-dimen-sional soil moisture assimilation system based on ensemble Kalman filter. Remote Sensing of Environment, 112(3): 888-900. doi: 10.1016/j.rse.2007.06.026
[17]	Kogan F, l990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11(8): 1405-1419. doi: 10.1080/01431169008955102
[18]	Lee S J, Jurkevich L, Dewaele P et al., 1994. Speckle filtering of synthetic aperture radar images: A review. Remote Sens-ing Reviews, 8(4): 313-340. doi: 10.1080/02757259409532206
[19]	Li F Q, Wade T, William P et al., 2010. Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals. Advances in Water Resources, 33(2): 201-214. doi: 10.1016/j.advwatres. 2009.11.007
[20]	Li X, Koike T, Mahadevan P, 2004. A very fast simulated re-annealing (VFSA) approach for land data assimilation. Computer & Geosciences, 30(3): 239-248. doi: 10.1016/j. ca-geo.2003.11.002
[21]	Li X, Lu L, Cheng G D et al., 2001. Quantifying landscape structure of the Heihe River Basin, north-west China using FRAGSTATS. Journal of Arid Environments, 48(4): 521-535. doi: org/10.1006/jare.2000.0715
[22]	Liu Qian, Wang Mingyu, Zhao Yingshi, 2010. A weighted average soil moisture assimilation experiment based on ensemble Kalman filter. Geography and Geo-Information Science, 26(1): 94-97. (in Chinese)
[23]	Mancini M R, Hoeben R, Troch P, 1999. Multifrequency radar observations of bare surface soil moisture content: A laboratory experiment. Water Resources Research, 35(6): 1827-1838. doi: 10.1029/1999WR900033
[24]	Miller R N, Ghil M, Ghautiez F, 1994. Advanced data assimilation in strongly nonlinear dynamical system. Journal of the Atmospheric Sciences, 51(8): 1037-1055. doi: 10.1175/1520-0469(1994)051<1037:ADAISN>2.0.CO;2
[25]	Reichle R H, Crow W T, Christian L et al., 2008. An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resources Research, 44(3): 23-34. doi: 10.1029/2007 WR006357
[26]	Reichle R H, McLaughlin D B, Entekhabi D, 2002a. Hydrologic data assimilation with the ensemble Kalman filter. Monthly Weather Review, 130(1): 103-114. doi: 10.1175/1520-0493 (2002)130
[27]	Reichle R H, Walker J P, Koster R D, 2002b. Extended versus ensemble filtering for land data assimilation. Journal of Hy-drometeorology, 3(2): 728-740. doi: 10.1175/1525-7541 (2002)003
[28]	Roo R D, Duetal Y, 2001. A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion. IEEE Transactions on Geoscience and Remote Sensing, 39(4): 864-872. doi: 10.1109/36.917912
[29]	Sandholt I, Rasmussen K, Andersen J, 2002. A simple interpreta-tion of the surface temperature/vegetation index space for as-sessment of surface moisture status. Remote Sensing of Envi-ronment, 79(2): 213-224. doi: 10.1016/S0034-4257(01) 00274-7
[30]	Sellers P J, Schimel D S, 1993. Remote sensing of the land bios-phere and biogeochemistry in the EOS era: Science priorities, methods and implementation. Global and Planetary Change, 7(4): 279-297. doi: 10.1016/0921-8181(93)90002-6
[31]	Ulaby F T, Allen C T, Eger G, 1984. Relating microwave back-scattering coefficient to leaf area index. Remote Sensing of Environment, 14: 113-133.
[32]	Ulaby F T, Batlivala P P, Dobson M C, 1978. Microwave back-scatter dependence on surface roughness, soil moisture, and soil texture. IEEE Transactions on Geoscience and Remote Sensing, 16(4): 286-295. doi: 10.1109/TGE.1978.294586
[33]	Ulaby F T, Sarahandi K, Donald M K, 1990. Michigan microwave canopy scattering model. International Journal of Remote Sensing, 11(7): 1223-1253. doi: 10.1080/0143116900 8955090
[34]	Verlaan M, Heemink A W, 2001, Nonlinearity in data assimilation applications: A practical method for analysis. Monthly Weather Review, 129(6): 1578-1589. doi: 10.1175/1520-0493 (2001)129
[35]	Walker J P, Willgoose G R, 2001. One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: A comparison of retrieval algorithms. Advances in Water Re-sources, 24(6): 631-650. doi: 10.1016/S0309-1708(00)00043-9
[36]	Wang S G, Li X, Han X J et al., 2011. Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER). Hydrology and Earth System Sciences, 15(5): 1415-1426. doi: 10.5194/hess-15-1415-2011
[37]	Wigmosta M S, Nijssen P S, Lettenmaier D P, 2002. The distri-buted hydrology soil vegetation model. In: Singh V P (eds.). Mathematical Models of Small Watershed Hydrology and Ap-plications. Highlands Ranch: Water Resources Press, 7-42.
[38]	Wigmosta M S, Vail L, Lettenmaier D P, 1994. A distributed hy-drology-vegetation model for complex terrain. Water Resource Research, 30(6): 1665-1679. doi: 10.1029/94WR00436
[39]	Wu T D, Chen K S, Shi J C, 2008. A study of an AIEM model for bistatic scattering from randomly rough surfaces. IEEE Transactions on Geoscience and Remote Sensing, 46(9): 2584-2598. doi: 10.1109/TGRS.2008.919822
[40]	Yang Shenbing, Shen Shuanghe, 2009. Mapping rice yield based on assimilation of ASAR data with rice growth model. Journal of Remote Sensing, 13(2): 282-290. (in Chinese)
[41]	Zhang S W, Li H R, Zhang W D et al., 2006. Estimating the soil moisture profile by assimilating near-surface observations with the ensemble Kalman filter (EnKF). Advances in Atmosphereic Sciences, 22(6): 936-945. doi: 10.1007/BF02918692

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沈阳化工大学材料科学与工程学院沈阳 110142

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Assimilating ASAR Data for Estimating Soil Moisture Profile Using an En-semble Kalman Filter

Key Laboratory of Geo-Informatics of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, Beijing 100830, China

Corresponding author: YU Fan. E-mail: yufan@casm.ac.cn

Received Date: 2012-07-23

Rev Recd Date: 2013-01-09

Publish Date: 2013-11-10

Key words:

assimilation /
ensemble Kalman filter (EnKF) /
soil moisture /
hydrological model /
Advanced Synthetic Aperture Radar (ASAR)

Abstract: Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar (ASAR) observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter (EnKF), the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model (DHSVM) was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important.

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Reference (41)

[1]	Brubaker K L, Entekhabi D, 1996. Analysis of feedback mechan-isms in land-atmosphere interaction. Water Resources Research, 32(5): 1343-1357. doi: 10.1029/96WR00005
[2]	Burgers G, Leeuwen P J, Evensen G, 1998. Analysis scheme in the ensemble Kalman filter. Monthly Weather Review, 126(6): 1719-1724. doi: 10.1175/1520-0493(1998)126
[3]	Crow W T, Berg M J, 2010. An improved approach for estimating observation and model error parameters in soil moisture data assimilation. Water Resources Research, 46(12): 12-51. doi: 10.1029/2010WR009402.
[4]	Delworth T, Manabe S, 1988. The influence of potential evapora-tion on the variabilities of the simulated soil wetness and climate. Journal of Climate, 1(5): 523-547. doi: 10.1175/1520-0442(1988)001
[5]	Dobson M C, Ulaby F T, 1986. Active microwave soil moisture research. IEEE Transactions on Geoscience and Remote Sensing, 24(1): 23-36. doi: 10.1109/TGRS.1986.289585
[6]	Dobson M C, Ulaby F T, Hallikainen M T, 1985. Microwave dielectric behavior of wet soil-Part II: Dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing, 23(1): 35-46. doi: 10.1109/TGRS.1985.289498
[7]	England A W, Galantowicz J F, Schretter M S, 1992. The radio-brightness thermal inertia measure of soil moisture. IEEE Transactions on Geoscience and Remote Sensing, 30(1): 132-139. doi: 10.1109/36.124223
[8]	Entekhabi D, Galantowicz J F, Njoku E G, 1994. Solving the in-verse problem for soil moisture and temperature profiles by sequential assimilation of multifrequency remotely sensed ob-servations. IEEE Transactions on Geoscience and Remote Sensing, 32(2): 438-448. doi: 10.1109/36.295058
[9]	Etienne H, Dombrowsky E, 2003. Estimation of the optimal in-terpolation parameters in a quasi-geostrophic model of the Northeast Atlantic using ensemble methods. Journal of Marine System, 40(4): 317-339. doi.org/10.1016/S0924-7963 (03)00023-X
[10]	Evensen G, 1994. Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5): 10143-10162. doi: 10.1029/94JC00572
[11]	Fung A K, Lee Z, Chen K S, 1992. Backscattering from a ran-domly rough dielectric surface. IEEE Transactions on Geos-cience and Remote Sensing, 30(2): 356-369. doi: 10.1109/36. 134085
[12]	Galantowicz J F, Entekhabi D, Njoku E G, 1999. Tests of sequen-tial data assimilation for retrieving profile soil moisture and temperature from observed L-band radiobrightness. IEEE Transactions on Geoscience and Remote Sensing, 37(4): 1860-1870. doi: 10.1109/36.774699
[13]	Haugen E J, Evensen G, 2002. Assimilation of SLA and SST data into an OGCM for the Indian Ocean. Ocean Dynamics, 52(3): 133-151. doi: 10.1007/s10236-002-0014-7
[14]	Heathman G C, Starks P J, Ahuja L R et al., 2003. ASsimilation of surface soil moisture to estimate profile soil water content. Journal of Hydrology, 27(1): 1-17. doi: 10.1016/S0022-1694 (03)00088-X
[15]	Houser P R, Shuttleworth W J, Gupta H V, 1998. Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resource Research, 34(12): 3405- 3420. doi: 10.1029/1998WR900001
[16]	Huang C H, Li X, Lu L et al., 2008. Experiments of one-dimen-sional soil moisture assimilation system based on ensemble Kalman filter. Remote Sensing of Environment, 112(3): 888-900. doi: 10.1016/j.rse.2007.06.026
[17]	Kogan F, l990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11(8): 1405-1419. doi: 10.1080/01431169008955102
[18]	Lee S J, Jurkevich L, Dewaele P et al., 1994. Speckle filtering of synthetic aperture radar images: A review. Remote Sens-ing Reviews, 8(4): 313-340. doi: 10.1080/02757259409532206
[19]	Li F Q, Wade T, William P et al., 2010. Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals. Advances in Water Resources, 33(2): 201-214. doi: 10.1016/j.advwatres. 2009.11.007
[20]	Li X, Koike T, Mahadevan P, 2004. A very fast simulated re-annealing (VFSA) approach for land data assimilation. Computer & Geosciences, 30(3): 239-248. doi: 10.1016/j. ca-geo.2003.11.002
[21]	Li X, Lu L, Cheng G D et al., 2001. Quantifying landscape structure of the Heihe River Basin, north-west China using FRAGSTATS. Journal of Arid Environments, 48(4): 521-535. doi: org/10.1006/jare.2000.0715
[22]	Liu Qian, Wang Mingyu, Zhao Yingshi, 2010. A weighted average soil moisture assimilation experiment based on ensemble Kalman filter. Geography and Geo-Information Science, 26(1): 94-97. (in Chinese)
[23]	Mancini M R, Hoeben R, Troch P, 1999. Multifrequency radar observations of bare surface soil moisture content: A laboratory experiment. Water Resources Research, 35(6): 1827-1838. doi: 10.1029/1999WR900033
[24]	Miller R N, Ghil M, Ghautiez F, 1994. Advanced data assimilation in strongly nonlinear dynamical system. Journal of the Atmospheric Sciences, 51(8): 1037-1055. doi: 10.1175/1520-0469(1994)051<1037:ADAISN>2.0.CO;2
[25]	Reichle R H, Crow W T, Christian L et al., 2008. An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resources Research, 44(3): 23-34. doi: 10.1029/2007 WR006357
[26]	Reichle R H, McLaughlin D B, Entekhabi D, 2002a. Hydrologic data assimilation with the ensemble Kalman filter. Monthly Weather Review, 130(1): 103-114. doi: 10.1175/1520-0493 (2002)130
[27]	Reichle R H, Walker J P, Koster R D, 2002b. Extended versus ensemble filtering for land data assimilation. Journal of Hy-drometeorology, 3(2): 728-740. doi: 10.1175/1525-7541 (2002)003
[28]	Roo R D, Duetal Y, 2001. A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion. IEEE Transactions on Geoscience and Remote Sensing, 39(4): 864-872. doi: 10.1109/36.917912
[29]	Sandholt I, Rasmussen K, Andersen J, 2002. A simple interpreta-tion of the surface temperature/vegetation index space for as-sessment of surface moisture status. Remote Sensing of Envi-ronment, 79(2): 213-224. doi: 10.1016/S0034-4257(01) 00274-7
[30]	Sellers P J, Schimel D S, 1993. Remote sensing of the land bios-phere and biogeochemistry in the EOS era: Science priorities, methods and implementation. Global and Planetary Change, 7(4): 279-297. doi: 10.1016/0921-8181(93)90002-6
[31]	Ulaby F T, Allen C T, Eger G, 1984. Relating microwave back-scattering coefficient to leaf area index. Remote Sensing of Environment, 14: 113-133.
[32]	Ulaby F T, Batlivala P P, Dobson M C, 1978. Microwave back-scatter dependence on surface roughness, soil moisture, and soil texture. IEEE Transactions on Geoscience and Remote Sensing, 16(4): 286-295. doi: 10.1109/TGE.1978.294586
[33]	Ulaby F T, Sarahandi K, Donald M K, 1990. Michigan microwave canopy scattering model. International Journal of Remote Sensing, 11(7): 1223-1253. doi: 10.1080/0143116900 8955090
[34]	Verlaan M, Heemink A W, 2001, Nonlinearity in data assimilation applications: A practical method for analysis. Monthly Weather Review, 129(6): 1578-1589. doi: 10.1175/1520-0493 (2001)129
[35]	Walker J P, Willgoose G R, 2001. One-dimensional soil moisture profile retrieval by assimilation of near-surface observations: A comparison of retrieval algorithms. Advances in Water Re-sources, 24(6): 631-650. doi: 10.1016/S0309-1708(00)00043-9
[36]	Wang S G, Li X, Han X J et al., 2011. Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER). Hydrology and Earth System Sciences, 15(5): 1415-1426. doi: 10.5194/hess-15-1415-2011
[37]	Wigmosta M S, Nijssen P S, Lettenmaier D P, 2002. The distri-buted hydrology soil vegetation model. In: Singh V P (eds.). Mathematical Models of Small Watershed Hydrology and Ap-plications. Highlands Ranch: Water Resources Press, 7-42.
[38]	Wigmosta M S, Vail L, Lettenmaier D P, 1994. A distributed hy-drology-vegetation model for complex terrain. Water Resource Research, 30(6): 1665-1679. doi: 10.1029/94WR00436
[39]	Wu T D, Chen K S, Shi J C, 2008. A study of an AIEM model for bistatic scattering from randomly rough surfaces. IEEE Transactions on Geoscience and Remote Sensing, 46(9): 2584-2598. doi: 10.1109/TGRS.2008.919822
[40]	Yang Shenbing, Shen Shuanghe, 2009. Mapping rice yield based on assimilation of ASAR data with rice growth model. Journal of Remote Sensing, 13(2): 282-290. (in Chinese)
[41]	Zhang S W, Li H R, Zhang W D et al., 2006. Estimating the soil moisture profile by assimilating near-surface observations with the ensemble Kalman filter (EnKF). Advances in Atmosphereic Sciences, 22(6): 936-945. doi: 10.1007/BF02918692

Assimilating ASAR Data for Estimating Soil Moisture Profile Using an En-semble Kalman Filter

doi: 10.1007/s11769-013-0623-8

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通讯作者: 陈斌, bchen63@163.com

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