88. Louvet S, Pellarin T, Al Bitar A, Cappelaere B, Galle S, Grippa M, Gruhier C, Kerr Y, Lebel T,
Mialon A (2015) SMOS soil moisture product evaluation over West-Africa from local to
regional scale. Remote Sens Environ 156:383–394
89. Wanders N, Karssenberg D, Bierkens M, Parinussa R, de Jeu R, van Dam J, de Jong S (2012)
Observation uncertainty of satellite soil moisture products determined with physically-based
modeling. Remote Sens Environ 127:341–356
90. Zhuo L, Han D (2017) Hydrological evaluation of satellite soil moisture data in two basins
of different climate and vegetation density conditions. Adv Meteorol 2017. https://doi.org/10.
1155/2017/1086456
91. Dente L, Su Z, Wen J (2012) Validation of SMOS soil moisture products over the maqu
and twente regions. Sensors 12(8):9965–9986
92. Rowlandson TL, Hornbuckle BK, Bramer LM, Patton JC, Logsdon SD (2012) Comparisons
of evening and morning SMOS passes over the Midwest United States. IEEE Trans Geosci
Rem Sens 50(5):1544–1555
93. Sanchez N, Martinez-Fernandez J, Scaini A, Perez-Gutierrez C (2012) Validation of the
SMOS L2 soil moisture data in the REMEDHUS network (Spain). IEEE Trans Geosci Rem
Sens 50(5):1602–1611
94. Jackson TJ (1980) Profile soil moisture from surface measurements. J Irrig Drain Div
106(2):81–92
95. Dorigo W, Scipal K, Parinussa R, Liu Y, Wagner W, De Jeu R, Naeimi V (2010) Error
characterisation of global active and passive microwave soil moisture datasets. Hydrol Earth
Syst Sci 14(12):2605–2616
96. Brocca L, Melone F, Moramarco T, Wagner W, Naeimi V, Bartalis Z, Hasenauer S (2010)
Improving runoff prediction through the assimilation of the ASCAT soil moisture product.
Hydrol Earth Syst Sci Discuss 7(4):4113–4144
97. Al-Bitar A, Leroux D, Kerr YH, Merlin O, Richaume P, Sahoo A, Wood EF (2012) Evaluation
of SMOS soil moisture products over continental US using the SCAN/SNOTEL network.
IEEE Trans Geosci Rem Sens 50(5):1572–1586
98. Crow WT, Van Loon E (2006) Impact of incorrect model error assumptions on the sequential
assimilation of remotely sensed surface soil moisture. J Hydrometeorol 7(3):421–432
99. Srivastava PK, Han D, Ramirez MR, Islam T (2013) Machine learning techniques for
downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resour Manag 27(8):3127–3144
100. Zhao R (1980) The Xinanjiang model. Hydrological forecasting proceedings Oxford symposium, vol 129, IASH, pp 351–356
101. Beven K (2006) On undermining the science? Hydrol Process 20(14):3141–3146
102. Jain SK, Singh VP (2003) Water resources systems planning and management. Elsevier,
Amsterdam
103. Pierdicca N, Pulvirenti L, Bignami C, Ticconi F (2013) Monitoring soil moisture in
an agricultural test site using SAR data: design and test of a pre-operational procedure.
IEEE J Sel Top Appl Earth Obs Remote Sens 6(3):1199–1210
104. Wigneron J-P, Kerr Y, Waldteufel P, Saleh K, Escorihuela M-J, Richaume P, Ferrazzoli P,
De Rosnay P, Gurney R, Calvet J-C (2007) L-band microwave emission of the biosphere
(L-MEB) model: description and calibration against experimental data sets over crop fields.
Remote Sens Environ 107(4):639–655
105. Ahmad S, Kalra A, Stephen H (2010) Estimating soil moisture using remote sensing data:
a machine learning approach. Adv Water Resour 33(1):69–80
106. Woodhouse IH, Hoekman DH (2000) A model-based determination of soil moisture trends
in Spain with the ERS-scatterometer. IEEE Trans Geosci Rem Sens 38(4):1783–1793
107. Zhuo L, Han D (2016) Multi-source hydrological soil moisture state estimation using data
fusion optimisation. Hydrol Earth Syst Sci Discuss. https://doi.org/10.5194/hess-2016-478
108. Elshorbagy A, Parasuraman K (2008) On the relevance of using artificial neural networks for
estimating soil moisture content. J Hydrol 362(1):1–18
280
L. Zhuo
Mialon A (2015) SMOS soil moisture product evaluation over West-Africa from local to
regional scale. Remote Sens Environ 156:383–394
89. Wanders N, Karssenberg D, Bierkens M, Parinussa R, de Jeu R, van Dam J, de Jong S (2012)
Observation uncertainty of satellite soil moisture products determined with physically-based
modeling. Remote Sens Environ 127:341–356
90. Zhuo L, Han D (2017) Hydrological evaluation of satellite soil moisture data in two basins
of different climate and vegetation density conditions. Adv Meteorol 2017. https://doi.org/10.
1155/2017/1086456
91. Dente L, Su Z, Wen J (2012) Validation of SMOS soil moisture products over the maqu
and twente regions. Sensors 12(8):9965–9986
92. Rowlandson TL, Hornbuckle BK, Bramer LM, Patton JC, Logsdon SD (2012) Comparisons
of evening and morning SMOS passes over the Midwest United States. IEEE Trans Geosci
Rem Sens 50(5):1544–1555
93. Sanchez N, Martinez-Fernandez J, Scaini A, Perez-Gutierrez C (2012) Validation of the
SMOS L2 soil moisture data in the REMEDHUS network (Spain). IEEE Trans Geosci Rem
Sens 50(5):1602–1611
94. Jackson TJ (1980) Profile soil moisture from surface measurements. J Irrig Drain Div
106(2):81–92
95. Dorigo W, Scipal K, Parinussa R, Liu Y, Wagner W, De Jeu R, Naeimi V (2010) Error
characterisation of global active and passive microwave soil moisture datasets. Hydrol Earth
Syst Sci 14(12):2605–2616
96. Brocca L, Melone F, Moramarco T, Wagner W, Naeimi V, Bartalis Z, Hasenauer S (2010)
Improving runoff prediction through the assimilation of the ASCAT soil moisture product.
Hydrol Earth Syst Sci Discuss 7(4):4113–4144
97. Al-Bitar A, Leroux D, Kerr YH, Merlin O, Richaume P, Sahoo A, Wood EF (2012) Evaluation
of SMOS soil moisture products over continental US using the SCAN/SNOTEL network.
IEEE Trans Geosci Rem Sens 50(5):1572–1586
98. Crow WT, Van Loon E (2006) Impact of incorrect model error assumptions on the sequential
assimilation of remotely sensed surface soil moisture. J Hydrometeorol 7(3):421–432
99. Srivastava PK, Han D, Ramirez MR, Islam T (2013) Machine learning techniques for
downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resour Manag 27(8):3127–3144
100. Zhao R (1980) The Xinanjiang model. Hydrological forecasting proceedings Oxford symposium, vol 129, IASH, pp 351–356
101. Beven K (2006) On undermining the science? Hydrol Process 20(14):3141–3146
102. Jain SK, Singh VP (2003) Water resources systems planning and management. Elsevier,
Amsterdam
103. Pierdicca N, Pulvirenti L, Bignami C, Ticconi F (2013) Monitoring soil moisture in
an agricultural test site using SAR data: design and test of a pre-operational procedure.
IEEE J Sel Top Appl Earth Obs Remote Sens 6(3):1199–1210
104. Wigneron J-P, Kerr Y, Waldteufel P, Saleh K, Escorihuela M-J, Richaume P, Ferrazzoli P,
De Rosnay P, Gurney R, Calvet J-C (2007) L-band microwave emission of the biosphere
(L-MEB) model: description and calibration against experimental data sets over crop fields.
Remote Sens Environ 107(4):639–655
105. Ahmad S, Kalra A, Stephen H (2010) Estimating soil moisture using remote sensing data:
a machine learning approach. Adv Water Resour 33(1):69–80
106. Woodhouse IH, Hoekman DH (2000) A model-based determination of soil moisture trends
in Spain with the ERS-scatterometer. IEEE Trans Geosci Rem Sens 38(4):1783–1793
107. Zhuo L, Han D (2016) Multi-source hydrological soil moisture state estimation using data
fusion optimisation. Hydrol Earth Syst Sci Discuss. https://doi.org/10.5194/hess-2016-478
108. Elshorbagy A, Parasuraman K (2008) On the relevance of using artificial neural networks for
estimating soil moisture content. J Hydrol 362(1):1–18
280
L. Zhuo