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Sciences in Cold and Arid Regions ›› 2021, Vol. 13 ›› Issue (1): 62-76.doi: 10.3724/SP.J.1226.2021.20067

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Evaluating effects of Dielectric Models on the surface soil moisture retrieval in the Qinghai-Tibet Plateau

Rong Liu1(),Xin Wang1,ZuoLiang Wang1,Jun Wen2   

  1. 1.Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2.College of Atmospheric Sciences, Chengdu university of Information Technology, Chengdu, Sichuan 610225, China
  • Received:2020-07-09 Accepted:2020-12-01 Online:2021-02-28 Published:2021-02-07
  • Contact: Rong Liu E-mail:rliu@lzb.ac.cn
  • Supported by:
    the National Science Foundation of China(42075065);the Second Tibetan Plateau Scientific Expedition and Research (STEP) program(2019QZKK0105)

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Abstract:

Based on the measurement of L-band ground-based microwave radiometer (ELBARA-III type) in the Qinghai-Tibet Plateau and the τ-ω radiative transfer model, this research evaluated the effects of four soil dielectric models, i.e., Wang-Schmugge, Mironov, Dobson, and Four-phase, on the L-band microwave brightness temperature simulation and soil moisture retrieval. The results show that with the same vegetation and roughness parameterization scheme, the four soil dielectric models display obvious differences in microwave brightness temperature simulation. When the soil moisture is less than 0.23 m3/m3, the simulated microwave brightness temperature in Wang-Schmugge model is significantly different from that of the other three models, with maximum differences of horizontal polarization and vertical polarization reaching 8.0 K and 4.4 K, respectively; when the soil moisture is greater than 0.23 m3/m3, the simulated microwave brightness temperature of Four-phase significantly exceeds that of the other three models; when the soil moisture is saturated, maximum differences in simulated microwave brightness temperature with horizontal polarization and vertical polarization are 6.1 K and 4.8 K respectively, and the four soil dielectric models are more variable in the microwave brightness temperature simulation with horizontal polarization than that with vertical polarization. As for the soil moisture retrieval based on the four dielectric models, the comparison study shows that, under the condition of horizontal polarization, Wang-Schmugge model can reduce the degree of retrieved soil moisture underestimating the observed soil moisture more effectively than other parameterization schemes, while under the condition of vertical polarization, the Mironov model can reduce the degree of retrieved soil moisture overestimating the observed soil moisture. Finally, based on the Wang-Schmugge model and FengYun-3C observation data, the spatial distribution of soil moisture in the study area is retrieved.

Key words: L-band, microwave brightness temperature, soil dielectric model, soil moisture retrieval

Figure 1

Landuse information of the study area"

Figure 2

Overview of field data and L-band passive microwave remote sensing instrument used in this study"

Table 1

Values of key parameters for soil and vegetation in radiative transfer model"

Density of soil matrix (g/cm3)

Porosity

(m3/m3)

Single scattering albedo of vegetationSand contentClay content

Volume density

(g/m3)

Frequency

(GHz)

Observation angle
2.650.500.0532.30%10.50%1.121.4150.0°

Figure 3

Simulation case at 1.4 GHz, the effect of soil moisture on dielectric constant under scenario (1)"

Figure 4

Simulation case at 1.4 GHz, the effect of dielectric models' differences on brightness temperature as soil moisture increased under scenario (1)"

Figure 5

Simulation case at 1.4 GHz, the effect of thermodynamic temperature on dielectric constant under scenario (2)"

Figure 6

The ELBARA-III observed brightness temperature at 50° incident angle and the simulated brightness temperature based on the four dielectric models"

Figure 7

Statistics of microwave brightness temperature between simulation and observation with 50° horizontal and vertical polarization for different dielectric models"

Figure 8

Time series of soil moisture retrieved based on four dielectric models and observed soil moisture at 2.0 and 5.0 cm depth"

Table 2

Statistics of soil moisture between H-polarization retrieval and measurement at 2.0 and 5.0 cm for different dielectric models"

2.0 cm5.0 cm
Wang-SchmuggeDobsoFour-phaseMironovWang-SchmuggeDobsoFour-phaseMironov
RMSE (m3/m3)0.0520.0610.0610.0650.0470.0570.0580.059
ubRMSE (m3/m3)0.0450.0510.0520.0480.0440.0520.0540.048
bias (m3/m3)-0.027-0.036-0.035-0.046-0.016-0.025-0.024-0.034
R20.800.780.790.790.780.760.770.77

Table 3

Statistics of soil moisture between V-polarization retrieval and measurement at 2.0 and 5.0 cm for different dielectric models"

2.0 cm5.0 cm
Wang-SchmuggeDobsoFour-phaseMironovWang-SchmuggeDobsoFour-phaseMironov
RMSE (m3/m3)0.0740.0760.0800.0610.0830.0880.0910.072
ubRMSE (m3/m3)0.0460.0450.0450.0430.0460.0480.0480.045
bias (m3/m3)0.0590.0630.0670.0460.0710.0740.0790.057
R20.810.840.850.840.790.820.820.82

Figure 9

Regional distribution of soil moisture over the source region of the Yellow River in summer, 2016"

Arcone S, Grant S, Boitnott G, et al., 2008. Complex permittivity and clay mineralogy of grain-size fractions in a wet silt soil. Geophysics, 73(3): J1-J13. DOI: 10.1190/1.2890776.
doi: 10.1190/1.2890776
Behari J, 2005. Microwave Dielectric Behaviour of Wet Soils. Springer. DOI: 10.1007/1-4020-3288-9.
doi: 10.1007/1-4020-3288-9
Birchak JR, Gardner CG, Hipp JE, et al., 1974. High dielectric constant microwave probes for sensing soil moisture. Proceedings of the IEEE, 62(1): 93-98. DOI: 10.1109/PROC. 1974.9388.
doi: 10.1109/PROC. 1974.9388
Bircher S, Andreasen M, Vuollet J, et al., 2015. Soil moisture sensor calibration for organic soil surface layers. Geoscientific Instrumentation, Methods and Data Systems Discussions, 5: 447-493. DOI: 10.5194/gi-5-109-2016.
doi: 10.5194/gi-5-109-2016
Colliander A, Jackson TJ, Bindlish R, et al., 2017. Validation of SMAP surface soil moisture products with core validation sites. Remote Sensing of Environment, 191: 215-231. DOI: 10.1016/j.rse.2017.01.021.
doi: 10.1016/j.rse.2017.01.021
Christian M, Weber D, Max W, et al., 2003. ELBARA, the ETH L-band radiometer for soil-moisture research. IEEE International Geoscience & Remote Sensing Symposium.
Park CH, Behrendt A, LeDrew E, et al., 2017. New approach for calculating the effective dielectric constant of the moist soil for microwaves. Remote Sensing, 9(7): 732. DOI: 10. 3390/rs9070732.
doi: 10. 3390/rs9070732
Dobson MC, Ulaby FT, Hallikainen MT, et al., 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.
doi: 10.1109/TGRS.1985.289498
Entekhabi D, Njoku EG, Neill PEO, et al., 2010. The Soil Moisture Active Passive (SMAP) Mission. Proceedings of the IEEE, 98(5): 704-716. DOI: 10.1109/JPROC.2010. 2043918.
doi: 10.1109/JPROC.2010. 2043918
Escorihuela MJ, Chanzy A, Wigneron JP, et al., 2010. Effective soil moisture sampling depth of L-band radiometry: A case study. Remote Sensing of Environment, 114(5): 995-1001. DOI: 10.1016/j.rse.2009.12.011.
doi: 10.1016/j.rse.2009.12.011
He Y, Guo X, Wilmshurst JF, 2007. Comparison of different methods for measuring leaf area index in a mixed grassland. Canadian journal of plant science. Revue canadienne de phytotechnie, 87(4): 803-813. DOI: 10.1007/s10457-007-9058-5.
doi: 10.1007/s10457-007-9058-5
Hilhorst M, Dirksen C, Kampers F, et al., 2000. New dielectric mixture equation for porous materials based on depolarization factors. Soil Science Society of America Journal, 64(5): 1581-1587. DOI: 10.2136/sssaj2000.6451581x.
doi: 10.2136/sssaj2000.6451581x
Kerr YH, Waldteufel P, Richaume P, et al., 2012. The SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1384-1403. DOI: 10.1109/TGRS.2012.2184548.
doi: 10.1109/TGRS.2012.2184548
Liu R, Wen J, Wang X, et al., 2019. Derivation of vegetation optical depth and water content in the source region of the yellow river using the fy-3b microwave data. Remote Sensing, 11(13): 1536. DOI:10.3390/rs11131536.
doi: 10.3390/rs11131536
Mo T, Choudhury BJ, Schmugge TJ, et al., 1982. A model for microwave emission from vegetation-covered fields. Journal of Geophysical Research, 87(C13): 11229-11237. DOI: 10.1029/JC087iC13p11229.
doi: 10.1029/JC087iC13p11229
Mironov V, Kerr Y, Wigneron JP, et al., 2012. Temperature and texture dependent dielectric model for moist soils at1.4 GHz. IEEE Geoscience and Remote Sensing Letters, 10(3): 419-423. DOI: 10.1109/LGRS.2012.2207878.
doi: 10.1109/LGRS.2012.2207878
Mironov VL, Bobrov PP, Fomin SV, 2013. Dielectric model of moist soils with varying clay content in the 0.04 to26.5 GHz frequency range. Control and Communications (SIBCON), 2013 International Siberian Conference on IEEE.
Mironov VL, Dobson MC, Kaupp VH, et al., 2004. Generalized refractive mixing dielectric model for moist soils. IEEE Transactions on Geoscience and Remote Sensing, 42(4): 773-785. DOI: 10.1109/TGRS.2003.823288.
doi: 10.1109/TGRS.2003.823288
Mironov VL, Kosolapova LG, Fomin SV, 2009. Physically and mineralogically based spectroscopic dielectric model for moist soils. IEEE Transactions on Geoscience and Remote Sensing, 47(7): 2059-2070. DOI: 10.1109/TGRS.2008. 2011631.
doi: 10.1109/TGRS.2008. 2011631
Mironova VL, Kosolapova LG, Lukin YI, et al., 2017. Temperature and texture dependent dielectric model for frozen and thawed mineral soils at a frequency of1.4 GHz. Remote Sensing of Environment, 200(1): 240-249. DOI: 10.1016/j.rse.2017.08.007.
doi: 10.1016/j.rse.2017.08.007
Park C, Montzka C, Jagdhuber T, et al., 2019. A dielectric mixing model accounting for soil organic matter. Vadose Zone Journal, 18(1): 190036. DOI: 10.2136/vzj2019.04.0036.
doi: 10.2136/vzj2019.04.0036
Pellarin T, Kerr YH, Wigneron J, 2006. Global simulation of brightness temperatures at6.6 and 10.7 GHz over land based on SMMR data set analysis. IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2492-2505. DOI: 10.1109/TGRS.2006.874139.
doi: 10.1109/TGRS.2006.874139
Roy A, Toose P, Williamson M, et al., 2017. Response of L-Band brightness temperatures to freeze/thaw and snow dynamics in a prairie environment from ground-based radiometer measurements. Remote Sensing of Environment, 191: 67-80. DOI: 10.1016/j.rse.2017.01.017.
doi: 10.1016/j.rse.2017.01.017
Schwank M, Wiesmann A, Werner C, et al., 2010. ELBARA II an L-band radiometer system for soil moisture research. Sensors, 10(1): 584-612. DOI: 10.3390/s100100584.
doi: 10.3390/s100100584
Seneviratne SI, Corti T, Davin EL, et al., 2010. Investigating soil moisture-climate interactions in a changing climate: A review. Earth Science Reviews, 99(3/4): 125-161. DOI: 10.1016/j.earscirev.2010.02.004.
doi: 10.1016/j.earscirev.2010.02.004
Stähli M, Stadler D, 1997. Measurement of water and solute dynamics in freezing soil columns with time domain reflectometry. Journal of Hydrology, 195(1): 352-369. DOI: 10. 1016/S0022-1694(96)03227-1.
doi: 10. 1016/S0022-1694(96)03227-1
Van Dam RL, Borchers B, Hendrickx JMH, 2005. Methods for prediction of soil dielectric properties. Proceedings of SPIE, 5794(5794): 188-197. DOI: 10.1117/12.602868.
doi: 10.1117/12.602868
Wang JR, Choudhury BJ, 1981. Remote sensing of soil moisture content over bare field at1.4 GHz frequency. Journal of Geophysical Research-Oceans, 86(NC6): 5277-5282. DOI: 10.1029/JC086iC06p05277.
doi: 10.1029/JC086iC06p05277
Wang JR, Schmugge TJ, 1980. An empirical-model for the complex dielectric permittivity of soils as a function of water-content. IEEE Transactions on Geoscience and Remote Sensing, 18(4): 288-295. DOI: 10.1109/TGRS.1980.350304.
doi: 10.1109/TGRS.1980.350304
Wen J, Jackson TJ, Bindlish R, et al., 2005. Retrieval of soil moisture and vegetation water content using ssm/i data over a corn and soybean region. Journal of Hydrometerology, 6(6): 854-863. DOI: 10.1175/JHM462.1.
doi: 10.1175/JHM462.1
Wen J, Lan Y, Su Z, et al., 2011. Advances in observation and modeling of land surface processes over the soure region og the yellow river. Advance in Earth Science, 26(6): 575-585. DOI: 10.3724/SP.J.1146.2006.01085.
doi: 10.3724/SP.J.1146.2006.01085
Wigneron JP, Jackson TJ, O'neill P, et al., 2017. Modelling the passive microwave signature from land surfaces: a review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. Remote Sensing of Environment, 192(1): 238-262. DOI: 10.1016/j.rse. 2017.01.024.
doi: 10.1016/j.rse. 2017.01.024
Wigneron JP, Kerr Y, Waldteufel P, et al., 2007. L-band microwave emission of the biosphere (L-MEB) Model: description and calibration against experimental data sets over crop fields. Remote Sensing of Environment, 107(4): 639-655. DOI: 10.1016/j.rse.2006.10.014.
doi: 10.1016/j.rse.2006.10.014
Zheng D, Li X, Wang X, et al., 2019. Sampling depth of L-band radiometer measurements of soil moisture and freeze-thaw dynamics on the Tibetan Plateau. Remote Sensing of Environment, 226(1): 16-25. DOI: 10.1016/j.rse.2019. 03.029.
doi: 10.1016/j.rse.2019. 03.029
Zheng D, Wang X, Van Der Velde R, et al., 2017. L-Band microwave emission of soil freeze-thaw process in the third pole environment. IEEE Transactions on Geoscience and Remote Sensing, 55(9): 5324-5338. DOI: 10.1109/TGRS. 2017.2705248.
doi: 10.1109/TGRS. 2017.2705248
Zheng D, Wang X, Velde RVD, et al., 2019. Assessment of soil moisture SMAP retrievals and ELBARA-III measurements in a Tibetan meadow ecosystem. IEEE Geoscience and Remote Sensing Letters, 99(1): 1-5. DOI: 10.1109/LGRS. 2019.2897786.
doi: 10.1109/LGRS. 2019.2897786
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