A dielectric mixing model accounting for soil organic matter

Biogeochemistry; Microwave sensors; Microwaves; Mixing; Moisture control; Organic compounds; Permittivity; Reflectometers; Salinity measurement; Advanced microwave scanning radiometer; Effective dielectric constants; Microwave radiative transfer model; Passive microwave sensors; Soil moisture active passive (SMAP); Soil Moisture and Ocean Salinity (SMOS); Surface soil moisture retrieval; Time-domain reflectometers; Soil moisture; AMSR-E; SMOS

Abstract :

[en] Most dielectric mixing models have been developed for mineral soils without extensive consideration of organic matter (OM). In addition, when used for in situ measurement, most of these models focus only on the real part of the effective dielectric constant without the corresponding imaginary part. Organic matter fractions in soils are found globally (57%), with an especially significant amount in the boreal region (17%). Without proper consideration of OM in dielectric mixing models and subsequent microwave radiative transfer modeling, brightness temperature (TB) calculations may be erroneous. This would lead to uncertainties in the estimation of higher level products, such as soil moisture retrievals from portable soil moisture sensors (e.g., time-domain reflectometers) or passive microwave sensors onboard the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Microwave Scanning Radiometer (AMSR2) satellites. We incorporated OM into a dielectric mixing model by adjusting the wilting point and porosity according to the OM content, i.e., the effective soil dielectric constant decreases with higher OM due to a decrease in the fraction of free water and an increase in bound water. With the proposed soil parameters in the dielectric mixing model, high levels of OM increase the TB for a specific soil moisture by decreasing the microwave effective dielectric constant. The simulated TB better reproduced SMAP-observed TB (11% in RMSE) through the improvement of the effective dielectric constant (40% reduction in RMSE). We anticipate that the application of our approach can improve microwave-based surface soil moisture retrievals in areas with high OM. © 2019 The Author(s).

Disciplines :

Earth sciences & physical geography
Environmental sciences & ecology

Author, co-author :

Park, C.-H.; National Institute of Meteorological Sciences, Earth System Research Division, Korea Meteorological Administration (KMA), Jeju, South Korea

Montzka, C.; Forschungszentrum Jülich GmbH, Institute of Bio-and Geosciences: Agrosphere (IBG-3), Jülich, 52428, Germany

Jagdhuber, T.; Microwaves and Radar Institute, German Aerospace Center (DLR), Weßling, 82234, Germany

Jonard, François ; Université de Liège - ULiège > Département de géographie > Systèmes d'information géographiques

De Lannoy, G.; Dep. of Earth and Environmental Sciences, KU Leuven, Heverlee, B-3001, Belgium

Hong, J.; Ecosystem-Atmosphere Process Lab., Dep. of Atmospheric Science, Yonsei Univ., Seoul, 03722, South Korea

Jackson, T. J.; Hydrology and Remote Sensing Lab., USDA, Beltsville Agricultural Research Center, Beltsville, MD 20705, United States

Wulfmeyer, V.; Institute of Physics and Meteorology, Univ. of Hohenheim, Stuttgart, 70599, Germany

Language :

English

Title :

A dielectric mixing model accounting for soil organic matter

Publication date :

2019

Journal title :

Vadose Zone Journal

ISSN :

1539-1663

Publisher :

Soil Science Society of America

Volume :

Issue :

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 21 September 2021

Statistics

Number of views

48 (2 by ULiège)

Number of downloads

24 (1 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Babaeian, E., M. Sadeghi, S.B. Jones, C. Montzka, H. Vereecken, and M. Tuller. 2019. Ground, proximal and satellite remote sensing of soil moisture. Rev. Geophys. 57:530–616. doi:10.1029/2018RG000618
Bircher, S., M. Andreasen, J. Vuollet, J. Vehviläinen, K. Rautiainen, F. Jonard, et al. 2016. Soil moisture sensor calibration for organic soil surface layers. Geosci. Instrum., Methods Data Syst. 5:109–125. doi:10.5194/gi-5-109-2016
Bircher, S., N. Skou, K.H. Jensen, J. Walker, and L. Rasmussen. 2012. A soil moisture and temperature network for SMOS validation in western Denmark. Hydrol. Earth Syst. Sci. 16:1445–1463. doi:10.5194/hess-16-1445-2012
Chan, C.Y., and R.J. Knight. 1999. Determining water content and saturation from dielectric measurements in layered materials. Water Resour. Res. 35:85–93. doi:10.1029/1998WR900039
Chan, S. 2013. SMAP ancillary data report on static water fraction. Jet Propulsion Lab., California Inst. Technol., Pasadena.
Chan, S., R. Bindlish, P. O’Neill, T. Jackson, E. Njoku, S. Dunbar, et al. 2018. Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sens. Environ. 204:931–941. doi:10.1016/j.rse.2017.08.025
Chan, S., R. Bindlish, R. Hunt, E. Njoku, J. Kimball, and T. Jackson. 2013. Vegetation water content. Ancillary data report. Jet Propulsion Lab., California Inst. Technol., Pasadena.
Chan, S.K., R. Bindlish, P.E. O’Neill, E. Njoku, T. Jackson, A. Colliander, et al. 2016. Assessment of the SMAP passive soil moisture product. IEEE Trans. Geosci. Remote Sens. 54:4994–5007. doi:10.1109/TGRS.2016.2561938
Choudhury, B., T.J. Schmugge, A. Chang, and R. Newton. 1979. Effect of surface roughness on the microwave emission from soils. J. Geophys. Res. Oceans 84:5699–5706. doi:10.1029/JC084iC09p05699
De Lannoy, G.J., R.D. Koster, R.H. Reichle, S.P. Mahanama, and Q. Liu. 2014. An updated treatment of soil texture and associated hydraulic properties in a global land modeling system. J. Adv. Model. Earth Syst. 6:957–979.
De Lannoy, G.J., and R.H. Reichle. 2016a. Global assimilation of multiangle and multipolarization SMOS brightness temperature observations into the GEOS-5 catchment land surface model for soil moisture estimation. J. Hydrometeorol. 17:669–691.
De Lannoy, G.J., and R.H. Reichle. 2016b. Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model. Hydrol. Earth Syst. Sci. 20:4895–4911.
Dobson, M.C., F.T. Ulaby, M.T. Hallikainen, and M.A. Elrayes. 1985. Microwave dielectric behavior of wet soil: 2. Dielectric mixing models. IEEE Trans. Geosci. Remote Sens. GE-23:35–46. doi:10.1109/TGRS.1985.289498
Dorigo, W., A. Xaver, M. Vreugdenhil, A. Gruber, A. Hegyiova, A. Sanchis-Dufau, et al. 2013. Global automated quality control of in situ soil moisture data from the International Soil Moisture Network. Vadose Zone J. 12(3). doi:10.2136/vzj2012.0097
Entekhabi, D., E.G. Njoku, P.E. O’Neill, K.H. Kellogg, W.T. Crow, W.N. Edel-stein, et al. 2010. The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE 98:704–716. doi:10.1109/JPROC.2010.2043918
Escorihuela, M.J., Y.H. Kerr, P. de Rosnay, J.-P. Wigneron, J.-C. Calvet, and F. Lemaitre. 2007. A simple model of the bare soil microwave emission at L-band. IEEE Trans. Geosci. Remote Sens. 45:1978–1987. doi:10.1109/TGRS.2007.894935
FAO. 2012. Harmonized world soil database (Version 1.2). FAO, Rome. http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/
Gupta, S., and W. Larson. 1979. Estimating soil water retention characteristics from particle size distribution, organic matter percent, and bulk density. Water Resour. Res. 15:1633–1635. doi:10.1029/WR015i006p01633
Hallikainen, M.T., F.T. Ulaby, M.C. Dobson, M.A. Elrayes, and L.K. Wu. 1985. Microwave dielectric behavior of wet soil: 1. Empirical models and experimental observations. IEEE Trans. Geosci, Remote Sens. GE-23:25–34. doi:10.1109/TGRS.1985.289497
Hengl, T., J.M. de Jesus, R.A. MacMillan, N.H. Batjes, G.B. Heuve-link, E. Ribeiro, et al. 2014. SoilGrids1km: Global soil information based on automated mapping. PLoS One 9:e105992. doi:10.1371/journal.pone.0105992
Jackson, T., and T. Schmugge. 1991. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 36:203–212. doi:10.1016/0034-4257(91)90057-D
Jackson, T.J., D. Chen, M. Cosh, F. Li, M. Anderson, C. Walthall, et al. 2004. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sens. Environ. 92:475–482. doi:10.1016/j.rse.2003.10.021
Jin, M., X. Zheng, T. Jiang, X. Li, X.-J. Li, and K. Zhao. 2017. Evaluation and improvement of SMOS and SMAP soil moisture products for soils with high organic matter over a forested area in Northeast China. Remote Sens. 9:387. doi:10.3390/rs9040387
Jonard, F., L. Weihermuller, K.Z. Jadoon, M. Schwank, H. Vereecken, and S. Lambot. 2011. Mapping field-scale soil moisture with L-band radiometer and ground-penetrating radar over bare soil. IEEE Trans. Geosci Remote Sens. 49:2863–2875. doi:10.1109/TGRS.2011.2114890
Jones, L.A., J.S. Kimball, K.C. McDonald, S.T.K. Chan, E.G. Njoku, and W.C. Oechel. 2007. Satellite microwave remote sensing of boreal and arctic soil temperatures from AMSR-E. IEEE Trans. Geosci Remote Sens. 45:2004–2018. doi:10.1109/TGRS.2007.898436
Jones, S.B., and S.P. Friedman. 2000. Particle shape effects on the effective permittivity of anisotropic or isotropic media consisting of aligned or randomly oriented ellipsoidal particles. Water Resour. Res. 36:2821– 2833. doi:10.1029/2000WR900198
Kellner, E., and L.-C. Lundin. 2001. Calibration of time domain reflectometry for water content in peat soil. Nord. Hydrol. 32:315–332. doi:10.2166/nh.2001.0018
Kerr, Y.H., P. Waldteufel, J.-P. Wigneron, S. Delwart, F. Cabot, J. Bou-tin, et al. 2010. The SMOS mission: New tool for monitoring key elements of the global water cycle. Proc. IEEE 98:666–687. doi:10.1109/JPROC.2010.2043032
Klotzsche, A., F. Jonard, M. Looms, J. van der Kruk, and J. Huis-man. 2018. Measuring soil water content with ground penetrating radar: A decade of progress. Vadose Zone J. 17:180052. doi:10.2136/vzj2018.03.0052
Lambot, S., M. Antoine, I. van den Bosch, E. Slob, and M. Vanclooster. 2004a. Electromagnetic inversion of GPR signals and subsequent hydrodynamic inversion to estimate effective vadose zone hydraulic properties. Vadose Zone J. 3:1072–1081. doi:10.2136/vzj2004.1072
Lambot, S., E. Slob, I. van den Bosch, B. Stockbroeckx, B. Scheers, and M. Vanclooster. 2004b. Estimating soil electric properties from monostatic ground‐penetrating radar signal inversion in the frequency domain. Water Resour. Res. 40:W04205. doi:10.1029/2003WR002095
Lambot, S., E.C. Slob, I. van den Bosch, B. Stockbroeckx, and M. Vanclooster. 2004c. Modeling of ground-penetrating radar for accurate characterization of subsurface electric properties. IEEE Trans. Geosci. Remote Sens. 42:2555–2568. doi:10.1109/TGRS.2004.834800
Mironov, V., and I. Savin. 2015. A temperature-dependent multi-relax-ation spectroscopic dielectric model for thawed and frozen organic soil at 0.05–15 GHz. Phys. Chem. Earth, Parts A/B/C 83–84:57–64. doi:10.1016/j.pce.2015.02.011
Mironov, V.L., R.D. De Roo, and I.V. Savin. 2010. Temperature-dependable microwave dielectric model for an Arctic soil. IEEE Trans. Geosci Remote Sens. 48:2544–2556. doi:10.1109/TGRS.2010.2040034
Mironov, V.L., L. Kosolapova, I.V. Savin, and K.V. Muzalevskiy. 2015. Temperature dependent dielectric model at 1.4 GHz for a tundra organic-rich soil thawed and frozen. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy. 26–31 July 2015. IEEE, Pisca-taway, NJ. p. 2016–2019. doi:10.1109/IGARSS.2015.7326194
Mironov, V.L., L.G. Kosolapova, and S.V. Fomin. 2009. Physically and mineralogically based spectroscopic dielectric model for moist soils. IEEE Trans. Geosci Remote Sens. 47:2059–2070. doi:10.1109/TGRS.2008.2011631
Mironov, V.L., K.V. Muzalevskiy, and I.V. Savin. 2013. Retrieving temperature gradient in frozen active layer of arctic tundra soils from radiothermal observations in L-band: Theoretical modeling. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6:1781–1785. doi:10.1109/JSTARS.2013.2262108
Miyamoto, T., T. Annaka, and J. Chikushi. 2003. Soil aggregate structure effects on dielectric permittivity of an Andisol measured by time domain reflectometry. Vadose Zone J. 2:90–97. doi:10.2136/vzj2003.9000
National Water and Climate Center. 2018a. Soil Climate Analysis Network (SCAN) data & products. Natl. Water Clim. Ctr., Portland, OR. https://www.wcc.nrcs.usda.gov/scan/
National Water and Climate Center. 2018b. Snow telemetry (SNOTEL) and snow course data and products. Natl. Water Clim. Ctr., Portland, OR. https://www.wcc.nrcs.usda.gov/snow/
O’Neill, P., and T. Jackson. 1990. Observed effects of soil organic matter content on the microwave emissivity of soils. Remote Sens. Environ. 31:175–182. doi:10.1016/0034-4257(90)90087-3
O’Neill, P., S. Chan, E. Njoku, T. Jackson, and R. Bindlish. 2015. Algorithm theoretical basis document (ATBD): Level 2 & 3 Soil Moisture (Passive) Data Products. Rev. B. Jet Propulsion Lab., California Inst. Technol., Pasadena.
Park, C.-H., A. Behrendt, E. LeDrew, and V. Wulfmeyer. 2017. New approach for calculating the effective dielectric constant of the moist soil for microwaves. Remote Sens. 9:732. doi:10.3390/rs9070732
Rautiainen, K., J. Lemmetyinen, J. Pulliainen, J. Vehvilainen, M. Drusch, A. Kontu, et al. 2012. L-band radiometer observations of soil processes in boreal and subarctic environments. IEEE Trans. Geosci. Remote Sens. 50:1483–1497. doi:10.1109/TGRS.2011.2167755
Rawls, W.J., D. Brakensiek, and K. Saxtonn. 1982. Estimation of soil water properties. Trans. ASAE 25:1316–1320. doi:10.13031/2013.33720
Reichle, R.H., J.V. Ardizzone, G.-K. Kim, R.A. Lucchesi, E.B. Smith, and B.H. Weiss. 2015. Soil Moisture Active Passive (SMAP) mission Level 4 surface and root zone soil moisture (L4_SM) product specification document. Jet Propulsion Lab., California Inst. Technol., Pasadena.
Reichle, R.H., G.J. De Lannoy, Q. Liu, J.V. Ardizzone, F. Chen, A. Colliander, et al. 2016. Soil Moisture Active Passive Mission L4_SM data product assessment (version 2 validated release). GMAO Office Note 12. Goddard Space Flight Ctr., Greenbelt, MD. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20160008109.pdf
Reichle, R.H., G.J. De Lannoy, Q. Liu, R.D. Koster, J.S. Kimball, W.T. Crow, et al. 2017. Global assessment of the SMAP Level-4 surface and root-zone soil moisture product using assimilation diagnostics. J. Hydrometeorol. 18:3217–3237. doi:10.1175/JHM-D-17-0130.1
Robinson, D., S.B. Jones, J. Blonquist, and S. Friedman. 2005. A physically derived water content/permittivity calibration model for coarse-textured, layered soils. Soil Sci. Soc. Am. J. 69:1372–1378. doi:10.2136/sssaj2004.0366
Robinson, D., S.B. Jones, J. Wraith, D. Or, and S. Friedman. 2003. A review of advances in dielectric and electrical conductivity measurement in soils using time domain reflectometry. Vadose Zone J. 2:444–475. doi:10.2136/vzj2003.4440
Roth, C., M. Malicki, and R. Plagge. 1992. Empirical evaluation of the relationship between soil dielectric constant and volumetric water content as the basis for calibrating soil moisture measurements by TDR. J. Soil Sci. 43:1–13. doi:10.1111/j.1365-2389.1992.tb00115.x
Saxton, K., and W. Rawls. 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 70:1569–1578. doi:10.2136/sssaj2005.0117
Schaap, M., D. Robinson, S.P. Friedman, and A. Lazar. 2003. Measurement and modeling of the dielectric permittivity of layered granular media using time domain reflectometry. Soil Sci. Soc. Am. J. 67:1113–1121. doi:10.2136/sssaj2003.1113
Seyfried, M., L. Grant, E. Du, and K. Humes. 2005. Dielectric loss and calibration of the Hydra Probe soil water sensor. Vadose Zone J. 4:1070– 1079. doi:10.2136/vzj2004.0148
Stenberg, B., R.A.V. Rossel, A.M. Mouazen, and J. Wetterlind. 2010. Visible and near infrared spectroscopy in soil science. Adv. Agron. 107:163–215.
Stoffregen, H., T. Zenker, and G. Wessolek. 2002. Accuracy of soil water content measurements using ground penetrating radar: Comparison of ground penetrating radar and lysimeter data. J. Hydrol. 267:201– 206. doi:10.1016/S0022-1694(02)00150-6
Topp, G., J. Davis, and A.P. Annan. 1980. Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resour. Res. 16:574–582. doi:10.1029/WR016i003p00574
Tóth, B., M. Weynants, A. Nemes, A. Makó, G. Bilas, and G. Tóth. 2015. New generation of hydraulic pedotransfer functions for Europe. Eur. J. Soil Sci. 66:226–238. doi:10.1111/ejss.12192
Van de Griend, A.A., and J.-P. Wigneron. 2004. The b-factor as a function of frequency and canopy type at H-polarization. IEEE Trans. Geosci Remote Sens. 42:786–794. doi:10.1109/TGRS.2003.821889
Vereecken, H., J. Maes, J. Feyen, and P. Darius. 1989. Estimating the soil moisture retention characteristic from texture, bulk density, and carbon content. Soil Sci. 148:389–403. doi:10.1097/00010694-198912000-00001
Wagner, N., T. Bore, J.C. Robinet, D. Coelho, F. Taillade, and S. Delepine‐Le-soille. 2013. Dielectric relaxation behavior of Callovo–Oxfordian clay rock: A hydraulic‐mechanical‐electromagnetic coupling approach. J. Geophys. Res. Solid Earth 118:4729–4744. doi:10.1002/jgrb.50343
Wang, J., and B. Choudhury. 1981. Remote sensing of soil moisture content, over bare field at 1.4 GHz frequency. J. Geophys. Res. Oceans 86:5277–5282. doi:10.1029/JC086iC06p05277
Wang, J.R. 1983. Passive microwave sensing of soil moisture content: The effects of soil bulk density and surface roughness. Remote Sens. Environ. 13:329–344. doi:10.1016/0034-4257(83)90034-2
Wang, J.R., and T.J. Schmugge. 1980. An empirical model for the complex dielectric permittivity of soils as a function of water content. IEEE Trans. Geosci. Remote Sens. GE-18:288–295. doi:10.1109/TGRS.1980.350304
Watanabe, M., G. Kadosaki, Y. Kim, M. Ishikawa, K. Kushida, Y. Sawada, et al. 2012. Analysis of the sources of variation in L-band backscatter from terrains with permafrost. IEEE Trans. Geosci Remote Sens. 50:44–54. doi:10.1109/TGRS.2011.2159843
Wigneron, J.-P., T. Jackson, P. O’Neill, G. De Lannoy, P. De Rosnay, J. Walker, 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 Sens. Environ. 192:238–262. doi:10.1016/j.rse.2017.01.024
Wigneron, J.-P., L. Laguerre, and Y.H. Kerr. 2001. A simple parameterization of the L-band microwave emission from rough agricultural soils. IEEE Trans. Geosci Remote Sens. 39:1697–1707. doi:10.1109/36.942548
Wösten, J., A. Lilly, A. Nemes, and C. Le Bas. 1999. Development and use of a database of hydraulic properties of European soils. Geoderma 90:169–185. doi:10.1016/S0016-7061(98)00132-3
Yang, F., G.-L. Zhang, J.-L. Yang, D.-C. Li, Y.-G. Zhao, F. Liu, et al. 2014. Organic matter controls of soil water retention in an alpine grassland and its significance for hydrological processes. J. Hydrol. 519:3086–3093. doi:10.1016/j.jhydrol.2014.10.054
Yi, Y., J.S. Kimball, R.H. Chen, M. Moghaddam, and C.E. Miller. 2019. Sensitivity of active layer freezing process to snow cover in arctic Alaska. Cryosphere 13:197–218. doi:10.5194/tc-13-197-2019