Computer Codes

We are sharing some research resouces with other researchers in the following three areas:

NMM3D Lookup Tables

3D Numerical Method of Maxwell’s equations(NMM3D) is a method of moment based algorithm applied to calculate
rough surface scattering and is suitable for application on both active and passive remote sensing. For active
remote sensing, it provides bistatic-scattering coefficient and most importantly, backscattering coefficients.
For passive remote sensing, it provides emissivity that can further apply to calculate brightness temperature.
NMM3D is implemented with various fast methods including UV/PBTG/SMCG with parallel computing. Recently we also
implement near field precondition to further speed up the convergence as well as simulation time. NMM3D are
simulated on NSF Extreme Science and Engineering Discovery Environment (XSEDE), previous TeraGrid. Clusters
we used on XSEDE(TeraGrid) to run NMM3D include Steele(Purdue), Kraken(NICS), Trestles(SDSC), Stampede(TACC),
and Darter(NICS). Although the computation is complex, we’ve pre-computed look up tables(LUT) for both backscattering
coefficients and emissivity. Please send email to Tai Qiao for further information.NMM3D results are based on surface profiles generated by Gaussian random process with exponential correlation function for given surface rms height, correlation length as example shown in Fig.1. For each realization(surface profile), there is a unique solution from NMM3D. At least 30 realizations are conducted to give backscattering and emissivity look up tables.

Format for backscattering coefficient look up table.

- Column 1 : Looking angle in degree
- Column 2 : Correlation Length to rms height ratio
- Column 3 : Real part of relative permittivity for lower medium to upper medium
- Column 4 : Imaginary part of relative permittivity for lower medium to upper medium
- Column 5 : Surface rms height in wavelength according to upper medium
- Column 6 : Backscattering coefficient VV in dB
- Column 7 : Backscattering coefficient HH in dB
- Column 8 : Backscattering coefficient (HV+VH)/2 in dB

- Column 1 : Looking angle in degree
- Column 2 : Correlation Length to rms height ratio
- Column 3 : Real part of relative permittivity for lower medium to upper medium
- Column 4 : Imaginary part of relative permittivity for lower medium to upper medium
- Column 5 : Surface rms height in wavelength according to upper medium
- Column 6 : Emissivity V
- Column 7 : Emissivity H

Lin, Z., T. Leung, et al. (2004). "Emissivity simulations in passive microwave remote sensing with 3-D numerical solutions of Maxwell equations." Geoscience and Remote Sensing, IEEE Transactions on 42(8): 1739-1748.

Huang, S., L. Tsang, et al. (2010). "Backscattering Coefficients, Coherent Reflectivities, and Emissivities of Randomly Rough Soil Surfaces at L-Band for SMAP Applications Based on Numerical Solutions of Maxwell Equations in Three-Dimensional Simulations." Geoscience and Remote Sensing, IEEE Transactions on 48(6): 2557-2568.

Shaowu, H. and T. Leung (2012). "Electromagnetic Scattering of Randomly Rough Soil Surfaces Based on Numerical Solutions of Maxwell Equations in Three-Dimensional Simulations Using a Hybrid UV/PBTG/SMCG Method." Geoscience and Remote Sensing, IEEE Transactions on 50(10): 4025-4035.

Oh, Y., K. Sarabandi, et al. (1992). "An empirical model and an inversion technique for radar scattering from bare soil surfaces." Geoscience and Remote Sensing, IEEE Transactions on 30(2): 370-381.

D. Entekhabi, S. Y., et al (2014). SMAP Handbook, NASA Jet Propulsion Laboratory.

Please direct your questions or comments to Tai Qiao and Leung Tsang.

DMRT_QMS (Matlab code available)

We are now sharing this code with other researchers.
The users are welcome and encouraged to send an email to Shurun Tan, stating your
email, name, affiliation and research areas. These information are voluntary, and they are used for future correspondence
whenever updates are made to the package.The following are the instructions for the DMRT matlab code.The package DMRT-QMS provides a useful toolkit to model the microwave signature of multi-layered (ML) snowpacks sitting on top of a rough soil surface. It is comprised of two parts: active and passive. The active part predicts backscatter; the passive part calculates brightness temperature. The code DMRT-QMS is an implementation of the Dense Media Radiative Transfer (DMRT) theory, based on the Quasi-Crystalline Approximation (QCA) of Mie scattering of densely packed Sticky spheres. The radiative transfer equation is solved using the discrete ordinate method by eigenvalue-quadrature analysis.

The code is developed in the Laboratory of Applications and Computations in Electromagnetics and Optics (LACEO) at University of Washington (UW), United States. The current version is 0.1. The copyright belongs to LACEO of UW. The model results has been compared against the CLPX and NoSREx field measurement campaign and partially validated. Users are welcome to apply it to available new datasets.

The code is written in Matlab, and grouped in three sub-folders: ‘common’, ‘active’ and ‘passive’. Subroutines shared by both active and passive and those functions independent to the DMRT theory are put in the ‘common’ folder. Subroutines dedicated to the solution of active and passive DMRT equations are located in the ‘active’ and ‘passive’ folders, respectively. Users please adapt ‘passive /test_DMRT_QMS_passive.m’ to deploy the passive code and modify ‘active/test_DMRT_QMS_active.m’ to run the active code.

The vertical structure of the layered snowpack could be specified in a snowpack description file. ‘snowpack.txt’ is an example of this file. Each row describes one snow layer, starting from top, going downwards. Each row contains five columns, specifying the layer thickness in cm, snow density in gm/cm3, snow temperature in Kelvin, grain diameter in cm, and the stickiness parameter. A stickiness parameter of 0.1 is suggested as a starting point.

The roughness in the bottom snow-soil interface is partially accounted for. In the passive remote sensing, DMRT-QMS supports two alternative methods to calculate the reflectivity of soil: 1) the QH model (Wang et al. 1981) and 2) the empirical formula of Wegmuller and Matzler (1999). A flat boundary is a special case of the QH model with Q = 0 and H = 0. In the active remote sensing, DMRT-QMS supports three alternative options to calculate the backscatter from the snow-soil interface: 1) a physical Numerical Maxwell Model of 3D rough surface (NMM3D) through a look up table (LUT) of backscatter, 2) a physical model of 1st order SPM 3D, 3) an empirical model of Oh et al. (1992). A flat boundary could be specified by zero rms height. SPM3D gives no cross-pol backscatter. The OH model is compared against measurements for ks between 0.1 and 6, kl between 2.5 and 20, soil moisture between 0.09 and 0.31 and incidence angle between 20 and 70 degree.

‘NMM3D_LUT_NRCS_40degree.dat’ is the NMM3D backscatter LUT with 40 degree incidence angle, ‘NMM3D_LUT_NRCS_40degree_interp.m’ is the corresponding interpolation subroutine to handle the LUT efficiently. The NMM3D LUT with 40 degree incidence angle spans a range of ks between 0.13 to 1.3, permittivity between 3 and 30, and the ratio of correlation length over rms height between 4 and 15.

The permittivity of soil is calculated through the generalized reflective mixing dielectric model of the Mineralogically Based Spectroscopic Dielectric Model (MBSDM) by Mironov et al. 2009.

Benchmarks: Please try ‘passive /test_DMRT_QMS_passive.m’, if correctly, it should produce a plot of brightness temperature against observation angle, and report the brightness temperature at 55 degree. It takes around 6.3 seconds to simulate this two layer snowpack specified in ‘snowpack.txt’ on an Intel i7-3770 CPU @ 3.40GHz.

Please run ‘active/test_DMRT_QMS_active.m’, if correctly, it should report the backscatter decomposition in dB scale. It takes around 43.3 seconds to simulate this two layer snowpack specified in ‘snowpack.txt’ on an Intel i7-3770 CPU @ 3.40GHz.

vv = -8.9967; (vol: -10.4866, surf: -14.3666)

hh = -8.3614; (vol: -9.3863, surf: -15.1347)

hv = -19.9289; (vol: -21.9886, surf: -24.1581)

vh = -19.9289; (vol: -21.9886, surf: -24.1581)

The following references are helpful to understand the models being implemented.

1. L. Tsang, J. Pan, D. Liang, Z. Li, D. W. Cline and Y. Tan, “Modeling active microwave remote sensing of snow using dense media radiative transfer (DMRT) theory with multiple-scattering effects,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 4, pp. 990-1004, Apr. 2007.

2. D. Liang, X. Xu, L. Tsang, K. M. Andreadis and E. G. Josberger, “The effects of layers in dry snow on its passive microwave emissions using dense media radiative transfer theory based on the quasicrystalline approximation (QCA/DMRT),” IEEE Trans. Geos. Rem. Sens., vol. 46, no. 11, pp. 3663-3671, Nov. 2008.

3. D. Liang, L. Tsang, S. Yueh and X. Xu, “Modeling active microwave remote sensing of multilayer dry snow using dense media radiative transfer theory,” IGARSS 2008, pp. III-39~42.

4. S. Huang, L. Tsang, “Electromagnetic Scattering of Randomly Rough Soil Surfaces Based on Numerical Solutions of Maxwell Solutions of Maxwell Equations in Three-Dimensional Simulations Using a Hybrid UV/PBTG/SMCG Method,” Geoscience and Remote Sensing, IEEE Transactions on, vol. 50, no. 10, pp. 4025-4035, Oct. 2012

5. W. Chang, S Tan, J Lemmetyinen L Tsang, X. Xu, X. Li and S Yueh, Dense Media Radiative Transfer Applied To SnowScat and SnowSAR, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, in press , 2014.

6. J. R. Wang and B. J. Choudhury, “Remote sensing of soil moisture content over bare field at 1.4GHz frequency,” J. Geophysical Research, vol. 86, no. C6, pp. 5277-5282, 1981.

7. U. Wegmuller, C. Matzler, “Rough bare soil reflectivity model,” IEEE Trans. Geos. Rem. Sens., vol. 37, no. 3, pp. 1391-1395, 1999

8. Y. Oh, K. Sarabandi and F. T. Ulaby, “An empirical model and an inversion technique for radar scattering form bare soil surfaces,” IEEE Trans. Geos. Rem. Sens., vol. 30, no. 2, pp. 370-381, 1992.

9. V. L. Mironov, L. G. Kosolapova and S. V. Fomin, “Physically and mineralogically based spectroscopic dielectric model for moist soils,” IEEE Trans. Geos. Rem. Sens., vol. 47, no. 7, pp. 2059-2070, 2009.

Please direct your questions or comments to Shurun Tan (srtan@umich.edu), Leung Tsang (leutsang@umich.edu).

DMRT-Bic (Matlab code available)

The DMRT-Bic package share the same function as DMRT-QMS, but different in the snow model. A computer-simulated bi-continuous snow model which has
a better representation of real snow microstructure is used in DMRT-Bic, as shown in the following picture.
1.L. Tsang, J. Pan, D. Liang, Z. X. Li, D. Cline, and Y. H. Tan, “Modeling active microwave remote sensing of snow using dense media radiative transfer (DMRT) theory with multiple scattering effects,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 4, pp. 990-1004, April 2007.

2. K.H. Ding, X.Xu, and L. Tsang, " Electromagnetic Scattering by Bicontinuous Random Microstructures with Discrete Permittivities, " IEEE Trans. Geosci. Remote Sens., vol.48, no.8, pp.3139-3151, Aug. 2010.

3. X. Xu., L. Tsang, and S. Yueh, " Electromagnetic Models of Co/Cross Polarization of Bicontinuous/DMRT in Radar Remote Sensing of Terrestrial Snow at X- and Ku-band for CoReH2O and SCLP Applications, " IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, pp. 1024-1032, 2012

4. W. Chang, S. Tan, L. Tsang, J. Lemmetyinen, X. Xu, and S. Yueh, "Dense media radiative transfer applied to SnowSAR and SnowScat," IEEE J. Sel. Topics Appl. Earth Observ., vol. 7, no. 9, pp. 3811-3825, Sep. 2014.

5. W. Chang, K.-H. Ding, L. Tsang and X. Xu, "Microwave scattering and medium characterization for terrestrial snow with QCA-Mie and bicontinuous models: comparison studies," IEEE Trans. Geosci. Remote. Sens., vol. 54, no. 6, pp. 3637-648, Jun. 2016.

6. S. Tan, C. Xiong, X. Xu, and L. Tsang, "Unaxial Effective Permittivity of Anisotropic Bicontinuous Random Media Using NMM3D," IEEE Geoscience and Remote Sensing Letters, vol. 13, pp. 1168 – 1172, 2016

Please direct your questions or comments to Jiyue Zhu (jiyuezhu@umich.edu), Weihui Gu (whgu@umich.edu), Leung Tsang (leutsang@umich.edu).