Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces

Oh, Yisok; Sarabandi, Kamal; Ulaby, F. T.

doi:10.1109/tgrs.2002.800232

Cited by 245 publications

(146 citation statements)

References 19 publications

(42 reference statements)

Supporting

Mentioning

141

Contrasting

Unclassified

Order By: Relevance

“…Radar experiments have shown that the CPD is close to 0 for soil at small incidence angles but the CPD shows an increasing standard deviation for rough surfaces. It has also been found that the CPD increases to a few tens of degrees with increasing incidence angle for rough soil (Sarabandi, 1992;Oh et al, 2002). The CPD is also influenced by vegetation cover, especially for oriented vegetation .…”

Section: Contribution Of a Rough Ground Surfacementioning

confidence: 88%

“…Remote sensing methods provide therefore a complementary tool to the detailed ground measurements such as CT or in situ measurements. For example, radio-wave birefringence measurements have been used to explore internal structures of ice sheets and glaciers with polarized radio-and microwaves (e.g., Hargreaves, 1977Hargreaves, , 1978Fujita et al, 2006;Matsuoka et al, 2009;Parrella et al, 2016). With passive microwave sensors, strong polarimetric signatures have also been found over the Greenland ice sheet: in Li et al (2008), the observed signatures could not be explained by surface features and were discussed with respect to microstructural variations of snow anisotropy, as predicted by Tsang (1991).…”

Section: Radio and Microwave Remote Sensing Observations Of The Dielementioning

confidence: 99%

“…For homogeneously scattering and/or absorbing volumes, |µ j | decreases exponentially, whereas µ j can be quite variable for snow in, for example, the percolation zone of glaciers (Parrella et al, 2016) which contains melt crusts or ice layers. In such cases, assumptions must be made for the penetration depth or the penetration depth must be determined independently and the inversion of the CPD to determine the anisotropy can quickly be questionable.…”

Section: Generalization For Scattering Multilayer Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Leinß

Löwe²,

Proksch³

et al. 2016

The Cryosphere

View full text Add to dashboard Cite

Abstract. The snow microstructure, i.e., the spatial distribution of ice and pores, generally shows an anisotropy which is driven by gravity and temperature gradients and commonly determined from stereology or computer tomography. This structural anisotropy induces anisotropic mechanical, thermal, and dielectric properties. We present a method based on radio-wave birefringence to determine the depth-averaged, dielectric anisotropy of seasonal snow with radar instruments from space, air, or ground. For known snow depth and density, the birefringence allows determination of the dielectric anisotropy by measuring the copolar phase difference (CPD) between linearly polarized microwaves propagating obliquely through the snowpack. The dielectric and structural anisotropy are linked by MaxwellGarnett-type mixing formulas. The anisotropy evolution of a natural snowpack in Northern Finland was observed over four winters (2009)(2010)(2011)(2012)(2013) with the ground-based radar instrument "SnowScat". The radar measurements indicate horizontal structures for fresh snow and vertical structures in old snow which is confirmed by computer tomographic in situ measurements. The temporal evolution of the CPD agreed in ground-based data compared to space-borne measurements from the satellite TerraSAR-X. The presented dataset provides a valuable basis for the development of new snow metamorphism models which include the anisotropy of the snow microstructure.

show abstract

Section: Contribution Of a Rough Ground Surfacementioning

confidence: 88%

Section: Radio and Microwave Remote Sensing Observations Of The Dielementioning

confidence: 99%

Section: Generalization For Scattering Multilayer Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Leinß

Löwe²,

Proksch³

et al. 2016

The Cryosphere

View full text Add to dashboard Cite

show abstract

“…However, many of these methods perform poorly when used to predict soil moisture for natural surfaces (i.e., that depart from bare soil) using radar backscatter data due to their restrictive assumptions [17]. To circumvent these problems, semi-empirical models were developed to predict soil moisture and surface roughness from radar imagery [17,18]. These models use co-polarized back-scatter coefficients, in the horizontal transmit-receive (HH) and/or vertical transmit-receive polarization (VV) to predict soil moisture as they are less sensitive to system noise and cross-interference than the weaker cross-polarized coefficients (i.e., HV and VH).…”

Section: Broad Range Of Model Assumptions and Predictive Accuracymentioning

confidence: 99%

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Hosseini¹,

Newlands

Dean

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often ignored in empirical analyses and validation studies. An optimal choice of model and predictors may, however, provide a more consistent and reliable explanation of the high environmental variability and stochasticity of soil moisture observational data. We integrate active polarimetric satellite remote-sensing data (RADARSAT-2, C-band) with ground-based in-situ data across an agricultural monitoring site in Canada. We apply a grouped step-wise algorithm to iteratively select best-performing predictors of soil moisture. Integrated modeling approaches may better account for observed uncertainty and be tuned to different applications that vary in scale and scope, while also providing greater insights into spatial scaling (upscaling and downscaling) of soil moisture variability from the field-to regional scale. We discuss several methodological extensions and data requirements to enable further statistical modeling and validation for improved agricultural decision-support.Remote Sens. 2015, 7 2753

show abstract

“…Most field studies and models assume bare soil conditions to measure soil moisture with radar remote sensing and modeling backscatter contributions of the soil surface and subsurface [Dubois et al, 1995, Fung et al, 1992, Merzouki et al, 2011, Oh, 2004, Oh et al, 1992, Oh et al, 2002, Rahman et al, 2008, Verhoest et al, 2007, Zribi and Dechambre, 2003]. …”

Section: Remote Sensing Of Soil Moisturementioning

confidence: 99%

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Phillips

Newlands

Liang

et al. 2014

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Determining the best way to efficiently use limited water resources, for food and energydedicated crops, has become crucial due to the rise in extreme events (floods/droughts) and higher variability in rainfall attributed to global climate change. Changing climate conditions will require new crops to be adapted to a changing agricultural environment. Reliable information on seasonal trends in crop growth and evapotranspiration with associated uncertainty/confidence ranges is crucial to guide the development of new crops and management strategies to cope with future climate. Given that crop growth is strongly coupled to soil moisture, developing reliable growth curves requires a detailed understanding of soil moisture at the field-scale. Typically, it is impractical to collect soil samples to adequately assess soil moisture that represents both spatial distribution at the field-scale and temporal dynamics on the scale of a growing season (e.g. 110 days for cereals). A novel way to address soil moisture monitoring challenges is through an integrated, agro-ecosystems-level approach using an integrated sensing system that can link data from multiple platforms (wireless sensors, satellites, airborne imagery, near real-time climate stations). Assimilated data can then be fed into predictive models to generate reference crop growth curves and predict regionally-specific yield potentials. However, integrated sensing requires interagency cooperation, common data processing standards and long-term, timely access to data. Large databases need to be reusable by various organizations and accessible in the future, with comprehensive metadata. During the 2012 growing season a feasibility study was conducted which involved measuring field-scale soil moisture with sensor network technology. The experiment utilized radially-distributed sensors for tracking in-season soil moisture data was collect using both automated in-situ sensors and hand-held sensors. Box-plots of soil moisture data was collected with hand-held revealed an early season wet soil moisture regime and late season dry soil moisture regime. The data sampled on July 5, 2012 was selected for ii geostatistical analysis. Bayesian kriging models and Bayesian kriging models with polynomial trends using different combinations prior distributions for the range and nugget were tested. Models with first order polynomial trend, a reciprocal 2 prior distribution for the range and a reciprocal prior distribution for the nugget fit tended to predict the sample distributions the best. Soil moisture was predicted at a set of random point using ordinary kriging, universal kriging, the two Bayesian kriging models and the Bayesian kriging models with first and second order polynomial trends. Overall, the universal kriging and Bayesian trend models predicted similar data distributions. OpenGIS-compliant services and standards were utilized to provide long-term access to sensor data and construct corresponding metadata. Sensor Model Language, an inter-operable metadata format, ...

show abstract

Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces

Cited by 245 publications

References 19 publications

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Contact Info

Product

Resources

About