The RADTRAN microwave surface emission models

Isaacs, Ronald G.; Jin, Ya‐Qiu; Worsham, R. D.; Deblonde, G.; Falcone, Vincent J.

doi:10.1109/36.29563

Cited by 22 publications

(17 citation statements)

References 15 publications

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“…This behavior results because, once the optical depth of the canopy becomes large enough to obscure emission from the surface (which occurs near 10 GHz for a thick canopy), any further increase in frequency tends to increase the scattering by the canopy, which will lower the effective emissivity of the surface plus canopy. Above 10 GHz, the emissivity modeled by Isaacs et al (1989) was also unpolarized and had little angular dependence. A more recent model for the emissivity of a leafy deciduous forest also showed saturation effects at 10 GHz for high biomass content (Ferrazzoli and Guerriero 1996).…”

Section: ͑1͒mentioning

confidence: 96%

“…The use of this simplified parametric model for the canopy emission over 18-40 GHz is justified by both models and observations. Isaacs et al (1989) developed a model for the emissivity of a heavily vegetated scene based on radiative transfer through a continuous random media and determined that, for optically thick (i.e., water laden) vegetation, the emissivity increased with frequency at frequencies less 10 GHz and then monotonically decreased with frequency at frequencies greater than 10 GHz. This behavior results because, once the optical depth of the canopy becomes large enough to obscure emission from the surface (which occurs near 10 GHz for a thick canopy), any further increase in frequency tends to increase the scattering by the canopy, which will lower the effective emissivity of the surface plus canopy.…”

Section: ͑1͒mentioning

confidence: 99%

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Determination of an Amazon Hot Reference Target for the On-Orbit Calibration of Microwave Radiometers

Brown

Ruf

2005

Journal of Atmospheric and Oceanic Technology

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A physically based model is developed to determine hot calibration reference brightness temperatures (T B s) over depolarized regions in the Amazon rain forest. The model can be used to evaluate the end-to-end calibration of any satellite microwave radiometer operating at a frequency between 18 and 40 GHz and angle of incidence between nadir and 55°. The model is constrained by Special Sensor Microwave Imager (SSM/I) T B s measured at 19.35, 22.2, and 37.0 GHz at a 53°angle of incidence and extrapolates/interpolates those measurements to other frequencies and incidence angles. The rms uncertainty in the physically based model is estimated to be 0.57 K. For instances in which coincident SSM/I measurements are not available, an empirical formula has been fit to the physical model to provide hot reference brightness temperature as a function of frequency, incidence angle, time of day, and day of year. The empirical formula has a 0.1-K rms deviation from the physically based model for annual averaged measurements and at most a 0.6-K deviation from the model for any specific time of day or day of year.

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Section: ͑1͒mentioning

confidence: 96%

Section: ͑1͒mentioning

confidence: 99%

Determination of an Amazon Hot Reference Target for the On-Orbit Calibration of Microwave Radiometers

Brown

Ruf

2005

Journal of Atmospheric and Oceanic Technology

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“…In addition to the satellite observation studies, microwave radiative transfer modeling results depend on reliable estimates of the microwave surface emittance for various geographical regions and vegetation biome types [Isaacs et al, 1989]. Improvement of such land microwave surface emittance databases and models by incorporating more realistic distributions of microwave surface emittance would enhance current microwave remote sensing efforts over land.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of microwave surface emittance over land using coincident microwave and infrared satellite measurements

Jones¹,

Haar²

1997

J. Geophys. Res.

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Abstract. A method for routinely generating microwave surface emittance over land from coincident microwave and infrared satellite data has been developed. The method includes a surface skin temperature retrieval, and a dynamic cloud discrimination method, in addition to an explicit atmospheric correction based on in situ atmospheric sounding information. An example of results from the method are presented for a 70-day period over the central United States. Atmospheric-corrected microwave surface emittance results are shown to enhance the use of the microwave data sets for determining land surface characteristics, especially in regards to analysis of the data's frequency dependencies. Several problems that affect the use of the microwave brightness temperature data were examined, including natural characteristics of the spatial and temporal variability of the microwave background signature. The microwave surface emittance was found to be sensitive to numerous rain events captured in the data set. Application of this method to generate longer-term climatologies of microwave surface emittance should improve the understanding of the microwave surface emittance behavior. In particular, realistic spatial distributions of microwave surface emittance should enhance current atmospheric microwave remote sensing efforts over land. The microwave surface emittance data set also shows potential in nonforested regions for flood-monitoring purposes using change detection methods. Modeling work such as this and related retrieval methods based on these models would benefit from having high-quality atmospheric corrected microwave surface emittance data sets for validation and for comparison purposes. 13,609

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“…Also, sea ice emissivity, defined as the ratio of the radiant flux from the material to that of a blackbody at the same physical temperature (Comiso 1983), can give insight into the following factors; ice types, composition, edge, age, thickness, surface characteristics, incidence angle, snow cover, frequency, polarization, ocean currents, weather, and etc. (e.g., Weeks 1981;Kidder and Vonder Haar 1995;Isaacs et al 1989;Grenfell 1992). In addition, information about the emissivity is prerequisite to deduce atmospheric properties from microwave measurements (Haggerty and Curry 2001).…”

Section: Introductionmentioning

confidence: 99%

“…During the past three decades, sea ice concentration and extent data have been obtained from several satellite-borne microwave radiometers using multichannels of 10, 19, 24, 37, 50.3, 85, 89, 140, 150, 157, and 220 GHz (e.g., Zwally et al 1983;Comiso 1983;Susskind et al 1984;Hollinger et al 1984;Grenfell and Lohanick 1985;Isaacs et al 1989;Hewison and English 1999;Miao et al 2001). These instruments include the Electronically Scanning Microwave Radiometer (ESMR), the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Microwave Sounding Unit (MSU).…”

Section: Introductionmentioning

confidence: 99%

Surface Emissivity and Hydrometeors Derived from Microwave Satellite Observations and Model Reanalyses

Yoo¹,

Prabhakara

Iacovazzi

2003

Journal of the Meteorological Society of Japan

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Satellite-observed Microwave Sounding Unit (MSU) channel 1 (Ch1) brightness temperature, and General Circulation Model (GCM) reanalyses over the globe, as well as radiative transfer simulations, have been used to investigate microwave surface emissivity, and low-tropospheric hydrometeors, during the period from January 1981 to December 1993. The average model Ch1 temperature has been derived from three kinds of GCM reanalyses, based on the MSU weighting function. Since the Ch1 temperature constructed from GCM-reanalysis air temperature neglects effects from surface emissivity differences and hydrometeors, it is highest in the summer hemisphere. On the other hand, MSU temperature over land is much higher than it is over ocean, due to depressed surface emissivity over water. Over the high latitude ocean the MSU Ch1 temperature is enhanced because of ice/snow emissivity, while it is reduced over the high latitude land.The difference values of Ch1 temperature between reanalysis and MSU decrease, in the regions of the ITCZ, SPCZ and sea ice mainly because of increased MSU temperature. The values decrease by about 4-6 K in the ITCZ and SPCZ regions, due to hydrometeors. This difference is found to decrease by about 10-30 K in sea ice regions, due to change in surface emissivity. To explain these results, we have estimated the contribution of surface emissivity and hydrometeors to the MSU Ch1 temperature, utilizing radiative transfer theory. The increase of 4-6 K in the temperature over the ITCZ and SPCZ is estimated to result from hydrometeors that give a precipitation rate of 1-1.5 mm/day under the horizontal homogeneity of rain within the MSU radiometer field of view (fov, @110 km), and 7-11 mm/day under inhomogeneous rain distribution within the fov, consistent with TRMM observations. The increase of 10-30 K over the high latitude ocean, arises from ice emissivity of 0.6-0.9. Seasonal movements of sea ice boundary also have been discussed. This study could be valuable by providing an independent technique of computing surface emissivity and hydrometeors over the globe, and for long time periods from microwave measurements, such as MSU and AMSU.

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The RADTRAN microwave surface emission models

Cited by 22 publications

References 15 publications

Determination of an Amazon Hot Reference Target for the On-Orbit Calibration of Microwave Radiometers

Determination of an Amazon Hot Reference Target for the On-Orbit Calibration of Microwave Radiometers

Retrieval of microwave surface emittance over land using coincident microwave and infrared satellite measurements

Surface Emissivity and Hydrometeors Derived from Microwave Satellite Observations and Model Reanalyses

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