Quantifying Uncertainties in Land-Surface Microwave Emissivity Retrievals

Tian, Yudong; Peters‐Lidard, Christa D.; Harrison, Kenneth W.; Prigent, Catherine; Norouzi, Hamidreza; Aires, Filipe; Boukabara, Sid; Furuzawa, Fumie A.; Masunaga, Hirohiko

doi:10.1109/tgrs.2013.2244214

Cited by 35 publications

(32 citation statements)

References 34 publications

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“…This issue is more highlighted in desert areas due to moisture scarcity and minimal vegetation interferences. These results are consistent with a previous study by Tian et al (2014) in terms of systematic differences at various frequencies. Also, a wider gap can be noticed between the average monthly emissivity values in the horizontal and vertical polarizations over desert.…”

Section: Resultssupporting

confidence: 94%

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Assessment of the consistency among global microwave land surface emissivity products

et al. 2015

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Abstract. The goal of this work is to intercompare four global land surface emissivity products over various landcover conditions to assess their consistency. The intercompared land emissivity products were generated over a 5-year period (2003)(2004)(2005)(2006)(2007) using observations from the Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E), the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and WindSat. First, all products were reprocessed in the same projection and spatial resolution as they were generated from sensors with various configurations. Then, the mean value and standard deviations of monthly emissivity values were calculated for each product to assess the spatial distribution of the consistencies/inconsistencies among the products across the globe. The emissivity products were also compared to soil moisture estimates and a satellite-based vegetation index to assess their sensitivities to changes in land surface conditions.Results show the existence of systematic differences among the products. Also, it was noticed that emissivity values in each product have similar frequency dependency over different land-cover types. Monthly means of emissivity values from AMSR-E in the vertical and horizontal polarizations seem to be systematically lower than the rest of the products across various land-cover conditions which may be attributed to the 01:30/13:30 LT overpass time of the sensor and possibly a residual skin temperature effect in the product. The standard deviation of the analyzed products was lowest (less than 0.01) in rain forest regions for all products and highest at northern latitudes, above 0.04 for AMSR-E and SSM/I and around 0.03 for WindSat. Despite differences in absolute emissivity estimates, all products were similarly sensitive to changes in soil moisture and vegetation. The correlation between the emissivity polarization differences and normalized difference vegetation index (NDVI) values showed similar spatial distribution across the products, with values close to the unit except over densely vegetated and desert areas.

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Section: Resultssupporting

confidence: 94%

“…The study of Tian et al (2014) accounted for random and systematic errors using statistical approaches and suggested that the differences among retrievals are caused likely by cloud or rain contaminations.…”

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confidence: 99%

Assessment of the consistency among global microwave land surface emissivity products

et al. 2015

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“…These results confirm the previous studies, with the largest differences being observed over snow cover regions and deserts [ Prigent et al , ; Ferraro et al , ; Ringerud et al , ; Tian et al , ]. Note that these surface types correspond not only to large discrepancies between modeled and satellite‐derived emissivities but also to significant variability among models and among sources of satellite‐derived emissivities [ Ferraro et al , ].…”

Section: Emissivity Comparisonsmentioning

confidence: 99%

Evaluation of modeled microwave land surface emissivities with satellite‐based estimates

et al. 2015

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An accurate estimate of the microwave surface emissivity is necessary for the retrieval of atmospheric quantities from microwave imagers or sounders. The objective of this study is to evaluate the microwave land surface emissivity modeling of the Community Radiative Transfer Model (CRTM), providing quantitative statistic information for further model improvements. First, the model‐simulated emissivity is compared to emissivity estimates derived from satellite observations (TELSEM, Tool to Estimate Land Surface Emissivities at Microwaves). The model simulations agree reasonably well with TELSEM over snow‐free vegetated areas, especially at vertical polarization up to 40 GHz. For snow‐free surfaces, the mean difference between CRTM and TELSEM emissivities at vertical polarization is lower than 0.01 below 40 GHz and increases to 0.02 at 89 GHz. At horizontal polarization, it increases with frequency, from 0.01 at 10.6 GHz to 0.04 at 89 GHz. Over deserts and snow, larger differences are observed, which can be due to the lack of quality inputs to the model in these complex environments. A further evaluation is provided by comparing brightness temperature (Tbs) simulations with AMSR‐E observations, where CRTM emissivity and TELSEM emissivity are coupled into a comprehensive radiative transfer model to simulate the brightness temperatures, respectively. The comparison shows smaller RMS errors with the satellite‐derived estimates than with the model, despite some significant bias at midday with the satellite‐derived emissivities at high frequencies. This study confirms and extends to the global scale previous evaluations of land surface microwave emissivity model. It emphasizes the needs for better physical modeling in arid regions and over snow‐covered surfaces.

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“…They pointed out that the emissivity at horizontally polarized 19 GHz can decrease to 0.8 because of prior heavy precipitation events or irrigated lands, which significantly wet the surface. Such a phenomenon, that is, that vegetation is able to depolarize the emissivity relative to bare soil, has been realized by numerous studies (e.g., Brunfeldt and Ulaby 1986;Tian et al 2013). Prigent et al (1997Prigent et al ( , 1998 investigated the MLSE at continental and global scales also using SSM/I data under clear sky.…”

Section: Introductionmentioning

confidence: 97%

“…Yang and Weng (2011) showed that the land surface temperature is the primary source of error in emissivity estimation for frequencies lower than 19 GHz. By evaluating several MLSE datasets, Tian et al (2013) demonstrated that there exist large discrepancies among the estimates from different sensors and from different investigators for the same targeted region. Results showed that the differences between day and night emissivities are reduced to less than 0.01 by integrating such a lookup table.…”

Section: Introductionmentioning

confidence: 99%

Principal Components of Multifrequency Microwave Land Surface Emissivities. Part II: Effects of Previous-Time Precipitation

You

Turk

Haddad

et al. 2014

Journal of Hydrometeorology

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The microwave land surface emissivity (MLSE) over the continental United States was examined during 2011 as a function of prior rainfall conditions using two independent emissivity estimation techniques, one providing instantaneous estimates based on a clear-scene emissivity principal component (PC) analysis and the other based on physical radiative transfer modeling. Results show that over grass, closed shrub, and cropland, prior rainfall can cause the horizontally polarized 10-GHz brightness temperature (TB) to drop by as much as 20 K, with a corresponding emissivity drop of approximately 0.06, whereby prior rain exhibited little influence on the emissivity over forest because of the dense vegetation. The correlation between emissivity and its leading principal components and the prior rainfall over grass, closed shrub, and cropland is 20.6, while it is only 20.1 over forested areas. Forward-simulated TB using the PC-based emissivity derived from instantaneous Tropical Rainfall Measuring Mission (TRMM) satellite overpasses agrees much better with TRMM Microwave Imager (TMI) observations relative to a climatologically based emissivity, especially after a period of heavy rain. Two potential applications of the PC-based emissivity are demonstrated. The first exploits the time history change of the MLSE to estimate the amount of prior rainfall. The second application is a method to estimate the emissivity underneath precipitating radiometric scenes by first adjusting the surfacesensitive principal components that were derived under clear-sky scenes and then by reconstructing the joint emissivity (all channels simultaneously) from the modified PC structure. The results are applicable to future overland passive microwave rainfall retrieval algorithms to simultaneously detect and estimate precipitation amounts under dynamically changing surface conditions.

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Quantifying Uncertainties in Land-Surface Microwave Emissivity Retrievals

Cited by 35 publications

References 34 publications

Assessment of the consistency among global microwave land surface emissivity products

Assessment of the consistency among global microwave land surface emissivity products

Evaluation of modeled microwave land surface emissivities with satellite‐based estimates

Principal Components of Multifrequency Microwave Land Surface Emissivities. Part II: Effects of Previous-Time Precipitation

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