An Evaluation of Microwave Land Surface Emissivities Over the Continental United States to Benefit GPM-Era Precipitation Algorithms

Ferraro, Ralph; Peters‐Lidard, Christa D.; Hernandez, C.; Turk, F. Joseph; Aires, Filipe; Prigent, Catherine; Lin, Xin; Boukabara, Sid; Furuzawa, Fumie A.; Gopalan, Kaushik; Harrison, Kenneth W.; Karbou, Fatima; Li, Li; Liu, Chuntao; Masunaga, Hirohiko; Moy, L.; Ringerud, Sarah; Skofronick-Jackson, Gail; Tian, Yudong; Wang, Naiyu

doi:10.1109/tgrs.2012.2199121

Cited by 104 publications

(82 citation statements)

References 43 publications

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“…Nevertheless, we believe the two sites over these extreme types of land surfaces give one an educated estimate on the range of errors over the land surface as a whole. This supplements another study by Ferraro et al [10] which compared the systematic differences over a few LSWG sites in the continental United States. Together both studies yield considerable insights into the factors and processes affecting the uncertainties in these retrievals.…”

Section: Summary and Discussionsupporting

confidence: 83%

“…LSWG has assembled a collection of clear-sky land surface emissivity retrievals from many contemporary space-borne passive microwave sensors, over selected, representative land surface types such as desert, rainforest, mid-latitude agricultural land, wet land and high-latitude cold regions [10]. The collection of sensors includes the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), WindSat aboard the Coriolis satellite, the Advanced Microwave Sounding Unit (AMSU), and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E).…”

Section: Methodsmentioning

confidence: 99%

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Quantifying Uncertainties in Land-Surface Microwave Emissivity Retrievals

Tian

Peters‐Lidard

Harrison

et al. 2014

IEEE Trans. Geosci. Remote Sensing

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Abstract-Uncertainties in the retrievals of microwave land surface emissivities were quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1~4% (3~12 K) over desert and 1~7% (3~20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5~2% (2~6 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 10~17 K under the most severe conditions. Index Terms-microwave radiometry, remote sensing, land surface emissivity, measurement uncertainty, systematic errors, random errors, brightness temperature.

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Section: Summary and Discussionsupporting

confidence: 83%

Section: Methodsmentioning

confidence: 99%

Quantifying Uncertainties in Land-Surface Microwave Emissivity Retrievals

Tian

Peters‐Lidard

Harrison

et al. 2014

IEEE Trans. Geosci. Remote Sensing

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“…The emissivity product based on TMI observations is provided by Nagoya University in monthly format (Furuzawa et al, 2012). This product uses Japanese 25-year ReAnalysis (JRA-25) as ancillary data (Onogi et al, 2007).…”

Section: Data Setsmentioning

confidence: 99%

“…Global land emissivity retrieval first was developed by Prigent et al (1998) when brightness temperatures from the Special Sensor Microwave Imager (SSM/I) were used. Other available products later were proposed from other sensors, such as the Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) (Norouzi et al, 2011;Moncet et al, 2011), the Advanced Microwave Sounding Unit (AMSU) (Karbou et al, 2005), and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) (Furuzawa et al, 2012). To retrieve land emissivity values, those studies did not necessarily use the same ancillary data, radiative transfer model, and assumptions to account for the atmospheric contribution.…”

mentioning

confidence: 99%

“…Emissivity estimates from different products were first intercompared as part of a joint effort by members of the Land Surface Working Group (LSWG) of the Global Precipitation Measurement (GPM) mission, with the goal of improving retrievals from the recently launched GPM satellite (Ferraro et al, 2013). The emissivity estimates were compared at three points that coincide with previous and ongoing in situ measurements for Soil Moisture Active Passive (SMAP) and GPM missions.…”

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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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A physical approach for a simultaneous retrieval of sounding, surface, hydrometeor, and cryospheric parameters from SNPP/ATMS

Boukabara

Garrett²,

Grassotti

et al. 2013

JGR Atmospheres

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[1] We present in this study the results obtained when applying a physical algorithm based on a variational methodology to data from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-Orbiting Partnership (SNPP) for a consistent retrieval of geophysical data in all weather conditions. The algorithm, which runs operationally at the U.S. National Oceanic and Atmospheric Administration, is applied routinely to a number of sounders from the Polar-Orbiting Operational Environmental Satellites, the Defense Meteorological Satellite Program, and the European Meteorological Operational satellite constellations. The one-dimension variational (1DVAR) methodology, which relies on a forward operator, the Community Radiative Transfer Model, allows for solving the inversion of the radiometric measurements into geophysical parameters which have a direct impact on the brightness temperatures. The parameters that are produced by this Microwave Integrated Retrieval System algorithm include the atmospheric temperature T(p), moisture Q(p), and vertically integrated total precipitable water; and the surface skin temperature and emissivity as well as the hydrometeor products of nonprecipitating cloud liquid water and rain-and ice-water paths. In this algorithm, a simple postprocessing is applied to the 1DVAR-generated emissivity to derive cryospheric products (snow water equivalent and sea-ice concentration) when the data are measured over these surfaces. The postprocessing is also applied to the hydrometeors products to generate a surface rainfall rate. This comprehensive set of sounding, surface, hydrometeor, and cryospheric products generated from SNPP/ATMS is therefore radiometrically consistent, meaning that when input to the forward operator, it will allow the simulation of the actual brightness temperatures measurements within noise levels. The geophysical consistency between the products, also critical, is satisfied due to the physical approach adopted and the geophysical constraints introduced through the correlation matrix used in the variational system. The results shown in this paper confirm that the performances of all products are within the expected accuracy and precision figures and comparable to performances usually obtained with single-parameter-dedicated algorithms, with the added value that the inverted products are both radiometrically and geophysically consistent.Citation: Boukabara, S. -A., et al. (2013), A physical approach for a simultaneous retrieval of sounding, surface, hydrometeor, and cryospheric parameters from SNPP/ATMS,

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An Evaluation of Microwave Land Surface Emissivities Over the Continental United States to Benefit GPM-Era Precipitation Algorithms

Cited by 104 publications

References 43 publications

Quantifying Uncertainties in Land-Surface Microwave Emissivity Retrievals

Quantifying Uncertainties in Land-Surface Microwave Emissivity Retrievals

Assessment of the consistency among global microwave land surface emissivity products

A physical approach for a simultaneous retrieval of sounding, surface, hydrometeor, and cryospheric parameters from SNPP/ATMS

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