Assessment of the consistency among global microwave land surface emissivity products

Norouzi, Hamidreza; Temimi, Marouane; Prigent, Catherine; Turk, J.; Khanbilvardi, R.; Tian, Yudong; Furuzawa, Fumie A.; Masunaga, Hirohiko

doi:10.5194/amt-8-1197-2015

Cited by 36 publications

(25 citation statements)

References 26 publications

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“…Evaluation of roughness effects by the Hr parameter (i.e., surface roughness) showed that some spatial patterns in the Hr values could be associated with the main vegetation types (higher values The vertically and horizontally polarized land surface emissivity at 10 GHz and the difference in polarization were consistent with the spatial variation in the land surface cover type on the Qinghai-Tibetan Plateau. Norouzi et al also found that emissivity values have frequency dependency over different land-cover types [48]. The northwest of the Qinghai-Tibetan Plateau is bare or semi-desert and the southeast is covered by shrubland, grassland, forest and other vegetation.…”

Section: Resultsmentioning

confidence: 97%

Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements

Qian

Bao

et al. 2019

Remote Sensing

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The Qinghai-Tibetan plateau plays an important role in climate change with its unique characteristics, and the surface emissivity is an important parameter to describe the surface characteristics. It is also very important for the accurate retrieval of surface and atmospheric parameters. Different types of surface features have their own radiation characteristics due to their differences in structure, water content and roughness. In this study, the microwave land surface emissivity (10.65, 18.7, 23.8, 36.5 and 89 GHz) of the Qinghai-Tibetan Plateau was calculated using the simplified microwave radiation transmission equation under clear atmospheric conditions based on Level 1 brightness temperatures from the Microwave Radiation Imager onboard the FY-3B meteorological satellite (FY-3B/MWRI) and the National Centers for Environmental Prediction Final (NCEP-FNL) Global Operational Analysis dataset. Furthermore, according to the IGBP (International Geosphere-Biosphere Program) classified data, the spectrum and spatial distribution characteristics of microwave surface emittance in Qinghai-Tibetan plateau were further analyzed. The results show that almost all 16 types of emissivity from IGBP at dual-polarization (vertical and horizontal) increase with the increase of frequency. The spatial distribution of the retrieving results is in line with the changes of surface cover types on the Qinghai-Tibetan plateau, showing the distribution characteristics of large polarization difference of surface emissivity in the northwest and small polarization difference in the southeast, and diverse vegetation can be clearly seen in the retrieving results. In addition, the emissivity is closely related to the type of land surface. Since the emissivity of vegetation is higher than that of bare soil, the contribution of bare soil increases and the surface emissivity decreases as the density of vegetation decreases. Finally, the source of retrieval error was analyzed. The errors in calculating the surface emissivity might mainly come from spatiotemporal collocation of reanalysis data with satellite measurements, the quality of these auxiliary datasets and cloud and precipitation pixel discrimination scheme. Further quantitative analysis of these errors is required, and even standard procedures may need to be improved as well to improve the accuracy of the calculation.

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Section: Resultsmentioning

confidence: 97%

Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements

Qian

Bao

et al. 2019

Remote Sensing

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“…Since the different emissivity data sets can have considerable inconsistencies [Ferraro et al, 2013;Tian et al, 2013;Norouzi et al, 2015], it is necessary to emphasize that we are in fact testing the different methods' capability to reproduce a given reference, not the "truth." This is still meaningful for applications such as GPM's GPROF-based retrievals, since GPROF will use a retrieved emissivity data set to build its database, and it is more pertinent to produce good predictions relative to that data set instead to truth.…”

Section: Spatial and Temporal Domains And Datamentioning

confidence: 99%

“…The study found considerable differences not only between the modeled and retrieved emissivities but also among the retrieved emissivity values from different sensors or different data providers. Follow-up studies have been expended to include other surface types [Tian et al, 2013], more reliable retrieved emissivity retrievals [Ringerud et al, 2014], or broader spatial and temporal coverages [Norouzi et al, 2015;Prigent et al, 2015].…”

Section: Introductionmentioning

confidence: 99%

An examination of methods for estimating land surface microwave emissivity

et al. 2015

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Land surface emissivity is a critical variable for the passive microwave‐based remote sensing of the land and atmosphere. Driven by the Global Precipitation Measurement mission, we implemented and evaluated a variety of approaches for quantitative estimation of land surface emissivity and its variability, within a well‐defined common framework. These approaches fall into three classes: physical modeling, statistical modeling, and a hybrid of physical and statistical modeling. Every approach is subject to evaluation against retrieved emissivity over a large area in the Southern Great Plains for a period of 2 years. Physical modeling, based on two radiative transfer models coupled to a land surface modeling framework, produced reasonable estimates, with channel‐ and polarization‐dependent errors. Calibration of these models with historical data substantially improved their performance at lower frequencies. The statistical method was tested with five different regression models, and each of them consistently outperformed physical models by about 50%. The best statistical model had an average error of 0.9–2.1%. These statistical models were slightly improved when empirical orthogonal function analysis was incorporated in the emissivity data. The hybrid approach produced errors between the uncalibrated and calibrated physical model errors. In addition to their predictive performance, other aspects of each approach's strengths and weaknesses are discussed.

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“…Studies of the factors controlling the natural variability of the MW land surface emissivity are a key focus of the National Aeronautics and Space Administration (NASA) Precipitation Measurement Missions (PMM) Land Surface Working Group (LSWG) (Ferraro et al ., ; Norouzi et al ., ; Tian et al ., ). Land surface modelling has demonstrated the complex relationships between physical surface emissivity factors such as soil composition, soil moisture and vegetation at GMI‐like frequencies (Ringerud et al ., ; Harrison et al, ).…”

Section: Introductionmentioning

confidence: 98%

An observationally based method for stratifying a priori passive microwave observations in a Bayesian‐based precipitation retrieval framework

Turk

Haddad

Kirstetter

et al. 2018

Quart J Royal Meteoro Soc

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Estimation of precipitation from space‐based passive microwave (PMW) radiometric brightness temperature (TB) observations that adapts to the wide variety of Earth surface background and environmental conditions is a long‐standing issue. Since these conditions are generally unknown from the TB observations, PMW‐based precipitation estimation techniques commonly utilize independent ancillary data sources, such as interpolated prognostic variables from numerical weather prediction forecast models, and discrete surface emissivity classifications. In some situations, the selection of these variables may restrain the algorithm performance under particular surface and atmospheric conditions. The objective of this article is to examine the emissivity principal component (EPC) analysis as a common stratification method for indexing, searching and weighting candidate precipitation profiles from a priori databases, adaptable for Bayesian‐based precipitation estimation algorithms applied to the Global Precipitation Measurement (GPM) Microwave Imager (GMI) or other PMW sensors, to minimize dependence upon ancillary data sources. The EPC has been previously shown to track the joint variability between the 10–89 GHz surface emissivity, total column precipitable water vapour (TPW) and surface temperature (Ts) conditions directly from the TB observations, and identify global locations of similar conditions. A parallel GMI precipitation retrieval was carried out where the identical a priori database was indexed by TPW, Ts and a surface emissivity class index. An independent validation of each precipitation retrieval scheme was carried out using GMI pixel‐matched Multi‐Radar Multi‐Source (MRMS) ground radar data over the continental USA and surrounding ocean waters. While the EPC‐based estimates demonstrated similar performance to the TPW‐based estimates over ocean backgrounds, a markedly improved detection, and reduction in bias, was found for moderate and higher (>5 mm/hr) rainfall rates over other backgrounds, especially vegetated surfaces and coastlines.

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Assessment of the consistency among global microwave land surface emissivity products

Cited by 36 publications

References 26 publications

Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements

Microwave Land Emissivity Calculations over the Qinghai-Tibetan Plateau Using FY-3B/MWRI Measurements

An examination of methods for estimating land surface microwave emissivity

An observationally based method for stratifying a priori passive microwave observations in a Bayesian‐based precipitation retrieval framework

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