Thermal Land Surface Emissivity Retrieved From SEVIRI/Meteosat

Trigo, Isabel F.; Peres, Leonardo F.; DaCamara, Carlos C.; Freitas, Sandra C.

doi:10.1109/tgrs.2007.905197

Cited by 112 publications

(81 citation statements)

References 28 publications

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“…Deserts are characterised by a lower emissivity at 8.7 µm than at 10.8 or 12.0 µm (e.g. Hulley et al, 2015;De Paepe and Dewitte, 2009;Trigo et al, 2008). It is possible that this induces larger IOT CiPS /IWP CiPS retrieval errors because the ANN cannot localise desert regions unambiguously using only latitude and viewing zenith angle.…”

Section: Cirrus Cloud Propertiesmentioning

confidence: 99%

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

2017

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Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate largescale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary.In this paper the CiPS (Cirrus Properties from SE-VIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m −2 . For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.

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Section: Cirrus Cloud Propertiesmentioning

confidence: 99%

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

2017

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“…[50] As in Freitas et al [2010], the radiometric error (sensor noise) of the observed brightness temperatures was assigned values of 0.11 K and 0.15 K for the 10.8 mm and 12.0 mm channels, respectively, yielding an uncertainty on the spectral temperature difference DT of 0.186 K. The resulting uncertainty on the surface temperature retrieval is dT s (T 10.8 ) = 0.11 K and dT s (DT) = 0.34 K. The uncertainty of the mean spectral emissivity ɛ was assigned a value of 0.018, and that on the spectral emissivity difference Dɛ a value of 0.0045, as in Trigo et al [2008], resulting in dT s (ɛ) = 0.80 K and dT s (Dɛ) = 0.36 K. Freitas et al [2010] cite comparable figures for the uncertainty on these quantities over urban areas for the thermal bands of SEVIRI. Also Wan [2008] assigns very similar uncertainties for the MODIS thermal channels at wavelengths comparable with those of SEVIRI's.…”

Section: Appendix A: the Split-window Methodsmentioning

confidence: 99%

Exploring a new method for the retrieval of urban thermophysical properties using thermal infrared remote sensing and deterministic modeling

Ridder¹,

Bertrand²,

Casanova³

et al. 2012

J. Geophys. Res.

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[1] Increasingly, mesoscale meteorological and climate models are used to predict urban weather and climate. Yet, large uncertainties remain regarding values of some urban surface properties. In particular, information concerning urban values for thermal roughness length and thermal admittance is scarce. In this paper, we present a method to estimate values for thermal admittance in combination with an optimal scheme for thermal roughness length, based on METEOSAT-8/SEVIRI thermal infrared imagery in conjunction with a deterministic atmospheric model containing a simple urbanized land surface scheme. Given the spatial resolution of the SEVIRI sensor, the resulting parameter values are applicable at scales of the order of 5 km. As a study case we focused on the city of Paris, for the day of 29 June 2006. Land surface temperature was calculated from SEVIRI thermal radiances using a new split-window algorithm specifically designed to handle urban conditions, as described in Appendix A, including a correction for anisotropy effects. Land surface temperature was also calculated in an ensemble of simulations carried out with the ARPS mesoscale atmospheric model, combining different thermal roughness length parameterizations with a range of thermal admittance values. Particular care was taken to spatially match the simulated land surface temperature with the SEVIRI field of view, using the so-called point spread function of the latter. Using Bayesian inference, the best agreement between simulated and observed land surface temperature was obtained for the Zilitinkevich (1970) and Brutsaert (1975) thermal roughness length parameterizations, the latter with the coefficients obtained by Kanda et al. (2007) Citation: De Ridder, K., C. Bertrand, G. Casanova, and W. Lefebvre (2012), Exploring a new method for the retrieval of urban thermophysical properties using thermal infrared remote sensing and deterministic modeling,

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“…Due to less sensitivity to the uncertainties in the LSEs and the atmospheric conditions, the GSW algorithm has been applied widely to various new generation satellite data to retrieve LST and obtained reliable results [25][26][27][28][29][30]32]. Wan [21] proposed a refined GSW algorithm to improve the LST accuracy and it is directly applied to the FY-3B/VIRR data to retrieve LST in this paper.…”

Section: The Gsw Algorithm For Fy-3b/virrmentioning

confidence: 99%

“…The split-window (SW) algorithm is a multi-channel method and is based on the fact that the difference of the atmospheric water vapor absorptions at selected longwave infrared channels is proportional to the difference in the brightness temperature (BT) at two channels [17]. Due to its simplicity and robustness, the SW algorithm has been applied widely to various satellite data, such as the Advanced Very High Resolution Radiometer (AVHRR) [18][19][20], Moderate Resolution Imaging Spectroradiometer (MODIS) [14,21,22], LandSat-8 [23,24], FengYun polar-orbiting meteorological satellite (VIRR/FY-3A) data [25][26][27], Spinning Enhanced Visible and Infrared Imager (SEVIRI) [28][29][30], Advanced Along-Track Scanning Radiometer (AATSR) [31], Geostationary Operational Environmental Satellites (GOES) [32], FengYun geostationary meteorological satellite (FY-2C) [33], and others.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China

Jiang

Liu

et al. 2015

Remote Sensing

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This paper uses the refined Generalized Split-Window (GSW) algorithm to derive the land surface temperature (LST) from the data acquired by the Visible and Infrared Radiometer on FengYun 3B (FY-3B/VIRR). The coefficients in the GSW algorithm corresponding to a series of overlapping ranges for the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST are derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The GSW algorithm is applied to retrieve LST from FY-3B/VIRR data in an arid area in northwestern China. Three emissivity databases are used to evaluate the accuracy of different emissivity databases for LST retrieval, including the ASTER Global Emissivity Database (ASTER_GED) at a 1-km spatial resolution (AG1km), an average of twelve ASTER emissivity data in the 2012 summer and emissivity spectra extracted from spectral libraries. The LSTs retrieved from the three emissivity databases are evaluated with ground-measured LST at four barren surface sites from June 2012 to December 2013 collected during the HiWATER field campaign. The results indicate that using emissivity OPEN ACCESSRemote Sens. 2015, 7 7081 extracted from ASTER_GED can achieve the highest accuracy with an average bias of 1.26 and −0.04 K and an average root mean square error (RMSE) of 2.69 and 1.38 K for the four sites during daytime and nighttime, respectively. This result indicates that ASTER_GED is a useful emissivity database for generating global LST products from different thermal infrared data and that using FY-3B/VIRR data can produce reliable LST products for other research areas.

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Thermal Land Surface Emissivity Retrieved From SEVIRI/Meteosat

Cited by 112 publications

References 28 publications

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Exploring a new method for the retrieval of urban thermophysical properties using thermal infrared remote sensing and deterministic modeling

Evaluation of Land Surface Temperature Retrieval from FY-3B/VIRR Data in an Arid Area of Northwestern China

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