Estimation of land surface window (8–12 μm) emissivity from multi‐spectral thermal infrared remote sensing — A case study in a part of Sahara Desert

Ogawa, Kenta; Schmugge, Thomas J.; Jacob, Frédéric; French, Andrew N.

doi:10.1029/2002gl016354

Cited by 81 publications

(60 citation statements)

References 8 publications

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“…From Figure 2a, it is clear that a distinct decrement in the apparent reflectance of cloud and snow occurs when the wavelength increases from visible (VIS, 0.6 μm) to Shortwave Infrared (SWIR, 1.6 μm). As snow has strong visible reflectance and strong SWIR absorbing characteristics, its reflectance remains higher compared to other land features in the visible wavebands, but much lower in the SWIR region [32]. This characteristic has been successfully used for identifying snow [33,34].…”

Section: Spectral Response Characteristics Of Insat-3d Imagermentioning

confidence: 90%

“…Interestingly, the brightness temperature difference between TIR1 (10.8 μm) and TIR2 (12 μm) presented herein does not appear as distinct as reported in other studies (e.g., [3,23]). This may be due to the atmospheric humidity or spectral emissivity variations rather than the dust layer itself [6,32]. However, this deserves further investigation.…”

Section: Spectral Response Characteristics Of Insat-3d Imagermentioning

confidence: 94%

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Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations

Xue

Yang³

et al. 2016

Remote Sensing

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Abstract:The Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R 2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R 2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R 2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity.

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Section: Spectral Response Characteristics Of Insat-3d Imagermentioning

confidence: 90%

Section: Spectral Response Characteristics Of Insat-3d Imagermentioning

confidence: 94%

Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations

Xue

Yang³

et al. 2016

Remote Sensing

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“…These tests are generally rather arbitrarily motivated by the spectral shape of dust extinction and byvisual inspections of possible τ eqv ratios providing information about dust. They are weak enough to allow for additional surface emissivity effects (Ogawa et al, 2003) to also pass the tests. If the score (sum of all positive tests) is larger than 250, the FOV is passed on to further processing (retrieval status level 3).…”

Section: Filtering Of Iasi Datamentioning

confidence: 99%

“…Thus this instrument is very well suited for remote sensing of airborne dust from thermal infrared observations. Over land surface emissivity with its high variability for desert soils is one critical parameter for dust remote sensing with thermal infrared methods (Shenk and Curran, 1974;Ackerman, 1997;Ogawa et al, 2003;Pierangelo et al, 2004;Klüser and Schepanski, 2009). Recently efforts have been made to compile a global database of surface emissivity (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Thermal infrared remote sensing of mineral dust over land and ocean: a spectral SVD based retrieval approach for IASI

2011

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Abstract. From the high spectral resolution thermal infrared observations of the Infrared Atmospheric Sounding Interferometer (IASI) mineral dust AOD (transferred from thermal infrared to 0.5 µm) is retrieved using a Singular Vector Decomposition of brightness temperature spectra. As infrared retrieval based on 8-12 µm observations, dust observation with IASI is independent from solar illumination. Through the linear combinations of suitable independent singular vectors weighted by their contribution to the observed signal, and a projection of different a-priori dust spectra on the resulting signal the dust can be well distinguished from the influence of surface emissivity and gas absorption. In contrast to lookup-table based single-channel retrievals this method takes advantage of the spectral shape of dust extinction and surface and atmosphere influence over the total 8-12 µm window band. Using different a-priori spectra for dust extinction allows also for an estimation of dust particle size in terms of effective radius based on the respective dust model size distributions. These dust models are also used for the transfer of infrared AOD to 0.5 µm.Four months of IASI observations covering Northern Africa and Arabia are used for evaluation. Two large scale dust events, one covering the Arabian Peninsula and adjacent parts of the Indian Ocean, the other over the Atlantic Ocean off the coast of West-Africa, are analysed and compared with other satellite images. They also show the good suitability of IASI data for dust observation at day and night. Monthly means derived from IASI observations represent well the known seasonal cycles of dust activity over Northern Africa and Arabia. IASI Dust AOD 0.5µm and AERONET coarse mode AOD 0.5µm are reasonably well (linearly) correCorrespondence to: L. Klüser (lars.klueser@dlr.de) lated with ρ = 0.623. Moreover, comparison of time series of AERONET and IASI observations shows that the evolution of dust events is very well covered by the IASI observations. Rank correlation between dust effective radius and AERONETÅngström exponent is −0.557 indicating the general capability of (qualitative) dust particle size information being provided by this method.

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“…For example, using a constant or an inaccurate emissivity value results in large errors in the surface energy budget estimations. A 10% error (e.g., from 0.9 to 1.0) on the emissivity approximately corresponds to a 10% error in the energy emitted from the surface (a portion of which may be compensated by the reflected incoming radiation) (Prabhakara and Dalu 1976;Ogawa et al 2003). Analyses of the sensitivity of simulated energy balance to changes in soil emissivity (Zhou et al 2003) revealed that, on average, over northern Africa and the Arabian Peninsula a decrease in the surface emissivity in the atmospheric window by 0.1 would increase the ground and surface air temperatures by about 1.18 and 0.88C, respectively, and decrease surface net and upward longwave radiation fluxes by about 6.6 and 8.1 W m 22 , respectively.…”

Section: Introductionmentioning

confidence: 99%

Infrared Continental Surface Emissivity Spectra and Skin Temperature Retrieved from IASI Observations over the Tropics

Capelle¹,

Chédin²,

Péquignot³

et al. 2012

Journal of Applied Meteorology and Climatology

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Land surface temperature and emissivity spectra are essential variables for improving models of the earth surface-atmosphere interaction or retrievals of atmospheric variables such as thermodynamic profiles, chemical composition, cloud and aerosol characteristics, and so on. In most cases, emissivity spectral variations are not correctly taken into account in climate models, leading to potentially significant errors in the estimation of surface energy fluxes and temperature. Satellite infrared observations offer the dual opportunity of accurately estimating these properties of land surfaces as well as allowing a global coverage in space and time. Here, high-spectral-resolution observations from the Infrared Atmospheric Sounder Interferometer (IASI) over the tropics (308N-308S), covering the period July 2007-March 2011, are interpreted in terms of 18 3 18 monthly mean surface skin temperature and emissivity spectra from 3.7 to 14 mm at a resolution of 0.05 mm. The standard deviation estimated for the surface temperature is about 1.3 K. For the surface emissivity, it varies from about 1%-1.5% for the 10.5-14-and 5.5-8-mm windows to about 4% around 4 mm. Results from comparisons with products such as Moderate Resolution Imaging Spectroradiometer (MODIS) low-resolution emissivity and surface temperature or ECMWF forecast data (temperature only) are presented and discussed. Comparisons with emissivity derived from the Airborne Research Interferometer Evaluation System (ARIES) radiances collected during an aircraft campaign over Oman and made at the scale of the IASI field of view offer valuable data for the validation of the IASI retrievals.

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Estimation of land surface window (8–12 μm) emissivity from multi‐spectral thermal infrared remote sensing — A case study in a part of Sahara Desert

Cited by 81 publications

References 8 publications

Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations

Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations

Thermal infrared remote sensing of mineral dust over land and ocean: a spectral SVD based retrieval approach for IASI

Infrared Continental Surface Emissivity Spectra and Skin Temperature Retrieved from IASI Observations over the Tropics

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