Optimization of the Advanced Earth Observing Satellite II Global Imager channels by use of radiative transfer calculations

Nakajima, Takashi Y.; Nakajima, Teruyuki; Nakajima, M.; Fukushima, Hajime; Kuji, Makoto; Uchiyama, Akihiro; Kishino, Motoaki

doi:10.1364/ao.37.003149

Cited by 61 publications

(30 citation statements)

References 14 publications

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“…The highest temporal samplings are provided by geostationary sensors: 15-30 min with GOES Imager [81] and MSG/SEVIRI [82], corresponding to ground resolutions between 2 and 4 km. Intermediate scales correspond to kilometric resolution sensors onboard sunsynchronous platforms, providing daily nighttime and daytime observations: NOAA/AVHRR [83], ADEOS/GLI [84], and Terra-Aqua/MODIS [85]. A 3 day temporal sampling with a 1 km resolution has been provided by ERS/ATSR-1 and -2, and ENVISAT/AATSR [86].…”

Section: Temporal and Spatial Capabilitiesmentioning

confidence: 99%

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Jacob¹,

Schmugge

Olioso

et al. 2008

Advances in Land Remote Sensing

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U n c o r r e c t e d P r o o f 244 F. Jacob et al.Abstract Thermal Infra Red (TIR) Remote sensing allow spatializing various land surface temperatures: ensemble brightness, radiometric and aerodynamic temperatures, soil and vegetation temperatures optionally sunlit and shaded, and canopy temperature profile. These are of interest for monitoring vegetated land surface processes: heat and mass exchanges, soil respiration and vegetation physiological activity. TIR remote sensors collect information according to spectral, directional, temporal and spatial dimensions. Inferring temperatures from measurements relies on developing and inverting modeling tools. Simple radiative transfer equations directly link measurements and variables of interest, and can be analytically inverted. Simulation models allow linking radiative regime to measurements. They require indirect inversions by minimizing differences between simulations and observations, or by calibrating simple equations and inductive learning methods. In both cases, inversion consists of solving an ill posed problem, with several parameters to be constrained from few information. Brightness and radiometric temperatures have been inferred by inverting simulation models and simple radiative transfer equations, designed for atmosphere and land surfaces. Obtained accuracies suggest refining the use of spectral and temporal information, rather than innovative approaches. Forthcoming challenge is recovering more elaborated temperatures. Soil and vegetation components can replace aerodynamic temperature, which retrieval seems almost impossible. They can be inferred using multiangular measurements, via simple radiative transfer equations previously parameterized from simulation models. Retrieving sunlit and shaded components or canopy temperature profile requires inverting simulation models. Then, additional difficulties are the influence of thermal regime, and the limitations of spaceborne observations which have to be along track due to the temperature fluctuations. Finally, forefront investigations focus on adequately using TIR information with various spatial resolutions and temporal samplings, to monitor the considered processes with adequate spatial and temporal scales. IntroductionUsing TIR remote sensing for environmental issues have been investigated the last three decades. This is motivated by the potential of the spatialized information for documenting the considered processes within and between the Earth system components: cryosphere [1-2], atmosphere [3][4][5][6], oceans [7][8][9], and land surfaces [10]. For the latter, TIR remote sensing is used to monitor forested areas [11][12][13][14], urban areas [15][16][17], and vegetated areas. We focus here on vegetated areas, natural and cultivated. The monitored processes are related to climatology, meteorology, hydrology and agronomy: (1) radiation, heat and water transfers at the soil-vegetation-atmosphere interface [18][19][20][21][22][23][24]; (2) interactions between land surface and atmospheric boundary la...

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Section: Temporal and Spatial Capabilitiesmentioning

confidence: 99%

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Jacob¹,

Schmugge

Olioso

et al. 2008

Advances in Land Remote Sensing

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“…Global Imager onboard Advanced Earth Observing Satellite-II (GLI/ADEOS-II) is a nadir-scanning optical imager for global observation of cloud, aerosol, water vapor as well as surface properties of vegetations, ocean color, sea surface temperature, and snow/ice (Nakajima et al 1998). ADEOS-II is a sun-synchronous polar orbiter, and GLI observes a whole globe in four days.…”

Section: Satellite and In Situ Observationsmentioning

confidence: 99%

Retrieval of Asian Dust Amount over Land using ADEOS-II/GLI near UV Data

Kuji

Yamanaka

Hayashida

et al. 2005

SOLA

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We propose a retrieval method of Asian dust (Yellow sand or Kosa aerosol) columnar amount around source regions using a near ultraviolet radiometry observation from space. The method simultaneously retrieves an optical thickness and mode radius of Kosa aerosol, and then derives its columnar amount. The method was applied to ADEOS-II/GLI data in the spring of 2003 around Taklimakan desert source region, inland China. The retrieved optical thickness and mode radius were about 0.34 and 1.75 µm, respectively, at a validation site. They are comparable to the in situ observations conducted within the framework of ADEC project. The estimated columnar amount around a validation site is about 2.77 g m 2 , which seems reasonable under a relatively calm situation. The method should be further validated with a regional model simulation study, and then it is useful to monitor Asian dust around source regions from space in the future.

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“…Examples include the Along-Track Scanning Radiometer (ATSR) on board the European Remote Sensing Satellite-1 (ERS-1) and -2 and the Environmental Satellite (ENVISAT ), the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission (TRMM) spacecraft (Barnes et al 2000), the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecrafts (Barnes et al 1998), and the Global Imager (GLI) on Midori-II [Advanced Earth Observing Satellite-II (ADEOS-II); Nakajima et al 1998]. On geosynchronous platforms, 3.9-m channels are available on the new Geostationary Operational Environmental Satellite (GOES) Imager and the Meteosat Second-Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI; Aminou 2002).…”

Section: Introductionmentioning

confidence: 99%

Model Calculations of Solar Spectral Irradiance in the 3.7-μm Band for Earth Remote Sensing Applications

Platnick

Fontenla

2008

Journal of Applied Meteorology and Climatology

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Since the launch of the first Advanced Very High Resolution Radiometer (AVHRR) instrument aboard the Television and Infrared Observational Satellite (TIROS-N), measurements in the 3.7-m atmospheric window have been exploited for use in cloud detection and screening, cloud thermodynamic phase and surface snow/ice discrimination, and quantitative cloud particle size retrievals. The utility of the band has led to the incorporation of similar channels on a number of existing satellite imagers and future operational imagers. Daytime observations in the band include both reflected solar and thermal emission energy. Since 3.7-m channels are calibrated to a radiance scale (via onboard blackbodies), knowledge of the top-ofatmosphere solar irradiance in the spectral region is required to infer reflectance. Despite the ubiquity of 3.7-m channels, absolute solar spectral irradiance data come from either a single measurement campaign (Thekaekara et al.) or synthetic spectra. In the current study, the historical 3.7-m band spectral irradiance datasets are compared with the recent semiempirical solar model of the quiet sun by Fontenla et al. The model has expected uncertainties of about 2% in the 3.7-m spectral region. The channel-averaged spectral irradiances using the observations reported by Thekaekara et al. are found to be 3.2%-4.1% greater than those derived from the Fontenla et al. model for Moderate Resolution Imaging Spectroradiometer (MO-DIS) and AVHRR instrument bandpasses; the Kurucz spectrum, as included in the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN4) distribution, gives channel-averaged irradiances 1.2%-1.5% smaller than the Fontenla model. For the MODIS instrument, these solar irradiance uncertainties result in cloud microphysical retrieval uncertainties that are comparable to other fundamental reflectance error sources.

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Optimization of the Advanced Earth Observing Satellite II Global Imager channels by use of radiative transfer calculations

Cited by 61 publications

References 14 publications

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Retrieval of Asian Dust Amount over Land using ADEOS-II/GLI near UV Data

Model Calculations of Solar Spectral Irradiance in the 3.7-μm Band for Earth Remote Sensing Applications

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