The Experts below are selected from a list of 150 Experts worldwide ranked by ideXlab platform
Alexandre Royer - One of the best experts on this subject based on the ideXlab platform.
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Surface temperature and emissivity separability over land surface from combined tir and swir avhrr data
IEEE Transactions on Geoscience and Remote Sensing, 1997Co-Authors: Kalifa Goïta, Alexandre RoyerAbstract:This paper presents a method to recover land surface emissivity and temperature from radiance measurements without a priori assumptions on these parameters. The model combines spectral radiances in the short wave infrared (SWIR) domain (such as AVHRR channel 3: 3.55-3.93 μm) and the thermal infrared (TIR) domain (such as AVHRR channels 4: 10.5-11.5 μm or 5: 11.5-12.5 μm). The model assumes that the data have been previously accurately corrected for atmospheric effects and that emissivity separation from temperature can be decoupled from the atmospheric correction procedure. The approach is based on the estimation of the Reflected Component of the SWIR channel radiance after computing its thermal emitted Component derived from a TIR channel brightness temperature. A correction factor is introduced to account for the emissivity difference between the spectral channels, and the authors propose two methods for estimating it. In the first method, referred to as the TS-RAM model, this factor is estimated using a linear regression model from the ratio of the atmospherically corrected radiances of the SWIR and TIR channels. The regression model is established from theoretical simulations computed with the acquisition conditions and using a reference emissivity database. In the second method (the Δday model), the correction factor is estimated from consecutive data sets acquired the same day, assuming that the emissivity remains constant between the two acquisition times. Knowing the surface emissivity in the SWIR channel, the land surface temperature (LST) and other channel emissivities can then be easily retrieved. The results obtained from simulations in the context of AVHRR data show that rms errors for the TS-RAM model are around ±0.005 for channel 3 emissivity, ±0,01 for channels 4 and 5 emissivities and ±0.5 K for surface temperature. The Δday model performs better, with rms errors of ±0.002 and ±0.3 K, respectively, for the channel 3 emissivity and LST as the basic model assumption is held. The performance and sensitivity of the model were assessed for a wide range of surface types and ground thermal conditions as well as for different measurement conditions, i.e., viewing and solar zenith angles and atmospheric conditions. This assessment shows that an uncertainty of ±0.5 g cm-2 in atmospheric integrated water vapor content, under standard thermal conditions K), will introduce errors of the order of 2.5% in SWIR emissivity and 2.5 K in LST. The authors suggest that the model be applied to data acquired in near-nadir conditions (view angles up to 30°) to reduce the influence of directional effects. An application using some selected cloudless NOAA-AVHRR 10 and 11 images over Quebec shows that the proposed approach is promising for characterizing heterogeneous land surfaces
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Surface temperature and emissivity separability over land surface from combined TIR and SWIR AVHRR data
IEEE Transactions on Geoscience and Remote Sensing, 1997Co-Authors: Kalifa Goïta, Alexandre RoyerAbstract:This paper presents a method to recover land surface emissivity and temperature from radiance measurements without a priori assumptions on these parameters. The model combines spectral radiances in the short wave infrared (SWIR) domain (such as AVHRR channel 3: 3.55-3.93 /spl mu/m) and the thermal infrared (TIR) domain (such as AVHRR channels 4: 10.5-11.5 /spl mu/m or 5: 11.5-12.5 /spl mu/m). The model assumes that the data have been previously accurately corrected for atmospheric effects and that emissivity separation from temperature can be decoupled from the atmospheric correction procedure. The approach is based on the estimation of the Reflected Component of the SWIR channel radiance after computing its thermal emitted Component derived from a TIR channel brightness temperature. A correction factor is introduced to account for the emissivity difference between the spectral channels, and the authors propose two methods for estimating it. In the first method, referred to as the TS-RAM model, this factor is estimated using a linear regression model from the ratio of the atmospherically corrected radiances of the SWIR and TIR channels. The regression model is established from theoretical simulations computed with the acquisition conditions and using a reference emissivity database. In the second method (the /spl Delta/day model), the correction factor is estimated from consecutive data sets acquired the same day, assuming that the emissivity remains constant between the two acquisition times. Knowing the surface emissivity in the SWIR channel, the land surface temperature (LST) and other channel emissivities can then be easily retrieved. The results obtained from simulations in the context of AVHRR data show that rms errors for the TS-RAM model are around /spl plusmn/0.005 for channel 3 emissivity, /spl plusmn/0,01 for channels 4 and 5 emissivities and /spl plusmn/0.5 K for surface temperature. The /spl Delta/day model performs better, with rms errors of /spl plusmn/0.002 and /spl plusmn/0.3 K, respectively, for the channel 3 emissivity and LST as the basic model assumption is held. The performance and sensitivity of the model were assessed for a wide range of surface types and ground thermal conditions as well as for different measurement conditions, i.e., viewing and solar zenith angles and atmospheric conditions. This assessment shows that an uncertainty of /spl plusmn/0.5 g cm/sup -2/ in atmospheric integrated water vapor content, under standard thermal conditions (280 K/spl les/LST/spl les/300 K), will introduce errors of the order of 2.5% in SWIR emissivity and 2.5 K in LST. The authors suggest that the model be applied to data acquired in near-nadir conditions (view angles up to 30/spl deg/) to reduce the influence of directional effects. An application using some selected cloudless NOAA-AVHRR 10 and 11 images over Quebec shows that the proposed approach is promising for characterizing heterogeneous land surfaces.
Kalifa Goïta - One of the best experts on this subject based on the ideXlab platform.
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Surface temperature and emissivity separability over land surface from combined tir and swir avhrr data
IEEE Transactions on Geoscience and Remote Sensing, 1997Co-Authors: Kalifa Goïta, Alexandre RoyerAbstract:This paper presents a method to recover land surface emissivity and temperature from radiance measurements without a priori assumptions on these parameters. The model combines spectral radiances in the short wave infrared (SWIR) domain (such as AVHRR channel 3: 3.55-3.93 μm) and the thermal infrared (TIR) domain (such as AVHRR channels 4: 10.5-11.5 μm or 5: 11.5-12.5 μm). The model assumes that the data have been previously accurately corrected for atmospheric effects and that emissivity separation from temperature can be decoupled from the atmospheric correction procedure. The approach is based on the estimation of the Reflected Component of the SWIR channel radiance after computing its thermal emitted Component derived from a TIR channel brightness temperature. A correction factor is introduced to account for the emissivity difference between the spectral channels, and the authors propose two methods for estimating it. In the first method, referred to as the TS-RAM model, this factor is estimated using a linear regression model from the ratio of the atmospherically corrected radiances of the SWIR and TIR channels. The regression model is established from theoretical simulations computed with the acquisition conditions and using a reference emissivity database. In the second method (the Δday model), the correction factor is estimated from consecutive data sets acquired the same day, assuming that the emissivity remains constant between the two acquisition times. Knowing the surface emissivity in the SWIR channel, the land surface temperature (LST) and other channel emissivities can then be easily retrieved. The results obtained from simulations in the context of AVHRR data show that rms errors for the TS-RAM model are around ±0.005 for channel 3 emissivity, ±0,01 for channels 4 and 5 emissivities and ±0.5 K for surface temperature. The Δday model performs better, with rms errors of ±0.002 and ±0.3 K, respectively, for the channel 3 emissivity and LST as the basic model assumption is held. The performance and sensitivity of the model were assessed for a wide range of surface types and ground thermal conditions as well as for different measurement conditions, i.e., viewing and solar zenith angles and atmospheric conditions. This assessment shows that an uncertainty of ±0.5 g cm-2 in atmospheric integrated water vapor content, under standard thermal conditions K), will introduce errors of the order of 2.5% in SWIR emissivity and 2.5 K in LST. The authors suggest that the model be applied to data acquired in near-nadir conditions (view angles up to 30°) to reduce the influence of directional effects. An application using some selected cloudless NOAA-AVHRR 10 and 11 images over Quebec shows that the proposed approach is promising for characterizing heterogeneous land surfaces
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Surface temperature and emissivity separability over land surface from combined TIR and SWIR AVHRR data
IEEE Transactions on Geoscience and Remote Sensing, 1997Co-Authors: Kalifa Goïta, Alexandre RoyerAbstract:This paper presents a method to recover land surface emissivity and temperature from radiance measurements without a priori assumptions on these parameters. The model combines spectral radiances in the short wave infrared (SWIR) domain (such as AVHRR channel 3: 3.55-3.93 /spl mu/m) and the thermal infrared (TIR) domain (such as AVHRR channels 4: 10.5-11.5 /spl mu/m or 5: 11.5-12.5 /spl mu/m). The model assumes that the data have been previously accurately corrected for atmospheric effects and that emissivity separation from temperature can be decoupled from the atmospheric correction procedure. The approach is based on the estimation of the Reflected Component of the SWIR channel radiance after computing its thermal emitted Component derived from a TIR channel brightness temperature. A correction factor is introduced to account for the emissivity difference between the spectral channels, and the authors propose two methods for estimating it. In the first method, referred to as the TS-RAM model, this factor is estimated using a linear regression model from the ratio of the atmospherically corrected radiances of the SWIR and TIR channels. The regression model is established from theoretical simulations computed with the acquisition conditions and using a reference emissivity database. In the second method (the /spl Delta/day model), the correction factor is estimated from consecutive data sets acquired the same day, assuming that the emissivity remains constant between the two acquisition times. Knowing the surface emissivity in the SWIR channel, the land surface temperature (LST) and other channel emissivities can then be easily retrieved. The results obtained from simulations in the context of AVHRR data show that rms errors for the TS-RAM model are around /spl plusmn/0.005 for channel 3 emissivity, /spl plusmn/0,01 for channels 4 and 5 emissivities and /spl plusmn/0.5 K for surface temperature. The /spl Delta/day model performs better, with rms errors of /spl plusmn/0.002 and /spl plusmn/0.3 K, respectively, for the channel 3 emissivity and LST as the basic model assumption is held. The performance and sensitivity of the model were assessed for a wide range of surface types and ground thermal conditions as well as for different measurement conditions, i.e., viewing and solar zenith angles and atmospheric conditions. This assessment shows that an uncertainty of /spl plusmn/0.5 g cm/sup -2/ in atmospheric integrated water vapor content, under standard thermal conditions (280 K/spl les/LST/spl les/300 K), will introduce errors of the order of 2.5% in SWIR emissivity and 2.5 K in LST. The authors suggest that the model be applied to data acquired in near-nadir conditions (view angles up to 30/spl deg/) to reduce the influence of directional effects. An application using some selected cloudless NOAA-AVHRR 10 and 11 images over Quebec shows that the proposed approach is promising for characterizing heterogeneous land surfaces.
Pgj Irwin - One of the best experts on this subject based on the ideXlab platform.
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the solar Reflected Component in jupiter s 5 μm spectra from nims galileo observations
Journal of Geophysical Research, 1998Co-Authors: P. Drossart, Th. Encrenaz, Emmanuel Lellouch, Kevin H. Baines, Robert W. Carlson, L. W. Kamp, Glenn S. Orton, S. B. Calcutt, M Roosserote, Pgj IrwinAbstract:A comparison between low-flux dayside and nightside spectra of Jupiter recorded by the Galileo near-infrared mapping spectrometer (NIMS) experiment gives the first accurate estimate of the solar Reflected Component at 5 μm, in the equatorial zone of Jupiter. A minimum flux level of about 0.6 μ W cm−2 sr−1/μm is found on the dayside, compared with 0.1 μ W cm−2 sr−1/μm on the nightside. These fluxes are 100–800 times lower respectively than the bright 5-μm thermal emission in the north equatorial belt (NEB) hot spots. The day/night difference can be interpreted as a solar Reflected Component from a cloud, presumably the ammonia cloud, with an albedo of the order of 15%, located at a pressure level of 0.79 bar or at higher altitudes (corresponding to cloud temperature of 160 K or lower). Compared to the measurements in hot spots made at other wavelengths from ground-based observations and from NIMS real time spectra, they imply a high cloud opacity in cold regions at atmospheric levels where the cloud optical depth in the hot spots is very low. The residual flux on the nightside arises from (1) a very small cloud transparency giving some access to deeper thermal emission or (2) as high-resolution solid-state imaging (SSI) images of Galileo suggest, to cloud inhomogeneities, with clearer regions of medium brightness temperatures, mixed with dark regions of much lower thermal emission. If the former have the same brightness as a typical hot spot, a filling factor of a few percent is sufficient to explain the observed flux level on the nightside cold regions.
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The solar Reflected Component in Jupiter's 5-μm spectra from NIMS/Galileo observations
Journal of Geophysical Research, 1998Co-Authors: P. Drossart, M. Roos-serote, Th. Encrenaz, Emmanuel Lellouch, Kevin H. Baines, Robert W. Carlson, L. W. Kamp, Glenn S. Orton, S. B. Calcutt, Pgj IrwinAbstract:A comparison between low-flux dayside and nightside spectra of Jupiter recorded by the Galileo near-infrared mapping spectrometer (NIMS) experiment gives the first accurate estimate of the solar Reflected Component at 5 μm, in the equatorial zone of Jupiter. A minimum flux level of about 0.6 μ W cm−2 sr−1/μm is found on the dayside, compared with 0.1 μ W cm−2 sr−1/μm on the nightside. These fluxes are 100–800 times lower respectively than the bright 5-μm thermal emission in the north equatorial belt (NEB) hot spots. The day/night difference can be interpreted as a solar Reflected Component from a cloud, presumably the ammonia cloud, with an albedo of the order of 15%, located at a pressure level of 0.79 bar or at higher altitudes (corresponding to cloud temperature of 160 K or lower). Compared to the measurements in hot spots made at other wavelengths from ground-based observations and from NIMS real time spectra, they imply a high cloud opacity in cold regions at atmospheric levels where the cloud optical depth in the hot spots is very low. The residual flux on the nightside arises from (1) a very small cloud transparency giving some access to deeper thermal emission or (2) as high-resolution solid-state imaging (SSI) images of Galileo suggest, to cloud inhomogeneities, with clearer regions of medium brightness temperatures, mixed with dark regions of much lower thermal emission. If the former have the same brightness as a typical hot spot, a filling factor of a few percent is sufficient to explain the observed flux level on the nightside cold regions.
Danny H.w. Li - One of the best experts on this subject based on the ideXlab platform.
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Daylight and energy implications for CIE standard skies
Energy Conversion and Management, 2007Co-Authors: Danny H.w. LiAbstract:Recently, the International Commission on Illumination (CIE) has adopted a range of 15 standard skies, which include the existing CIE overcast, very clear and cloudless polluted skies, covering the whole probable spectrum of usual skies found in the world. The traditional daylight factor (DF) approach with the calculations being based on an isotropic overcast sky, however, cannot cater to the dynamic variations in daylight luminance and illuminance as the sun's position changes under non-overcast skies. Currently, we propose a numerical procedure that considers the changes in the luminance of sky elements to predict the interior daylight illuminance under the 15 CIE standard skies. This paper evaluates the method by using a typical room with a large vertical glazing window facing north. The available daylight for the room at mean hourly sun positions in each month in terms of DF and illuminance levels were determined and compared with those based on a computer program, namely, RADIANCE. A modification to the ground Reflected Component was made when a well defined shadow was cast in front of the window façade. It is shown that the results estimated by the proposed approach are in reasonably good agreement with those produced from RADIANCE. The interior daylight and lighting energy consumption were also determined using the proposed and the traditional DF approaches. The findings reveal that daylighting designs using existing CIE overcast sky only would considerably underestimate the indoor daylight availability and electric lighting energy savings, especially under high design indoor illuminance settings. © 2006 Elsevier Ltd. All rights reserved.
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A study of the daylighting performance and energy use in heavily obstructed residential buildings via computer simulation techniques
Energy and Buildings, 2006Co-Authors: Danny H.w. Li, C. L. Tsang, S.l. Wong, G. H.w. CheungAbstract:The quality and quantity of natural light entering a building depends on both internal and external factors. In Hong Kong, many buildings are high-rise blocks constructed close to each other and hence the external factor plays a significant role in daylighting designs. This paper studies the daylighting performance and energy use for residential flats facing large sky obstructions via computer simulations. Key building parameters affecting daylighting designs are presented. The daylighting performance for typical interior rooms was investigated in terms of illuminance level and daylight factor. The daylight levels of residential flats can be severely reduced by neighboring buildings and hence the externally Reflected Component would be the main source of natural light. The indoor daylight levels for kitchen and living/dining faced large neighboring building were found always less than the standard maintenance illuminance during daytime period. These imply that many residential flats in Hong Kong would have to rely on supplementary electric lighting. ?? 2006 Elsevier B.V. All rights reserved.
Ting-zhu Huang - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - Total Variation Regularized Low-Rank Sparsity Decomposition for Blind Cloud and Cloud Shadow Removal from Multitemporal Imagery
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Yong Chen, Wei He, Naoto Yokoya, Ting-zhu HuangAbstract:This paper proposes a spatial-spectral total variation (TV) regularized low-rank sparsity decomposition model for blind cloud and cloud shadow (cloud/shadow) detection and removal of multitemporal remote sensing imagery. Our concept is to decompose the contaminated image into the surface-Reflected Component and the cloud/shadow Component. Low-rank regularization is utilized to model the spectral-temporal correlation of the surface-Reflected Component, meanwhile, the ` 1 -norm and spatial-spectral total variation regularization is employed to describe the sparse prior and spatial-spectral continuity of the cloud/shadow Component. To better preserve the information in cloud/shadow-free areas, the cloud/shadow detection results obtained as a by-product of our method are used to guide the information compensation from the original contaminated images. Several experiments are presented to demonstrate the effectiveness of the proposed method.
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Total Variation Regularized Low-Rank Sparsity Decomposition for Blind Cloud and Cloud Shadow Removal from Multitemporal Imagery
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Yong Chen, Wei He, Naoto Yokoya, Ting-zhu HuangAbstract:This paper proposes a spatial-spectral total variation (TV) regularized low-rank sparsity decomposition model for blind cloud and cloud shadow (cloud/shadow) detection and removal of multitemporal remote sensing imagery. Our concept is to decompose the contaminated image into the surface-Reflected Component and the cloud/shadow Component. Low-rank regularization is utilized to model the spectral-temporal correlation of the surface-Reflected Component, meanwhile, the `1-norm and spatial-spectral total variation regularization is employed to describe the sparse prior and spatial-spectral continuity of the cloud/shadow Component. To better preserve the information in cloud/shadow-free areas, the cloud/shadow detection results obtained as a by-product of our method are used to guide the information compensation from the original contaminated images. Several experiments are presented to demonstrate the effectiveness of the proposed method.