Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models

Verhoef, W.; Bach, Heike

doi:10.1016/s0034-4257(03)00143-3

Cited by 286 publications

(173 citation statements)

References 8 publications

Supporting

Mentioning

168

Contrasting

Unclassified

Order By: Relevance

“…The basic atmospheric effect model is well described in [10,[28][29][30][31]. We use MODerate resolution atmospheric TRANsmission version 4.1 (MODTRAN 4) [32] to estimate the radiance components in Equation (8).…”

Section: Basic Atmospheric Effect Modelingmentioning

confidence: 99%

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

et al. 2018

View full text Add to dashboard Cite

Abstract:A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance (L rs,t (λ)). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference. We propose a method that determines pre-estimates of surface reflectance (ρ t,pre ) where effects associated with L rs,t (λ) are less influential. The method identifies pixels comprising pure materials from ρ t,pre . AOD values at the pure pixels are iteratively estimated using l 2 -norm optimization. Using the adjacency range function, the AOD is estimated at each pixel. We applied the method on Hyperspectral Mapper and Airborne Prism Experiment instruments for experiments on synthetic data and on real data. To simulate real imaging conditions, noise was added to the data. The estimation error of the AOD is minimized to 0.06-0.08 with a signal-to-reconstruction-error equal to 35 dB. We compared the proposed method with a dense dark vegetation (DDV)-based state-of-the-art method. This reference method, resulted in a larger variability in AOD estimates resulting in low signal-to-reconstruction-error between 5-10 dB. For per-pixel estimation of AOD, the performance of the reference method further degraded. We conclude that the proposed method is more precise than the DDV methods and can be extended to other AC parameters.

show abstract

Section: Basic Atmospheric Effect Modelingmentioning

confidence: 99%

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Hence, we generated pseudo match-up data through radiative transfer model simulations using MODTRAN4 over a wide range of atmospheric and surface conditions. In this paper, MODTRAN4 [36] has been used, because it represents the state-of-the-art in realistic computing of absorption and scattering in the terrestrial atmosphere at high spectral resolution (1 cm ) over the various infrared spectral ranges, providing accurate simulations of atmospheric radiative transfer [37][38][39]. Furthermore, the model was based on the angular dependence of the detected radiance, similar to what has been done in previous studies [2,31,32,40].…”

Section: Development Of Lst Retrieval Algorithmmentioning

confidence: 99%

Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data

Cho

Suh

2013

Remote Sensing

View full text Add to dashboard Cite

Abstract:We evaluated the precision of land surface temperature (LST) operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS). The split-window (SW)-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface emissivity values and surface lapse rate conditions using Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4). The estimation capabilities of the COMS SW (CSW) LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The surface emissivity values for two SW channels were generated using a vegetation cover method. The CSW algorithm estimated the LST distribution reasonably well (averaged bias = 0.00 K, Root Mean Square Error (RMSE) = 1.41 K, correlation coefficient = 0.99); however, the estimation capabilities of the CSW algorithm were significantly impacted by large brightness temperature differences and surface lapse rates. The CSW algorithm reproduced spatiotemporal variations of LST comparing well to MODIS LST data, irrespective of what month or time of day the data were collected from. The one-year evaluation results with MODIS LST data showed that the annual mean bias, RMSE and correlation coefficient for the CSW algorithm were −1.009 K, 2.613 K and 0.988, respectively.

show abstract

“…In both settings, based on low simulated aerosol concentrations and low MODIS aerosol optical depth retrievals over the study area, the default aerosol attenuation of maritime extinction with 23 km visibility (MODTRAN4 default) was used in conjunction with the Navy Oceanic Vertical Aerosol Model (NOVAM). Radiative transfer was simulated three times for each profile for a simulated Lambertian surface with spectrally flat surface albedos of 0.0, 0.5 and 1.0, which allowed for the estimation of ground reflectance for the central wavelength of each MASTER band (Verhoef and Bach 2003). Ground temperature was determined by first determining upwelling ground radiance, then inverting the Planck equation with an assumed emissivity of 0.98 (Schmugge et al 2002).…”

Section: Radiative Transfer Modellingmentioning

confidence: 99%

“…The signal detected by a remote sensor is the overall result of three main radiative contributions: direct reflection from the target, scattering from the atmosphere and reflected radiation (from the target and elsewhere) diffusely transmitted to the sensor (Verhoef and Bach 2003). The radiance measured from land and ocean ecosystems at the Earth's surface may be biased by the radiance of atmospheric constituents located between the surface and the sensor (Adler-Golden et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Atmospheric correction for MASTER image data using localized modelled and observed meteorology and trace gases

Tkacik

Luna-Cruz

Clinton

et al. 2012

Remote Sensing Letters

View full text Add to dashboard Cite

Atmospheric correction for remote sensing-based studies typically does not use information from spatio-temporally resolved meteorological models. We assessed the effect of using observations and mesoscale weather and chemical transport models on multispectral retrievals of land and ocean properties. We performed two atmospheric corrections on image data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS)/Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) airborne simulator over Monterey Bay, California. One correction used local atmospheric profiles of meteorology and trace gases at overpass and the other used the 1976 US Standard default atmospheric profile in the MODTRAN4 radiative transfer model. We found only minor impacts from atmospheric correction in the Fluorescence Line Height index of ocean chlorophyll, but substantive differences in retrievals of surface temperature and the Normalized Difference Vegetation Index. Improvements in sea surface temperature retrieval were validated by in situ measurements. Results indicate that spatio-temporally specific atmospheric correction factors from mesoscale models can improve retrievals of surface properties from remotely sensed image data.

show abstract

Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models

Cited by 286 publications

References 8 publications

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data

Atmospheric correction for MASTER image data using localized modelled and observed meteorology and trace gases

Contact Info

Product

Resources

About