MODTRAN5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options

Berk, Alexander; Cooley, T.; Anderson, Gail P.; Acharya, Prabhat K.; Bernstein, Lawrence S.; Muratov, Leonid; Lee, Jamine; Fox, Marsha J.; Adler‐Golden, Steven M.; Chetwynd, James H.; Hoke, Michael L.; Lockwood, Ronald B.; Gardner, James A.; Lewis, Paul E.

doi:10.1117/12.564634

Cited by 44 publications

(46 citation statements)

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“…We compensated atmospheric disturbances present in the at-sensor radiance measurements and calculated hemispherical-conical reflectance factors (HCRF) using atmospheric parameters (i.e., surface irradiance, atmospheric transmittance, path scattered radiance, spherical albedo) calculated with the radiative transfer model MODTRAN5 [33]. The obtained atmospheric parameters were spectrally resampled based on the sensor characteristics.…”

Section: Parameters Derived From Spectroradiometric Measurementsmentioning

confidence: 99%

Combining Sun-Induced Chlorophyll Fluorescence and Photochemical Reflectance Index Improves Diurnal Modeling of Gross Primary Productivity

et al. 2016

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Sun-induced chlorophyll fluorescence (F) is a novel remote sensing parameter providing an estimate of actual photosynthetic rates. A combination of this new observable and Monteith's light use efficiency (LUE) concept was suggested for an advanced modeling of gross primary productivity (GPP). In this demonstration study, we evaluate the potential of both F and the more commonly used photochemical reflectance index (PRI) to approximate the LUE term in Monteith's equation and eventually improve the forward modeling of GPP diurnals. Both F and the PRI were derived from ground and airborne based spectrometer measurements over two different crops. We demonstrate that approximating dynamic changes of LUE using F and PRI significantly improves the forward modeling of GPP diurnals. Especially in sugar beet, a changing photosynthetic efficiency during the day was traceable with F and incorporating F in the forward modeling significantly improved the estimation of GPP. Airborne data were projected to produce F and PRI maps for winter wheat and sugar beet fields over the course of one day. We detected a significant variability of both, F and the PRI within one field and particularly between fields. The variability of F and PRI was higher in sugar beet, which also showed a physiological down-regulation of leaf photosynthesis. Our results underline the potential of F to serve as a superior indicator for the actual efficiency of the photosynthetic machinery, which is linked to physiological responses of vegetation.

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Section: Parameters Derived From Spectroradiometric Measurementsmentioning

confidence: 99%

Combining Sun-Induced Chlorophyll Fluorescence and Photochemical Reflectance Index Improves Diurnal Modeling of Gross Primary Productivity

et al. 2016

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“…The fundamental calibration parameters include the photon response nonuniformity correction (PRNU) to compensate for the vignetting effects and the nonuniformity of the detector [4,[13][14][15]23,24]; the sensor calibration parameters may also include the absolute calibration model from DNs to physical units of radiance. For absolutely calibrated sensors, the physically based atmospheric correction can be utilized; corrections for the path radiance, impacts of the adjacent objects and irradiance levels can be estimated utilizing the atmospheric radiative transfer codes, such as Modtran or 6S and the insitu observations of weather conditions [25][26][27]. Important challenges in the use of this approach in typical UAV operating scenarios include the missing calibration and potential instability of the UAV sensors and the fact that UAVs are often operated at low altitudes, where cloudy weather conditions are typical.…”

Section: Introductionmentioning

confidence: 99%

Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment

Honkavaara

Khoramshahi

2018

Remote Sensing

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Unmanned airborne vehicles (UAV) equipped with novel, miniaturized, 2D frame format hyper-and multispectral cameras make it possible to conduct remote sensing measurements cost-efficiently, with greater accuracy and detail. In the mapping process, the area of interest is covered by multiple, overlapping, small-format 2D images, which provide redundant information about the object. Radiometric correction of spectral image data is important for eliminating any external disturbance from the captured data. Corrections should include sensor, atmosphere and view/illumination geometry (bidirectional reflectance distribution function-BRDF) related disturbances. An additional complication is that UAV remote sensing campaigns are often carried out under difficult conditions, with varying illumination conditions and cloudiness. We have developed a global optimization approach for the radiometric correction of UAV image blocks, a radiometric block adjustment. The objective of this study was to implement and assess a combined adjustment approach, including comprehensive consideration of weighting of various observations. An empirical study was carried out using imagery captured using a hyperspectral 2D frame format camera of winter wheat crops. The dataset included four separate flights captured during a 2.5 h time period under sunny weather conditions. As outputs, we calculated orthophoto mosaics using the most nadir images and sampled multiple-view hyperspectral spectra for vegetation sample points utilizing multiple images in the dataset. The method provided an automated tool for radiometric correction, compensating for efficiently radiometric disturbances in the images. The global homogeneity factor improved from 12-16% to 4-6% with the corrections, and a reduction in disturbances could be observed in the spectra of the object points sampled from multiple overlapping images. Residuals in the grey and white reflectance panels were less than 5% of the reflectance for most of the spectral bands.

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“…Nevertheless, NASA recommends not using SWA for atmospheric correction [49]. Therefore, it is still necessary to use radiative transfer models such as MODTRAN [50][51][52][53] to remove the atmospheric effects occurring between the Earth's surface and the satellite sensor. These models simulate the attenuating and enhancing effect of atmospheric gases and aerosols on the TIR signal and are used to convert the at-sensor or top-of-atmosphere (TOA) radiance to surface-leaving radiance.…”

Section: Problems With Thermal Infrared Datamentioning

confidence: 99%

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Wicki

Parlow

2017

Remote Sensing

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Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data.

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MODTRAN5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options

Cited by 44 publications

References 0 publications

Combining Sun-Induced Chlorophyll Fluorescence and Photochemical Reflectance Index Improves Diurnal Modeling of Gross Primary Productivity

Combining Sun-Induced Chlorophyll Fluorescence and Photochemical Reflectance Index Improves Diurnal Modeling of Gross Primary Productivity

Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

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