MODerate Resolution Atmospheric TRANsmission Research Articles

Net Surface Shortwave Radiation Retrieval Using Random Forest Method With MODIS/AQUA Data

The net surface shortwave radiation (NSSR) at the Earth's surface drives evapotranspiration, photosynthesis, and other physical and biological processes. The primary objective of this study is to estimate NSSR in all sky conditions by using narrowband data of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the AQUA satellite. The random forest (RF) machine learning method for retrieving NSSR was developed with MODerate resolution atmospheric TRANsmission model (MODTRAN 5) simulated data. The bias, root mean square error (RMSE), and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the training dataset of the model are 0.04 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , 2.03 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , and 1.00, respectively; for testing data, these values are 0.53 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , 5.50 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , and 1.00, respectively. Note that the proposed method is better than the traditional method (RMSE 7.29 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ) with MODTRAN data, and the sky conditions (clear and cloudy) do not need to be distinguished in the RF method. Seven in situ measurements of the Surface Radiation (SURFRAD) observation network were used to validate the estimated NSSR with MODIS/AQUA data using the proposed RF method, and the bias, RMSE, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the comparison are -8.4 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , 76.8 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , and 0.91, respectively. Approximately 70% of the absolute difference of all the samples is below 50 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . Considering its concise process and relatively improved accuracy, both in regard to model development and validation, it can be concluded that the retrieval of NSSR with RF will be an efficient and feasible method in the future.

Flexible atmospheric compensation technique (FACT): a 6S based atmospheric correction scheme for remote sensing data

Atmospheric correction is an important pre-processing step in various spatio-temporal and multi-sensor remote sensing data analyzes based applications. Absolute atmospheric corrections are carried out using physical based models, generally known as radiative transfer (RT) codes such as MODerate resolution atmospheric TRANsmission (MODTRAN), Second Simulation of the Satellite Signal in the Solar Spectrum (6S) etc. Most of the available atmospheric correction schemes are commercially off-the-shelf and use patented RT codes. The objective of the present work is to develop an open-end atmospheric correction scheme, named as Flexible Atmospheric Compensation Technique (FACT), based on open source 6S RT code. The proposed FACT scheme utilizes look-up architecture for simulating the outputs of 6S RT code for various input parameters’ combination. Input parameters such as initial visibility, columnar water vapour are estimated using the dark object and the Continuum Interpolated Band Ratio (CIBR) methods respectively. The proposed FACT scheme has been evaluated exhaustively using spatio-spectral statistical error measures such as Spatial-Root Mean Square Error (S-RMSE), Spatial-Mean Absolute Error (S-MAE) and spectral-RMSE by comparing the performance with widely used Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) and the customized NASA JPL’s atmospheric correction scheme. Datasets from hyperspectral (AVIRIS-NG and Hyperion) and multispectral (LANDSAT-8 OLI and WorldView-3) remote sensors were chosen for comparative analysis of the developed atmospheric correction scheme against other atmospheric correction schemes. Results confirm that the proposed FACT scheme offers accuracy of about 95% for hyperspectral imaging sensors and close to 98% for multispectral imaging sensors when compared with FLAASH. Despite marginal disagreements for certain land cover features at the water vapour absorbing spectral regions, we find the proposed FACT scheme a plausible option for carrying out absolute atmospheric correction of various operational remote imaging sensors.

Atmospheric Correction for Tower-Based Solar-Induced Chlorophyll Fluorescence Observations at O2-A Band

Solar-induced chlorophyll fluorescence (SIF) has been proven to be an efficient indicator of vegetation photosynthesis. To investigate the relationship between SIF and Gross Primary Productivity (GPP), tower-based continuous spectral observations coordinated with eddy covariance (EC) measurements are needed. As the strong absorption effect at the O2-A absorption bands has an obvious influence on SIF retrieval based on the Fraunhofer Line Discrimination (FLD) principle, atmospheric correction is required even for tower-based SIF observations made with a sensor tens of meters above the canopy. In this study, an operational and simple solution for atmospheric correction of tower-based SIF observations at the O2-A band is proposed. The aerosol optical depth (AOD) and radiative transfer path length (RTPL) are found to be the dominant factors influencing the upward and downward transmittances at the oxygen absorption band. Look-up tables (LUTs) are established to estimate the atmosphere transmittance using AOD and RTPL based on the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) model simulations, and the AOD is estimated using the ratio of the downwelling irradiance at 790 nm to that at 660 nm (E790/E660). The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL based on an empirical equation. A series of field measurements were carried out to evaluate the performance of the atmospheric correction method for tower-based SIF observations. The difference between the SIF retrieved from tower-based and from ground-based observations decreased obviously after the atmospheric correction. The results indicate that the atmospheric correction method based on a LUT is efficient and also necessary for more accurate tower-based SIF retrieval, especially at the O2-A band.

Comparison of empirical and predicted ultraviolet aircraft signatures

In light of the potential threat to aircraft from missiles using ultraviolet (UV) wavebands, it is important to understand the signature of an aircraft and how this can be predicted. This study compares empirical UV signature data to modeled data from camouflage electro-optical simulation (CAMEOSIM) to determine how well the contrast between the object and the background can be predicted using local knowledge of the atmosphere. CAMEOSIM uses the standard moderate resolution atmospheric transmission (MODTRAN) model to estimate the radiative transfer through the atmosphere. Both MODTRAN and CAMEOSIM are well validated in visible and infrared wavebands, and MODTRAN can accurately predict UV radiative transfer. Unfortunately, the work so far has concentrated on bulk transfer to describe the sky background in the UV where the aircraft scene is typically a negative contrast “hole” in a positive sky background. Importantly, path-to-path scattering is a key consideration in this scene since it is this that will tend to blur the edges of an object and reduce the contrast associated with it. A developed understanding of the limitations is required. It is determined that prediction is possible up to the ranges of 5 km. The local visibility (in km) is required for this prediction.

Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

Many remote sensing sensors operate in similar spatial and spectral regions, which provides an opportunity to combine the data from different sensors to increase the temporal resolution for short and long-term trend analysis. However, combining the data requires understanding the characteristics of different sensors and presents additional challenges due to their variation in operational strategies, sensor differences and environmental conditions. These differences can introduce large variability in the time-series analysis, limiting the ability to model, predict and separate real change in signal from noise. Although the research community has identified the factors that cause variations, the magnitude or the effect of these factors have not been well explored and this is due to the limitations with the real-world data, where the effects of the factors cannot be separated. Our work mitigates these shortcomings by simulating the surface, atmosphere, and sensors in a virtual environment. We modeled and characterized a deciduous forest canopy and estimated its at-sensor response for the Landsat 8 (L8) and Sentinel 2 (S2) sensors using the MODerate resolution atmospheric TRANsmission (MODTRAN) modeled atmosphere. This paper presents the methods, analysis and the sensitivity of the factors that impacts multi-sensor observations for temporal analysis. Our study finds that atmospheric compensation is necessary as the variation due to the atmosphere can introduce an uncertainty as high as 40% in the Normalized Difference Vegetation Index (NDVI) products used in change detection and time-series applications. The effect due to the differences in the Relative Spectral Response (RSR) of the two sensors, if not compensated, can introduce uncertainty as high as 20% in the NDVI products. The view angle differences between the sensors can introduce uncertainty anywhere from 9% to 40% in NDVI depending on the atmospheric compensation methods. For a difference of 5 days in acquisition, the effect of solar zenith angle can vary between 4% to 10%, depending on whether the atmospheric attenuations are compensated or not for the NDVI products.

Retrieval of land surface properties from an annual time series of Landsat TOA radiances during a drought episode using coupled radiative transfer models

The accurate retrieval of land surface vegetation properties under varying environmental conditions from time series of moderately high spatial resolution satellite observations is challenging. By coupling various Radiative Transfer (RT) models one can describe the soil, vegetation and atmosphere contributions in a “bottom-up” approach and, thereby, simulate top-of-atmosphere (TOA) spectral radiance data comparable to satellite-observed TOA radiances. This makes it possible to retrieve vegetation properties directly from TOA radiances rather than from atmospherically corrected top-of-canopy (TOC) reflectance data. The advantages of this approach are that a separate atmospheric correction of the satellite images is not necessary, and that the anisotropic surface reflection can also be taken into account effectively. In this study, we coupled various RT models, including the brightness – shape – moisture (BSM) reflectance model of the soil, the optical radiative transfer (RTMo) model of vegetation and the ‘MODerate resolution atmospheric TRANsmission’ (MODTRAN) model of the atmosphere, to simulate an annual time series of Landsat satellite TOA radiances observed during a drought episode in California Mediterranean grasslands in 2004. The inversion of this coupled system through an optimization technique against Landsat TOA radiances resulted in direct retrieval of vegetation properties. We accommodated the surface anisotropic reflection in our coupled modeling and also defined a novel anisotropy index to quantitatively express the importance of this phenomenon in satellite image analysis for the first time. Our study showed that the coupled use of RT models was able to accurately reproduce the time series of observed TOA radiances collected under varying soil moisture contents during an extended drought episode. The proposed coupling approach is useful for successful retrieval of vegetation properties from time series of satellite TOA radiance data to produce maps of land surface properties and to monitor vegetation properties variations in an operational straightforward way. The approach can also be easily adapted for conducting multi-sensor time series studies, creating a much denser temporal sampling than would be possible for separate single sensors.

Anthropogenic aerosol emissions mapping and characterization by imaging spectroscopy – application to a metallurgical industry and a petrochemical complex

ABSTRACTThis paper is focused on the retrieval of industrial aerosol optical thickness (AOT) and microphysical properties by means of airborne imaging spectroscopy. Industrial emissions generally lead to optically thin plumes requiring an adapted detection method taking into account the weak proportion of particles sought in the atmosphere. To this end, a semi-analytical model combined with the Cluster-Tuned Matched Filter (CTMF) algorithm is presented to characterize those plumes, requiring the knowledge of the soil under the plume. The model allows the direct computation of the at-sensor radiance when a plume is included in the radiative transfer. When applied to industrial aerosol classes as defined in this paper, simulated spectral radiances can be compared to ‘real’ MODTRAN (Moderate Resolution Atmospheric Transmission) radiances using the Spectral Angle Mapper (SAM). On the range from 0.4 to 0.7 µm, for three grounds (water, vegetation, and bright one), SAM scores are lower than 0.043 in the worst case (a both absorbing and scattering particle over a bright ground), and usually lower than 0.025. The darker the ground reflectance is, the more accurate the results are (typically for reflectance lower than 0.3). Concerning AOT retrieval capabilities, with a pre-calculated model for a reference optical thickness of 0.25, we are able to retrieve plume AOT at 550 nm in the range 0.0 to 0.4 with an error usually ranging between 9% and 13%. The first test case is a CASI (Compact Airborne Spectrographic Imager) image acquired over the metallurgical industry of Fos-sur-Mer (France). First results of the use of the model coupled with CTMF algorithm reveal a scattering aerosol plume with particle sizes increasing with the distance from the stack (from detection score of 54% near the stack for particles with a diameter of 0.1 µm, to 69% away from it for 1.0 µm particles). A refinement is made then to estimate more precisely aerosol plume properties, using a multimodal distribution based on the previous results. It leads to find a mixture of sulfate and brown carbon particles with a plume AOT ranging between 0.2 and 0.5. The second test case is an AHS (Airborne Hyperspectral Scanner) image acquired over the petrochemical site of Antwerp (Belgium). The first CTMF application results in detecting a brown carbon aerosol of 0.1 µm mode (detection score is 51%). Refined results show the evolution of the AOT decreasing from 0.15 to 0.05 along the plume for a mixture of brown carbon fine mode and 0.3 µm radius of sulfate aerosol.

High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value &gt;0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area.

Uncertainty analysis of the flame temperature determination based on atmospheric absorption effect with optical emission spectroscopy

ABSTRACTFlame temperature, which is an important parameter in combustion diagnostics, provides a better understanding of flame radiative characteristics and can be deduced from the flame emission spectrum. However, in a combustion process with a large-scale flame that requires field measurement, considerable attenuation of the signature from the flame source occurs owing to the absorption of cold molecules in the atmosphere, which produces uncertainties while estimating the flame temperature. We analyze the effect of atmospheric absorption on the flame temperature estimation uncertainty with the optical emission spectroscopic method. We determine the flame temperature of IR (infrared) countermeasure flare combustion by fitting the reference spectrum model applying atmospheric absorption and without applying atmospheric absorption. Line-by-line modeling is conducted using a high-temperature molecular spectroscopic database. The instrument function of the spectrometer is applied, and the atmospheric transmission information is obtained by using the moderate-resolution atmospheric transmission database. In addition, we calculate the estimated flame temperature uncertainty without applying atmospheric absorption when the atmospheric path length, molecular concentration, and flame temperature are varied. Through the analysis, we suggest the wavelength range where the atmospheric absorption is negligible so that the uncertainty is minimized without applying it. Finally, we determine the flame temperature of the IR flare detected with an absolutely calibrated radiometric spectrometer by considering atmospheric absorption. All the fitting results show good agreement with experimental data.

Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier

The application of Landsat satellite imagery in land cover classification is affected by atmospheric and topographic errors, which have led to the development of different correction methods. In this study, moderate resolution atmospheric transmission (MODTRAN) and dark object subtraction (DOS) atmospheric corrections, and cosine topographic correction were evaluated individually and combined in a heterogeneous landscape in Zambia. These pre-processing methods were tested using a combination of object-based image analysis (OBIA) and Random Forests (RF) non-parametric classifier (hereafter referred to as OBIA-RF). This assessment aimed at understanding the combined effects of different pre-processing methods and the OBIA-RF classification method on the accuracy of Landsat operational land (OLI-8) imagery with different spatial resolutions. Here, we used pansharpened and standard Landsat OLI-8 images with 15 and 30 m spatial resolutions, respectively. The results showed that non pre-processed images reached a classification accuracy of 68% for pansharpened and 66% for standard Landsat OLI-8. Classification accuracy improved to 93% (pansharpened) and 86% (standard) when combined MODTRAN and cosine topographic correction pre-processing were applied. The results highlight the importance of pansharpening, as well as atmospheric and topographic corrections for Landsat OLI-8 imagery, when used as input in OBIA classification with the RF classifier.

Comparison of observed and modeled cloud-free longwave downward radiation (2010–2016) at the high mountain BSRN Izaña station

Abstract. A 7-year (2010–2016) comparison study between measured and simulated longwave downward radiation (LDR) under cloud-free conditions was performed at the Izaña Atmospheric Observatory (IZO, Spain). This analysis encompasses a total of 2062 cases distributed approximately evenly between day and night. Results show an excellent agreement between Baseline Surface Radiation Network (BSRN) measurements and simulations with libRadtran V2.0.1 and MODerate resolution atmospheric TRANsmission model (MODTRAN) V6 radiative transfer models (RTMs). Mean bias (simulated − measured) of &lt; 1.1 % and root mean square of the bias (RMS) of &lt; 1 % are within the instrumental error (2 %). These results highlight the good agreement between the two RTMs, proving to be useful tools for the quality control of LDR observations and for detecting temporal drifts in field instruments. The standard deviations of the residuals, associated with the RTM input parameters uncertainties are rather small, 0.47 and 0.49 % for libRadtran and MODTRAN, respectively, at daytime, and 0.49 to 0.51 % at night-time. For precipitable water vapor (PWV) &gt; 10 mm, the observed night-time difference between models and measurements is +5 W m−2 indicating a scale change of the World Infrared Standard Group of Pyrgeometers (WISG), which serves as reference for atmospheric longwave radiation measurements. Preliminary results suggest a possible impact of dust aerosol on infrared radiation during daytime that might not be correctly parametrized by the models, resulting in a slight underestimation of the modeled LDR, of about −3 W m−2, for relatively high aerosol optical depth (AOD &gt; 0.20).

Atmospheric Correction of Hyperspectral GCAS Airborne Measurements Over the North Atlantic Ocean and Louisiana Shelf

The Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) instrument has been used as a precursor for a hyperspectral instrument on the future geostationary satellite, yet its ability to “measure” ocean reflectance needs to be evaluated. Here, we demonstrate its capacity through vicarious calibration and atmospheric correction of data collected during flight campaigns over the Louisiana shelf in September 2013 and over the North Atlantic Ocean in November 2015. GCAS-measured at-sensor radiance was first vicariously calibrated using concurrent measurements by the Moderate Resolution Imaging Spectrometer (MODIS) and radiative transfer simulations with the MODerate resolution atmospheric TRANsmission (MODTRAN). Then, atmospheric correction has been implemented using MODTRAN-developed lookup tables and the traditional Gordon and Wang “black pixel” approach but with nonzero water-leaving radiance in the near-infrared accounted for through iteration. The atmospheric correction algorithm was applied to the vicariously calibrated GCAS imagery, with resulting $R_{\mathrm{ rs}}$ compared with concurrent MODIS $R_{\mathrm{ rs}}$ and in situ $R_{\mathrm{ rs}}$ . The comparison shows a mean relative difference of about 25% ( $N = 11$ ) between GCAS and in situ $R_{\mathrm{ rs}}$ in the blue–green bands for clear to moderately turbid waters.

Challenges in Methane Column Retrievals from AVIRIS-NG Imagery over Spectrally Cluttered Surfaces: A Sensitivity Analysis

A comparison between efforts to detect methane anomalies by a simple band ratio approach from the Airborne Visual Infrared Imaging Spectrometer-Classic (AVIRIS-C) data for the Kern Front oil field, Central California, and the Coal Oil Point marine hydrocarbon seep field, offshore southern California, was conducted. The detection succeeded for the marine source and failed for the terrestrial source, despite these sources being of comparable strength. Scene differences were investigated in higher spectral and spatial resolution collected by the AVIRIS-C successor instrument, AVIRIS Next Generation (AVIRIS-NG), by a sensitivity study. Sensitivity to factors including water vapor, aerosol, planetary boundary layer (PBL) structure, illumination and viewing angle, and surface albedo clutter were explored. The study used the residual radiance method, with sensitivity derived from MODTRAN (MODerate resolution atmospheric correction TRANsmission) simulations of column methane (XCH4). Simulations used the spectral specifications and geometries of AVIRIS-NG and were based on a uniform or an in situ vertical CH4 profile, which was measured concurrent with the AVIRIS-NG data. Small but significant sensitivity was found for PBL structure and water vapor; however, highly non-linear, extremely strong sensitivity was found for surface albedo error. For example, a 10% decrease in the surface albedo corresponded to a 300% XCH4 increase over background XCH4 to compensate for the total signal, less so for stronger plumes. This strong non-linear sensitivity resulted from the high percentage of surface-reflected radiance in the airborne at-sensor total radiance. Coarse spectral resolution and feedback from interferents like water vapor underlay this sensitivity. Imaging spectrometry like AVIRIS and the Hyperspectral InfraRed Imager (HyspIRI) candidate satellite mission, have the advantages of contextual spatial information and greater at-sensor total radiance. However, they also face challenges due to their relatively broad spectral resolution compared to trace gas specific orbital sensors, e.g., the Greenhouse gases Observing SATellite (GOSAT), which is especially applicable to trace gas retrievals over scenes with high spectral albedo variability. Results of the sensitivity analysis are applicable for the residual radiance method and CH4 profiles used in the analysis, but they illustrate potential significant challenges in CH4 retrievals using other approaches.

Radiometric calibration evaluation for RSBs of Suomi-NPP/VIIRS and Aqua/MODIS based on the 2015 Dunhuang Chinese Radiometric Calibration Site in situ measurements

ABSTRACTThe Dunhuang Chinese Radiometric Calibration Site (CRCS), used for the vicarious calibration (VC) of reflective solar bands (RSBs), was determined as the primary radiometric calibration site for Chinese space-borne optical sensors and was also selected in 2008 by the Working Group on Calibration and Validation of the Committee on Earth Observation Satellites as one of the instrumented reference sites. In August 2015, an in situ measurement was carried out at the Dunhuang site to evaluate the RSB radiometric calibration of the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (NPP) and Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua based on the reflectance-based method. A portable spectroradiometer was used in the experiment to obtain the surface reflectance, and the atmospheric parameters were obtained by sun photometers and radiosonde. A Dunhuang surface bidirectional reflectance distribution function model obtained during the field missions in 2008 and 2013 was implemented. Two days of in situ measurement data including 2 days of VIIRS data and 1 day of MODIS data were used for this evaluation. The results show that the radiometric calibration accuracy is within ±2% for most NPP/VIIRS and Aqua/MODIS RSBs based on the Dunhuang site. It should be noted that there is a relatively large difference in the NPP/VIIRS day–night band (DNB) and Aqua/MODIS band 7 results at the central wavelength of 2.1 μm, with biases of – 4.78% and – 5.71%, respectively. One factor contributing to the difference is the atmospheric transmittance calculation in these bands using the 6S radiative transfer model. If Moderate Resolution Atmospheric Transmission model is used for atmospheric transmittance correction, part of the bias of the MODIS band 7 and VIIRS DNB can be eliminated. However, the consistency of the VIIRS M11 and MODIS B7 is 3.47%, which is larger than that of the other bands.

Versatile time-dependent spatial distribution model of sun glint for satellite-based ocean imaging

We propose a versatile model to describe the time-dependent spatial distribution of sun glint areas in satellite-based wave water imaging. This model can be used to identify whether the imaging is affected by sun glint and how strong the glint is. The observing geometry is calculated using an accurate orbit prediction method. The Cox-Munk model is used to analyze the bidirectional reflectance of wave water surface under various conditions. The effects of whitecaps and the reflectance emerging from the sea water have been considered. Using the moderate resolution atmospheric transmission radiative transfer model, we are able to effectively calculate the sun glint distribution at the top of the atmosphere. By comparing the modeled data with the medium resolution imaging spectrometer image and Feng Yun 2E (FY-2E) image, we have proven that the time-dependent spatial distribution of sun glint areas can be effectively predicted. In addition, the main factors in determining sun glint distribution and the temporal variation rules of sun glint have been discussed. Our model can be used to design satellite orbits and should also be valuable in either eliminating sun glint or making use of it.

Fast and accurate hybrid stream PCRTM-SOLAR radiative transfer model for reflected solar spectrum simulation in the cloudy atmosphere.

A hybrid stream PCRTM-SOLAR model has been proposed for fast and accurate radiative transfer simulation. It calculates the reflected solar (RS) radiances with a fast coarse way and then, with the help of a pre-saved matrix, transforms the results to obtain the desired high accurate RS spectrum. The methodology has been demonstrated with the hybrid stream discrete ordinate (HSDO) radiative transfer (RT) model. The HSDO method calculates the monochromatic radiances using a 4-stream discrete ordinate method, where only a small number of monochromatic radiances are simulated with both 4-stream and a larger N-stream (N ≥ 16)discrete ordinate RT algorithm. The accuracy of the obtained channel radiance is comparable to the result from N-stream moderate resolution atmospheric transmission version 5 (MODTRAN5). The root-mean-square errors are usually less than 5x 10-4 mW/cm2/sr/cm-1. The computational speed is three to four-orders of magnitude faster than the medium speed correlated-k option MODTRAN5. This method is very efficient to simulate thousands of RS spectra under multi-layer clouds/aerosols and solar radiation conditions for climate change study and numerical weather prediction applications.

Spatial heterogeneity in geothermally-influenced lakes derived from atmospherically corrected Landsat thermal imagery and three-dimensional hydrodynamic modelling

Atmospheric correction of Landsat 7 thermal data was carried out for the purpose of retrieval of lake skin water temperature in Rotorua lakes, and Lake Taupo, North Island, New Zealand. The effect of the atmosphere was modelled using four sources of atmospheric profile data as input to the MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer model. The retrieved skin water temperatures were validated using a high-frequency temperature sensor deployed from a monitoring buoy at the water surface of Lake Rotorua. The most accurate atmospheric correction method was with Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric profile data (root-mean-square-error, RMSE, 0.48K), followed by radiosonde (0.52K), Atmospheric Infrared Sounder (AIRS) Level 3 (0.54K), and the NASA atmospheric correction parameter calculator (0.94K). Retrieved water temperature was used for assessing spatial heterogeneity and accuracy of surface water temperature simulated with a three-dimensional (3-D) hydrodynamic model of Lake Rotoehu, located approximately 20km east of Lake Rotorua. This comparison indicated that the model was suitable for reproducing the dominant horizontal variations in surface water temperature in the lake. This study demonstrated the potential of accurate satellite-based thermal monitoring to validate temperature outputs from 3-D hydrodynamic model simulations. It also provided atmospheric correction options for local and global applications of Landsat thermal data.

Atmospheric correction of APEX hyperspectral data

Abstract Atmospheric correction plays a crucial role among the processing steps applied to remotely sensed hyperspectral data. Atmospheric correction comprises a group of procedures needed to remove atmospheric effects from observed spectra, i.e. the transformation from at-sensor radiances to at-surface radiances or reflectances. In this paper we present the different steps in the atmospheric correction process for APEX hyperspectral data as applied by the Central Data Processing Center (CDPC) at the Flemish Institute for Technological Research (VITO, Mol, Belgium). The MODerate resolution atmospheric TRANsmission program (MODTRAN) is used to determine the source of radiation and for applying the actual atmospheric correction. As part of the overall correction process, supporting algorithms are provided in order to derive MODTRAN configuration parameters and to account for specific effects, e.g. correction for adjacency effects, haze and shadow correction, and topographic BRDF correction. The methods and theory underlying these corrections and an example of an application are presented.

Airglow simulation based on the Atmospheric Ultraviolet Radiance Integrated Code of 2012

The Atmospheric Ultraviolet Radiance Integrated Code (AURIC) is a software package developed by Computational Physics, Inc. (CPI) under the sponsorship of the Air Force Phillips Laboratory/Geophysics Directorate (PL/GP) (currently the Air Force Research Laboratory) for middle and upper atmospheric radiance modeling from the far ultraviolet to the near infrared. The AURIC has been considered as a general model for the radiative transfer simulation of airglow. Based on the theory of MODerate resolution atmospheric TRANsmission (MODTRAN), the AURIC extends calculation to altitudes above 100 km and the wavelength down to 80 nm. A package of AURIC v1.2 was released in 2002, which can be used for single-point simulation from 1947 to 1999. It means that the model is not suitable for atmospheric simulation of large datasets or for atmospheric parameters retrieval from amount of satellite measurements. In this paper, AURIC v1.2 is upgraded to AURIC-2012 based on MATLAB with improvements for modules of the Geomagnetic Parameter (GEOPARM), Atmosphere neutral composition (ATOMS), and Ionospheric electron density (IONOS). The improved AURIC can be used for global automatic airglow simulation and also for automatic retrieval of atmospheric compositions from satellite global observations, such as O/N2 and electron density etc. Besides, the model supplies possibilities for further improvement of airglow radiative mechanism and for substitution of other modules. Based on the AURIC-2012, Limb Column Emission Intensity (L-CEI) and Volume Emission Rate (VER) are calculated. For validation, the results were compared with measurements of the Global Ultraviolet Imager (GUVI) and TIMED Doppler Interferometer (TIDI), respectively. The averaged relative errors of L-CEI and VER at peak altitude are both within 20%. Finally, L-CEI varying with latitude, altitude, solar activity, and geomagnetic activity is simulated, and the distribution characteristics of the simulation and their influencing factors are analyzed subsequently.

Cross-Calibration of the HSI Sensor Reflective Solar Bands Using Hyperion Data

This paper describes a methodology that uses Hyperion imagery as reference data to calibrate the Chinese Hyperspectral Imager (HSI) onboard the HJ-1A satellite. Two test sites near Dunhuang in Gansu and in Inner Mongolia were used for the cross-calibration. To account for the uncertainties in the top of atmosphere (TOA) reflectance due to the bidirectional reflectance distribution function (BRDF) and relative spectral response (RSR) differences between the two sensors, a model is adopted to transfer the Hyperion TOA reflectance to the effective HSI TOA reflectance. The influence of BRDF is analyzed, and two BRDF correction approaches are applied to the two test sites, respectively. For the Dunhuang test site, the ground synchronously measured reflectance in different solar zenith angles is used for BRDF correction. For the Inner Mongolia test site, a kernel-driven model is applied. The influence of RSR mismatch is computed using a spectral profile adjustment factor (SPAF), which takes into account the spectral profile of the target and the RSR of each sensor. The SPAF is calculated according to the TOA reflectance simulated using moderate resolution atmospheric transmission. One-point calibration and multipoint calibration coefficients are computed, respectively. Ground reflectance data measured in June 2010 at the Inner Mongolia test site were used to validate the cross-calibration coefficients based on the Hyperion image. The results support the proposal that the cross-calibration method between two hyperspectral sensors is effective, and the use of multipoint calibration coefficient with nonzero offset has great potential for hyperspectral sensor calibration.