scholarly journals Atmospheric Correction of True-Color RGB Imagery with Limb Area-Blending Based on 6S and Satellite Image Enhancement Techniques Using Geo-Kompsat-2A Advanced Meteorological Imager Data

2021 ◽  
Author(s):  
Minsang Kim ◽  
Jun-Hyung Heo ◽  
Eun-Ha Sohn
Keyword(s):  
Satellite Image ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Radiative Transfer Model ◽  
High Temporal Resolution ◽  
Transfer Model ◽  
Green Band ◽  
Color Distribution ◽  
True Color ◽  
Color Imagery

AbstractThis study aims for producing high-quality true-color red-green-blue (RGB) imagery that is useful for interpreting various environmental phenomena, particularly for GK2A. Here we deal with an issue that general atmospheric correction methods for RGB imagery might be breakdown at high solar/viewing zenith angle of GK2A due to erroneous atmospheric path lengths. Additionally, there is another issue about the green band of GK2A of which centroid wavelength (510 nm) is different from that of natural green band (555 nm), resulting in the unrealistic RGB imagery. To overcome those weakness of the RGB imagery for GK2A, we apply the second simulation of the satellite signal in the solar spectrum radiative transfer model look-up table with improved information considering altitude of the reflective surface to reduce the exaggerated atmospheric correction, and a blending technique that mixed the true-color imagery before and after atmospheric correction which produced a naturally expressed true-color image. Consequently, the root mean square error decreased by 0.1–0.5 in accordance with the solar and view zenith angles. The green band signal was modified by combining it with a veggie band to form hybrid green which adjust centroid wavelength of approximately 550 nm. The original composite of true-color RGB imagery is dark; therefore, to brighten the imagery, histogram equalization is conducted to flatten the color distribution. High-temporal-resolution true-color imagery from the GK2A AMI have significant potential to provide scientists and forecasters as a tools to visualize the changing Earth and also expected to intuitively understand the atmospheric phenomenon to the general public.

scholarly journals Calibration of Satellite Low Radiance by AERONET-OC Products and 6SV Model

2021 ◽  
Vol 13 (4) ◽  
pp. 781
Author(s):  
Cristiana Bassani ◽  
Sindy Sterckx
Keyword(s):  
Optical Sensors ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Water Quality Monitoring ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Blue Band ◽  
Satellite Signal ◽  
Almost All ◽  
User Friendly

For water quality monitoring using satellite data, it is often required to optimize the low radiance signal through the application of radiometric gains. This work describes a procedure for the retrieval of radiometric gains to be applied to OLI/L8 and MSI/S2A data over coastal waters. The gains are defined by the ratio of the top of atmosphere (TOA) reflectance simulated using the Second Simulation of a Satellite Signal in the Solar Spectrum—vector (6SV) radiative transfer model, REF, and the TOA reflectance acquired by the sensor, MEAS, over AERONET-OC stations. The REF is simulated considering quasi-synchronous atmospheric and aquatic AERONET-OC products and the image acquisition geometry. Both for OLI/L8 and MSI/S2A the measured TOA reflectance was higher than the modeled signal in almost all bands resulting in radiometric gains less than 1. The use of retrieved gains showed an improvement of reflectance remote sensing, Rrs, when with ACOLITE atmospheric correction software. When the gains are applied an accuracy improvement of the Rrs in the 400–700 nm domain was observed except for the first blue band of both sensors. Furthermore, the developed procedure is quick, user-friendly, and easily transferable to other optical sensors.

scholarly journals DEVELOPMENT OF SCHEME FOR THE ATMOSPHERIC CORRECTION OF LANDSAT-8 OLI DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 671-674
Author(s):  
V. N. Pathak ◽  
M. R. Pandya ◽  
D. B. Shah ◽  
H. J. Trivedi
Keyword(s):  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Coefficient Of Determination ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Standard Data ◽  
Satellite Signal

<p><strong>Abstract.</strong> In the present study, a physics based method called Scheme for Atmospheric Correction of Landsat-8 (SACLS8) is developed for the Operational Land Imager (OLI) sensor of Landsat-8. The Second Simulation of the Satellite Signal in the Solar Spectrum Vector (6SV) radiative transfer model is used in the simulations to obtain the surface reflectance. The surface reflectance derived using the SACL8 scheme is validated with the <i>in-situ</i> measurements of surface reflectance carried out at the homogeneous desert site located in the Little Rann of Kutch, Gujarat, India. The results are also compared with Landsat-8 surface reflectance standard data product over the same site. The good agreement of results with high coefficient of determination (R<sup>2</sup><span class="thinspace"></span>><span class="thinspace"></span>0.94) and low root mean square error (of the order of 0.03) with <i>in-situ</i> measurement values as well as those obtained from the Landsat-8 surface reflectance data establishes a good performance of the SACLS8 scheme for the atmospheric correction of Landsat-8 dataset.</p>

Estimation of surface reflectance using deep neural network with KOMPSAT-3A data

2020 ◽  
Author(s):  
Daeseong Jung ◽  
Donghyun Jin ◽  
Sungwon Choi ◽  
Noh-hun Seong ◽  
Kyung-soo Han
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Image Data ◽  
Training Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
Input Variables

&lt;p&gt;The acquisition of image data from satellite is performed by the satellite&amp;#8217;s sensor after the light from the sun is reflected in object at the surface. In this process, light passes through the earth's atmosphere twice and is affected by the scattering, absorption and reflection by the atmosphere. This effect of the atmosphere reduces the power of the sun's light entering the sensor and consequently influences image data. The process of removing this effect is called atmospheric correction. Generally, the radiative transfer model (RTM) such as the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) is used in the atmospheric correction methods for surface reflectance retrieval. In general, RTM have high accuracy. But, RTM processing takes long time to perform atmospheric correction. So, several studies have applied the Look-up Table (LUT) method based on RTM. However, LUT is not an exact method due to the increment and range of input variables. In this study, we used the Deep Neural Network (DNN) method to predict surface reflectance for KOMPSAT-3A data. To Build an effective DNN model, 6S-based LUT is used as training data and the hyper-parameters have been adjusted. To evaluate the surface reflectance retrieval, we compared the surface reflectance derived of 6S RTM, 6S-based LUT and DNN methods.&lt;/p&gt;

scholarly journals An Approach to Minimize Atmospheric Correction Error and Improve Physics-Based Satellite-Derived Bathymetry in a Coastal Environment

2020 ◽  
Vol 12 (17) ◽  
pp. 2752
Author(s):  
Christopher O. Ilori ◽  
Anders Knudby
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Complete Solution ◽  
Atmospheric Correction ◽  
Florida Keys ◽  
Radiative Transfer Model ◽  
Inversion Method ◽  
Transfer Model ◽  
Landsat 8 ◽  
Depth Range

Physics-based radiative transfer model (RTM) inversion methods have been developed and implemented for satellite-derived bathymetry (SDB); however, precise atmospheric correction (AC) is required for robust bathymetry retrieval. In a previous study, we revealed that biases from AC may be related to imaging and environmental factors that are not considered sufficiently in all AC algorithms. Thus, the main aim of this study is to demonstrate how AC biases related to environmental factors can be minimized to improve SDB results. To achieve this, we first tested a physics-based inversion method to estimate bathymetry for a nearshore area in the Florida Keys, USA. Using a freely available water-based AC algorithm (ACOLITE), we used Landsat 8 (L8) images to derive per-pixel remote sensing reflectances, from which bathymetry was subsequently estimated. Then, we quantified known biases in the AC using a linear regression that estimated bias as a function of imaging and environmental factors and applied a correction to produce a new set of remote sensing reflectances. This correction improved bathymetry estimates for eight of the nine scenes we tested, with the resulting changes in bathymetry RMSE ranging from +0.09 m (worse) to −0.48 m (better) for a 1 to 25 m depth range, and from +0.07 m (worse) to −0.46 m (better) for an approximately 1 to 16 m depth range. In addition, we showed that an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, can be used to estimate bathymetry with a result that is similar to what can be obtained from the best individual scene. This approach can reduce time spent on the pre-screening and filtering of scenes. The correction method implemented in this study is not a complete solution to the challenge of AC for satellite-derived bathymetry, but it can eliminate the effects of biases inherent to individual AC algorithms and thus improve bathymetry retrieval. It may also be beneficial for use with other AC algorithms and for the estimation of seafloor habitat and water quality products, although further validation in different nearshore waters is required.

scholarly journals Local Tilt Optimization of Photovoltaic Solar Panels for Maximum Radiation Absorption

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Mauro Masili ◽  
Liliane Ventura
Keyword(s):  
Solar Radiation ◽  
Tilt Angle ◽  
Spectral Response ◽  
Solar Spectrum ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Radiation Absorption ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Solar Panels

Incident solar radiation on photovoltaic (PV) solar panels is not constant throughout the year. Besides dependence on the season, solar radiation is reliant on the location and weather conditions. For a given location on Earth, the best-fixed orientation of a PV panel can be determined by achieving the maximum incident solar irradiance throughout the year or for a predetermined period. In this paper, we use a sophisticated atmospheric radiative transfer model to calculate the direct and diffuse solar irradiation (radiant exposure) for the solar spectrum incident on PV solar panels to determine the best tilt angle of the panel in order to maximize absorption of solar radiation for selected periods. We used the Regula-Falsi numerical method to obtain the tilt angle at which the derivative of solar irradiation (concerning the tilt angle) approaches zero. Moreover, the spectral response of typical silicon cells is taken into account. These calculations were carried out in São Carlos (SP), a town in the southeast of Brazil. The best tilt angle was obtained for three selected periods. Additionally, we provide results for Southern latitudes ranging from 0° to −55° in steps of −5° for the meteorological seasons. We have shown that for each period, there is an increase in solar radiation absorption compared to the traditional installation angle based exclusively on the local latitude. These calculations can be extended to any location.

scholarly journals A Radiative Transfer Model-Based Multi-Layered Regression Learning to Estimate Shadow Map in Hyperspectral Images

2019 ◽  
Vol 1 (3) ◽  
pp. 904-927 ◽  
Author(s):  
Usman A. Zahidi ◽  
Ayan Chatterjee ◽  
Peter W. T. Yuen
Keyword(s):  
Radiative Transfer ◽  
Hyperspectral Image ◽  
Mathematical Formulation ◽  
False Negative ◽  
Atmospheric Correction ◽  
Ground Truth ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Regression Learning

The application of Empirical Line Method (ELM) for hyperspectral Atmospheric Compensation (AC) premises the underlying linear relationship between a material’s reflectance and appearance. ELM solves the Radiative Transfer (RT) equation under specialized constraint by means of in-scene white and black calibration panels. The reflectance of material is invariant to illumination. Exploiting this property, we articulated a mathematical formulation based on the RT model to create cost functions relating variably illuminated regions within a scene. In this paper, we propose multi-layered regression learning-based recovery of radiance components, i.e., total ground-reflected radiance and path radiance from reflectance and radiance images of the scene. These decomposed components represent terms in the RT equation and enable us to relate variable illumination. Therefore, we assume that Hyperspectral Image (HSI) radiance of the scene is provided and AC can be processed on it, preferably with QUick Atmospheric Correction (QUAC) algorithm. QUAC is preferred because it does not account for surface models. The output from the proposed algorithm is an intermediate map of the scene on which our mathematically derived binary and multi-label threshold is applied to classify shadowed and non-shadowed regions. Results from a satellite and airborne NADIR imagery are shown in this paper. Ground truth (GT) is generated by ray-tracing on a LIDAR-based surface model in the form of contour data, of the scene. Comparison of our results with GT implies that our algorithm’s binary classification shadow maps outperform other existing shadow detection algorithms in true positive, which is the detection of shadows when it is in ground truth. It also has the lowest false negative i.e., detecting non-shadowed region as shadowed, compared to existing algorithms.

Empirical Polarization Distribution Models for CLARREO-Imager Intercalibration

2016 ◽  
Vol 33 (3) ◽  
pp. 439-451 ◽  
Author(s):  
D. Goldin ◽  
C. Lukashin
Keyword(s):  
Linear Polarization ◽  
Infrared Imaging ◽  
Solar Spectrum ◽  
National Research Council ◽  
Real Data ◽  
Radiative Transfer Model ◽  
Degree Of Polarization ◽  
Transfer Model ◽  
Distribution Models ◽  
Polarization Distribution

AbstractPolarization effects bias the performance of various existing passive spaceborne instruments, such as MODIS and the Visible Infrared Imaging Radiometer Suite (VIIRS), as well as geostationary imagers. It is essential to evaluate and correct for these effects in order to achieve the required accuracy of the total reflectance at the top of the atmosphere.In addition to performing highly accurate decadal climate change observations, one of the objectives of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission recommended by the National Research Council for launch by NASA is to provide the on-orbit intercalibration with the imagers over a range of parameters, including polarization. Whenever the on-orbit coincident measurements are not possible, CLARREO will provide the polarization distributions constructed using the adding–doubling radiative transfer model (ADRTM), which will cover the entire reflected solar spectrum. These ADRTM results need to be validated using real data. To this end the empirical polarization distribution models (PDMs) based on the measurements taken by the Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar (PARASOL) mission were developed. Examples of such PDMs for the degree of polarization and the angle of linear polarization for the cloudless ocean scenes are shown here. These PDMs are compared across the three available PARASOL polarization bands, and the effect of aerosols on them is examined. The PDM-derived dependence of the reflectance uncertainty on the degree of polarization for imagers, such as MODIS or VIIRS, after their intercalibration with the CLARREO instrument is evaluated. The influence of the aerosols on the reflectance uncertainty is examined. Finally, the PDMs for the angle of linear polarization is cross-checked against the single-scattering approximation.

scholarly journals Technical Note: A novel parameterization of the transmissivity due to ozone absorption in the <i>k</i>-distribution method and correlated-<i>k</i> approximation of Kato et al. (1999) over the UV band

2015 ◽  
Vol 15 (13) ◽  
pp. 7449-7456 ◽  
Author(s):  
W. Wandji Nyamsi ◽  
A. Arola ◽  
P. Blanc ◽  
A. V. Lindfors ◽  
V. Cesnulyte ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Solar Spectrum ◽  
Ground Level ◽  
Technical Note ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Spectral Interval ◽  
Computationally Efficient ◽  
Ozone Absorption ◽  
Distribution Method

Abstract. The k-distribution method and the correlated-k approximation of Kato et al. (1999) is a computationally efficient approach originally designed for calculations of the broadband solar radiation at ground level by dividing the solar spectrum in 32 specific spectral bands from 240 to 4606 nm. Compared to a spectrally resolved computation, its performance in the UV band appears to be inaccurate, especially in the spectral intervals #3 [283, 307] nm and #4 [307, 328] nm because of inaccuracy in modeling the transmissivity due to ozone absorption. Numerical simulations presented in this paper indicate that a single effective ozone cross section is insufficient to accurately represent the transmissivity over each spectral interval. A novel parameterization of the transmissivity using more quadrature points yields maximum errors of respectively 0.0006 and 0.0143 for intervals #3 and #4. How to practically implement this new parameterization in a radiative transfer model is discussed for the case of libRadtran (library for radiative transfer). The new parameterization considerably improves the accuracy of the retrieval of irradiances in UV bands.

scholarly journals Long term Observations minus Background monitoring of ground-based microwave radiometer network. Part 1: Brightness Temperatures

2017 ◽  
Author(s):  
Francesco De Angelis ◽  
Domenico Cimini ◽  
Ulrich Löhnert ◽  
Olivier Caumont ◽  
Alexander Haefele ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Scale Model ◽  
Radiative Transfer Model ◽  
Line Center ◽  
High Temporal Resolution ◽  
Transfer Model ◽  
Observation Operator ◽  
Brightness Temperatures

Abstract. Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g., variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O-B). Monitoring of O-B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O-B analysis for MWR observations in clear sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O-B monitoring can effectively detect instrument malfunctions. O-B statistics (bias, standard deviation and root-mean-square) for water vapor channels (22.24–30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line center (~ 2–2.5 K) towards the high-frequency wing (~ 0.8–1.3 K). Statistics for zenith and lower elevation observations show a similar trend, though values increase with increasing air mass. O-B statistics for temperature channels show different behaviour for relatively transparent (51–53 GHz) and opaque channels (54-58 GHz). Opaque channels show lower uncertainties (

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