scholarly journals A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products From MODIS

2021 ◽  
Vol 2 ◽  
Author(s):  
Alexei Lyapustin ◽  
Feng Zhao ◽  
Yujie Wang
Keyword(s):  
Eastern China ◽  
Spectral Characteristics ◽  
Atmospheric Correction ◽  
Radiative Transfer Model ◽  
Surface Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
Cloud Detection ◽  
Wide Range ◽  
North Eastern

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03), central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals than MOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereas MOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

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.

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>

scholarly journals EVALUATION OF THE AEROSOL TYPE EFFECT ON THE SURFACE REFLECTANCE RETRIEVAL USING CHRIS/PROBA IMAGES OVER LAND

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1311-1316 ◽  
Author(s):  
C. Tirelli ◽  
C. Manzo ◽  
G. Curci ◽  
C. Bassani
Keyword(s):  
Land Surface ◽  
Transport Model ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Radiative Transfer Model ◽  
Urban Site ◽  
Transfer Model ◽  
Surface Reflectance ◽  
Aerosol Model ◽  
Aerosol Type

Surface reflectance has a central role in the analysis of land surface for a broad variety of agricultural, geological and urban studies. An accurate atmospheric correction, obtained by an appropriate selection of aerosol type and loading, is the first requirement for a reliable surface reflectance estimation. The aerosol type is defined by its micro-physical properties, while the aerosol loading is described by optical thickness at 550 nm. The aim of this work is to evaluate the radiative impact of the aerosol model on the surface reflectance obtained from CHRIS (Compact High Resolution Imaging Spectrometer) hyperspectral data over land by using the specifically developed algorithm CHRIS@CRI (CHRIS Atmospherically Corrected Reflectance Imagery) based on the 6SV radiative transfer model. Five different aerosol models have been used: one provided by the AERONET inversion products (used as reference), three standard aerosol models in 6SV, and one obtained from the output of the GEOS-Chem global chemistry-transport model (CTM). As test case the urban site of Bruxelles and the suburban area of Rome Tor Vergata have been considered. The results obtained encourages the use of CTM in operational retrieval and provides an evaluation of the role of the aerosol model in the atmospheric correction process, considering the different microphysical properties impact.

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 Retrieval of Surface Reflectance using SACRS2: a Scheme for Atmospheric Correction of ResourceSat-2 AWiFS data

2014 ◽  
Vol XL-8 ◽  
pp. 865-868 ◽  
Author(s):  
M. R. Pandya ◽  
V. N. Pathak ◽  
D. B. Shah ◽  
R.. P Singh
Keyword(s):  
Atmospheric Correction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Atmospheric Effects ◽  
Maximum Relative Error ◽  
Surface Reflectance ◽  
Water Vapour Content ◽  
Correction Model ◽  
Test Dataset ◽  
Spectral Bands

The Indian Remote Sensing (IRS) satellite series has been providing data since 1988 through various Earth observation missions. Before using IRS data for the quantitative analysis and parameter retrieval, it must be corrected for the atmospheric effects because spectral bands of IRS sensors are contaminated by intervening atmosphere. Standard atmospheric correction model tuned for the IRS sensors was not available for deriving surface reflectance. Looking at this gap area, a study was carried out to develop a physicsbased method, called SACRS2- a Scheme for Atmospheric Correction of Resourcesat-2 (RS2) AWiFS data. SACRS2 is a computationally fast scheme developed for correcting large amount of data acquired by RS2-AWiFS sensor using a detailed radiative transfer model 6SV. The method is based on deriving a set of coefficients which depend on spectral bands of the RS2-AWiFS sensor through thousands of forward signal simulations by 6SV. Once precise coefficients of all the physical processes of atmospheric correction are determined for RS2-AWiFS spectral bands then a complete scheme was developed using these coefficients. Major inputs of the SACRS2 scheme are raw digital numbers recorded by RS2-AWiFS sensor, aerosol optical thickness at 550 nm, columnar water vapour content, ozone content and viewing-geometry. Results showed a good performance of SACRS2 with a maximum relative error in the SACRS2 simulations ranged between approximately 2 to 7 percent with respect to reference 6SV computations. A complete software package containing the SACRS2 model along with user guide and test dataset has been released on the website (www.mosdac.gov.in) for the researchers.

scholarly journals NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part I: Algorithm Description

2005 ◽  
Vol 44 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Adam Dybbroe ◽  
Karl-Göran Karlsson ◽  
Anke Thoss
Keyword(s):  
Radiative Transfer ◽  
Land Surface ◽  
Short Range ◽  
Radiative Transfer Model ◽  
Actual State ◽  
Transfer Model ◽  
Cloud Detection ◽  
Cloud Type ◽  
Wide Range ◽  
Type Classification

Abstract New methods and software for cloud detection and classification at high and midlatitudes using Advanced Very High Resolution Radiometer (AVHRR) data are developed for use in a wide range of meteorological, climatological, land surface, and oceanic applications within the Satellite Application Facilities (SAFs) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), including the SAF for Nowcasting and Very Short Range Forecasting Applications (NWCSAF) project. The cloud mask employs smoothly varying (dynamic) thresholds that separate fully cloudy or cloud-contaminated fields of view from cloud-free conditions. Thresholds are adapted to the actual state of the atmosphere and surface and the sun–satellite viewing geometry using cloud-free radiative transfer model simulations. Both the cloud masking and the cloud-type classification are done using sequences of grouped threshold tests that employ both spectral and textural features. The cloud-type classification divides the cloudy pixels into 10 different categories: 5 opaque cloud types, 4 semitransparent clouds, and 1 subpixel cloud category. The threshold method is fuzzy in the sense that the distances in feature space to the thresholds are stored and are used to determine whether to stop or to continue testing. They are also used as a quality indicator of the final output. The atmospheric state should preferably be taken from a short-range NWP model, but the algorithms can also run with climatological fields as input.

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 Ozone formation under low solar radiation in eastern China

2019 ◽  
Author(s):  
Xuexi Tie ◽  
Xin Long ◽  
Guohui Li ◽  
Shuyu Zhao ◽  
Jianming Xu
Keyword(s):  
Solar Radiation ◽  
Eastern China ◽  
Threshold Level ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Oh Radicals ◽  
Chemical Production ◽  
The Past ◽  
Photochemical Production ◽  
Steady State Model

Abstract. PM2.5, a particulate matter with a diameter of 2.5 micrometers or less, is one of the major components of the air pollution in eastern China. In the past few years, China's government made strong efforts to reduce the PM2.5 pollutions. However, another important pollutant (ozone) becomes an important problem in eastern China. Ozone (O3) is produced by photochemistry, which requires solar radiation for the formation of O3. Under heavy PM2.5 pollution, the solar radiation is often depressed, and the photochemical production of O3 is prohibited. This study shows that during fall in eastern China, under heavy PM2.5 pollutions, there were often strong O3 photochemical productions, causing a co-occurrence of high PM2.5 and O3 concentrations. This co-occurrence of high PM2.5 and O3 is un-usual and is the main focus of this study. Recent measurements show that there were often high HONO surface concentrations in major Chinese mega cities, especially during daytime, with maximum concentrations ranging from 0.5 to 2 ppbv. It is also interesting to note that the high HONO concentrations were occurred during high aerosol concentration periods, suggesting that there were additional HONO surface sources in eastern China. Under the high daytime HONO concentrations, HONO can be photo-dissociated to be OH radicals, which enhance the photochemical production of O3. In order to study the above scientific issues, a radiative transfer model (TUV; Tropospheric Ultraviolet-Visible) is used in this study, and a chemical steady state model is established to calculate OH radical concentrations. The calculations show that by including the OH production of the photo-dissociated of HONO, the calculated OH concentrations are significantly higher than the values without including this production. For example, by including HONO production, the maximum of OH concentration under the high aerosol condition (AOD = 2.5) is similar to the value under low aerosol condition (AOD = 0.25) in the no-HONO case. This result suggests that even under the high aerosol condition, the chemical oxidizing process for O3 production can occurred, which explain the co-occurrence of high PM2.5 and high O3 in fall season in eastern China. However, the O3 concentrations were not significantly affected by the appearance of HONO in winter. This study shows that the seasonal variation of solar radiation plays important roles for controlling the OH production in winter. When the solar radiation is in a very low level in winter, it reaches the threshold level to prevent the OH chemical production, even by including the HONO production of OH. This study provides some important scientific highlights to better understand the O3 pollutions in eastern China.

scholarly journals Extensive reduction of surface UV radiation since 1750 in world's populated regions

2009 ◽  
Vol 9 (20) ◽  
pp. 7737-7751 ◽  
Author(s):  
M. M. Kvalev&amp;aring;g ◽  
G. Myhre ◽  
C. E. Lund Myhre
Keyword(s):  
Uv Radiation ◽  
Correlation Coefficients ◽  
Ultra Violet ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Polar Regions ◽  
Ultra Violet Radiation ◽  
Wide Range ◽  
Extensive Surface ◽  
Year 2000

Abstract. Human activity influences a wide range of components that affect the surface UV radiation levels, among them ozone at high latitudes. We calculate the effect of human-induced changes in the surface erythemally weighted ultra-violet radiation (UV-E) since 1750. We compare results from a radiative transfer model to surface UV-E radiation for year 2000 derived by satellite observations (from Total Ozone Mapping Spectroradiometer) and to ground based measurements at 14 sites. The model correlates well with the observations; the correlation coefficients are 0.97 and 0.98 for satellite and ground based measurements, respectively. In addition to the effect of changes in ozone, we also investigate the effect of changes in SO2, NO2, the direct and indirect effects of aerosols, albedo changes and aviation-induced contrails and cirrus. The results show an increase of surface UV-E in polar regions, most strongly in the Southern Hemisphere. Furthermore, our study also shows an extensive surface UV-E reduction over most land areas; a reduction up to 20% since 1750 is found in some industrialized regions. This reduction in UV-E over the industrial period is particularly large in highly populated regions.

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