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

<p>The acquisition of image data from satellite is performed by the satellite’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.</p>

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 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 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.

scholarly journals Radiometric Variations of On-Orbit FORMOSAT-5 RSI from Vicarious and Cross-Calibration Measurements

2019 ◽  
Vol 11 (22) ◽  
pp. 2634 ◽  
Author(s):  
Tang-Huang Lin ◽  
Jui-Chung Chang ◽  
Kuo-Hsien Hsu ◽  
Yun-Shan Lee ◽  
Sheng-Kai Zeng ◽  
...  
Keyword(s):  
Field Experiments ◽  
High Surface ◽  
Solar Spectrum ◽  
Calibration Coefficient ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Atmospheric Effects ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Cross Calibration

A new Taiwanese satellite, FORMOSAT-5 (FS-5), with a payload remote sensing instrument (RSI) was launched in August 2017 to continue the mission of its predecessor FORMOSAT-2 (FS-2). Similar to FS-2, the RSI provides 2-m resolution panchromatic and 4-m resolution multi-spectral images as the primary payload on FS-5. However, the radiometric properties of the optical sensor may vary, based on the environment and time after being launched into the space. Thus, maintaining the radiometric quality of FS-5 RSI imagery is essential and significant to scientific research and further applications. Therefore, the objective of this study aimed at the on-orbit absolute radiometric assessment and calibration of on-orbit FS-5 RSI observations. Two renowned approaches, vicarious calibrations and cross-calibrations, were conducted at two calibration sites that employ a stable atmosphere and high surface reflectance, namely, Alkali Lake and Railroad Valley Playa in North America. For cross-calibrations, the Landsat-8 Operational Land Imager (LS-8 OLI) was selected as the reference. A second simulation of the satellite signal in the solar spectrum (6S) radiative transfer model was performed to compute the surface reflectance, atmospheric effects, and path radiance for the radiometric intensity at the top of the atmosphere. Results of vicarious calibrations from 11 field experiments demonstrated high consistency with those of seven case examinations of cross-calibration in terms of physical gain in spectra, implying that the practicality of the proposed approaches is high. Moreover, the multi-temporal results illustrated that RSI decay in optical sensitivity was evident after launch. The variation in the calibration coefficient of each band showed no obvious consistency (6%–24%) in 2017, but it tended to be stable at the order of 3%–5% of variation in most spectral bands during 2018. The results strongly suggest that periodical calibration is required and essential for further scientific applications.

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 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 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 Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)

2020 ◽  
Vol 12 (19) ◽  
pp. 3160
Author(s):  
Yan Zhou ◽  
Christopher Grassotti
Keyword(s):  
Neural Network ◽  
Bias Correction ◽  
Retrieval System ◽  
Forward Model ◽  
Training Data ◽  
Advanced Technology ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Neural Network Approach ◽  
Dynamic Bias

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

Consistent retrieval of cloud/aerosol single scattering properties and surface reflectance

2021 ◽  
Author(s):  
Marta Luffarelli ◽  
Yves Govaerts
Keyword(s):  
Radiative Transfer ◽  
Optical Thickness ◽  
Solution Space ◽  
Single Scattering ◽  
Aerosol Optical Thickness ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
Earth System ◽  
System Components

&lt;p&gt;The CISAR (Combined Inversion of Surface and AeRosols) algorithm is exploited in the framework of the ESA Aerosol Climate Change Initiatiave (CCI) project, aiming at providing a set of atmospheric (cloud and aerosol) and surface reflectance products derived from S3A/SLSTR observations using the same radiative transfer physics and assumptions. CISAR is an advance algorithm developed by Rayference originally designed for the retrieval of aerosol single scattering properties and surface reflectance from both geostationary and polar orbiting satellite observations. &amp;#160;It is based on the inversion of a fast radiative transfer model (FASTRE). The retrieval mechanism allows a continuous variation of the aerosol and cloud single scattering properties in the solution space.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Traditionally, different approaches are exploited to retrieve the different Earth system components, which could lead to inconsistent data sets. The simultaneous retrieval of different atmospheric and surface variables over any type of surface (including bright surfaces and water bodies) with the same forward model and inversion scheme ensures the consistency among the retrieved Earth system components. Additionally, pixels located in the transition zone between pure clouds and pure aerosols are often discarded from both cloud and aerosol algorithms. This &amp;#8220;twilight zone&amp;#8221; can cover up to 30% of the globe. A consistent retrieval of both cloud and aerosol single scattering properties with the same algorithm could help filling this gap.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The CISAR algorithm aims at overcoming the need of an external cloud mask, discriminating internally between aerosol and cloud properties. This approach helps reducing the overestimation of aerosol optical thickness in cloud contaminated pixels. The surface reflectance product is delivered both for cloud-free and cloudy observations. &amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Global maps obtained from the processing of S3A/SLSTR observations will be shown. The SLSTR/CISAR products over events such as, for instance, the Australian fire in the last months of 2019, will be discussed in terms of aerosol optical thickness, aerosol-cloud discrimination and fine/coarse mode fraction.&lt;/p&gt;

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