scholarly journals Calibration of Automatic Sun Photometer with Temperature Correction in Field Environment

2021 ◽  
Vol 14 (1) ◽  
pp. 66
Author(s):  
Shuyu Chen ◽  
Yuan Li ◽  
Fengmei Cao ◽  
Yuxiang Zhang
Keyword(s):  
Indium Gallium Arsenide ◽  
Atmospheric Correction ◽  
Temperature Correction ◽  
Large Temperature ◽  
Field Environment ◽  
Before And After ◽  
Sun Photometer ◽  
Nonlinear Regression Method ◽  
Calibration Coefficients ◽  
Ingaas Detector

Aerosol optical depth (AOD) is an important atmospheric correction parameter in remote sensing. In order to obtain AOD accurately, the surface-based automatic sun photometer needs to carry out calibration regularly. The normally used Langley method can be effective only when the AOD and the calibration coefficients of the instrument remain unchanged throughout the day. However, when observing the AOD with CE318 sun photometer in field environment, it was found that the AOD of silicon (Si) detector at 1020 nm and indium gallium arsenide (InGaAs) detector at 1639 nm was strongly influenced by temperature due to the large temperature difference at the Dunhuang site. Based on the corresponding relationship between AOD and wavelength, the model of the calibration coefficients varying with temperature was established by nonlinear regression method in field environment. By comparing the AOD before and after temperature correction with the theoretical one, the ratio of data with relative error (RE) less than 5% increased from 0.195 and 0.14 to 0.894 and 0.355, respectively. By this method, calibration can be carried out without the limit of constant AOD. In addition, it is simpler, more convenient, and less costly to perform temperature correction in a field environment than in a laboratory.

scholarly journals New Approaches to Processing Ground-based SAR (GBSAR) Data for Deformation Monitoring

2018 ◽  
Vol 10 (12) ◽  
pp. 1936 ◽  
Author(s):  
Sichun Long ◽  
Aixia Tong ◽  
Ying Yuan ◽  
Zhenhong Li ◽  
Wenhao Wu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Atmospheric Correction ◽  
Deformation Monitoring ◽  
Atmospheric Effects ◽  
Threshold Optimization ◽  
Before And After ◽  
Multiple Threshold ◽  
The Stability ◽  
Average Coherence

In this paper, aiming at the limitation of persistence scatterers (PS) points selection, a new method for selecting PS points has been introduced based on the average coherence coefficient, amplitude dispersion index, estimated signal-to-noise ratio and displacement standard deviation of multiple threshold optimization. The stability and quality of this method are better than that of a single model. In addition, an atmospheric correction model has also been proposed to estimate the atmospheric effects on Ground-based synthetic aperture radar (GBSAR) observations. After comparing the monitoring results before and after correction, we clearly found that the results are in good agreement with the actual observations after applying the proposed atmospheric correction approach.

O2 solubility in blood and temperature correction factors for Po2

1964 ◽  
Vol 19 (5) ◽  
pp. 901-906 ◽  
Author(s):  
J. Hedley-Whyte ◽  
M. B. Laver
Keyword(s):  
Aqueous Solution ◽  
Technical Assistance ◽  
Relative Increase ◽  
Temperature Correction ◽  
Effect Of Temperature ◽  
Correction Factors ◽  
Paired Samples ◽  
Before And After ◽  
Dilute Aqueous Solution ◽  
Relative Solubility

Tonometry of 100 paired samples of blood and measurement of Po2 before and after warming in a sealed syringe showed that the relative solubility of O2 (αB/αHh2O) in fully saturated blood is constant at all temperatures between 24.5 and 38 C, i.e., fully saturated blood appears to behave as a dilute aqueous solution. When Po2 is kept constant during cooling the relative increase in dissolved O2 in blood is the same as with water. Thus, when cooling from 38 to 20 C the increase in dissolved O2, if Po2 is kept constant, is 31%. Correction factors for the effect of temperature on Po2 of blood warmed to 38 C can be based on ratios of αHh2O38/αHh20T. These factors were found to give adequate correction at O2 tensions above 250 mm Hg. Inaccuracies were found when previously proposed factors were applied to blood having a Po2 of 300 mm Hg. These results were interpreted as showing that blood is essentially fully saturated at a Po2 of approximately 250 mm Hg. Note (With the Technical Assistance of A. Murphy and A. Seifen) relative solubility; agr; hypothermia; hemoglobin; dissociation; tonometry; O2 electrode; physiologic shunt Submitted on December 23, 1963

scholarly journals Evaluation of the MODIS Aerosol Retrievals over Ocean and Land during CLAMS

2005 ◽  
Vol 62 (4) ◽  
pp. 974-992 ◽  
Author(s):  
R. C. Levy ◽  
L. A. Remer ◽  
J. V. Martins ◽  
Y. J. Kaufman ◽  
A. Plana-Fattori ◽  
...  
Keyword(s):  
Atmospheric Correction ◽  
Radiant Energy ◽  
Solar Spectrum ◽  
Lookup Table ◽  
Aerosol Size Distribution ◽  
The Sun ◽  
Satellite Platform ◽  
Validation Experiment ◽  
Modis Aod ◽  
Sun Photometer

Abstract The Chesapeake Lighthouse Aircraft Measurements for Satellites (CLAMS) experiment took place from 10 July to 2 August 2001 in a combined ocean–land region that included the Chesapeake Lighthouse [Clouds and the Earth’s Radiant Energy System (CERES) Ocean Validation Experiment (COVE)] and the Wallops Flight Facility (WFF), both along coastal Virginia. This experiment was designed mainly for validating instruments and algorithms aboard the Terra satellite platform, including the Moderate Resolution Imaging Spectroradiometer (MODIS). Over the ocean, MODIS retrieved aerosol optical depths (AODs) at seven wavelengths and an estimate of the aerosol size distribution. Over the land, MODIS retrieved AOD at three wavelengths plus qualitative estimates of the aerosol size. Temporally coincident measurements of aerosol properties were made with a variety of sun photometers from ground sites and airborne sites just above the surface. The set of sun photometers provided unprecedented spectral coverage from visible (VIS) to the solar near-infrared (NIR) and infrared (IR) wavelengths. In this study, AOD and aerosol size retrieved from MODIS is compared with similar measurements from the sun photometers. Over the nearby ocean, the MODIS AOD in the VIS and NIR correlated well with sun-photometer measurements, nearly fitting a one-to-one line on a scatterplot. As one moves from ocean to land, there is a pronounced discontinuity of the MODIS AOD, where MODIS compares poorly to the sun-photometer measurements. Especially in the blue wavelength, MODIS AOD is too high in clean aerosol conditions and too low under larger aerosol loadings. Using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative code to perform atmospheric correction, the authors find inconsistency in the surface albedo assumptions used by the MODIS lookup tables. It is demonstrated how the high bias at low aerosol loadings can be corrected. By using updated urban/industrial aerosol climatology for the MODIS lookup table over land, it is shown that the low bias for larger aerosol loadings can also be corrected. Understanding and improving MODIS retrievals over the East Coast may point to strategies for correction in other locations, thus improving the global quality of MODIS. Improvements in regional aerosol detection could also lead to the use of MODIS for monitoring air pollution.

scholarly journals What Can Multifractal Analysis Tell Us about Hyperspectral Imagery?

2020 ◽  
Vol 12 (24) ◽  
pp. 4077
Author(s):  
Michał Krupiński ◽  
Anna Wawrzaszek ◽  
Wojciech Drzewiecki ◽  
Małgorzata Jenerowicz ◽  
Sebastian Aleksandrowicz
Keyword(s):  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
High Spectral Resolution ◽  
Absorption Bands ◽  
Landscape Type ◽  
Spectral Bands ◽  
Before And After ◽  
Additional Value

Hyperspectral images provide complex information about the Earth’s surface due to their very high spectral resolution (hundreds of spectral bands per pixel). Effective processing of such a large amount of data requires dedicated analysis methods. Therefore, this research applies, for the first time, the degree of multifractality to the global description of all spectral bands of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. Subsets of four hyperspectral images, presenting four landscape types, are analysed. In particular, we verify whether multifractality can be detected in all spectral bands. Furthermore, we analyse variability in multifractality as a function of wavelength, for data before and after atmospheric correction. We try to identify absorption bands and discuss whether multifractal parameters provide additional value or can help in the problem of dimensionality reduction in hyperspectral data or landscape type classification.

scholarly journals Validation of aerosol estimation in atmospheric correction algorithm ATCOR

2015 ◽  
Vol XL-7/W3 ◽  
pp. 677-683 ◽  
Author(s):  
B. Pflug ◽  
M. Main-Knorn ◽  
A. Makarau ◽  
R. Richter
Keyword(s):  
Satellite Images ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Biomass Estimation ◽  
Spatial And Temporal Variability ◽  
Landsat 8 ◽  
Vertical Column ◽  
Sun Photometer ◽  
Better Than

Atmospheric correction of satellite images is necessary for many applications of remote sensing, i.e. computation of vegetation indices and biomass estimation. The first step in atmospheric correction is estimation of the actual aerosol properties. Due to the spatial and temporal variability of aerosol amount and type, this step becomes crucial for an accurate correction of satellite data. Consequently, the validation of aerosol estimation contributes to the validation of atmospheric correction algorithms. In this study we present the validation of aerosol estimation using own sun photometer measurements in Central Europe and measurements of AERONET-stations at different locations in the world. Our ground-based sun photometer measurements of vertical column aerosoloptical thickness (AOT) spectra are performed synchronously to overpasses of the satellites RapidEye, Landsat 5, Landsat 7 and Landsat 8. Selected AERONET data are collocated to Landsat 8 overflights. The validation of the aerosol retrieval is conducted by a direct comparison of ground-measured AOT with satellite derived AOT using the ATCOR tool for the selected satellite images. The mean uncertainty found in our experiments is ΔAOT550nm ≈ 0.03±0.02 for cloudless conditions with cloud+haze fraction below 1%. This AOT uncertainty approximately corresponds to an uncertainty in surface albedo of Δρ ≈ 0.003. Inclusion of cloudy and hazy satellite images into the analysis results in mean ΔAOT550nm ≈ 0.04±0.03 for both RapidEye and Landsat imagery. About ⅓ of samples perform with the AOT uncertainty better than 0.02 and about ⅔ perform with AOT uncertainty better than 0.05.

scholarly journals Atmospheric phase delay correction of PS-InSAR to Monitor Land Subsidence in Surabaya

2021 ◽  
Vol 936 (1) ◽  
pp. 012033
Author(s):  
Toifatul Ulma ◽  
Ira Mutiara Anjasmara ◽  
Noorlaila Hayati
Keyword(s):  
Land Subsidence ◽  
Atmospheric Correction ◽  
Significance Test ◽  
Phase Delay ◽  
Tropospheric Delay ◽  
Interferometric Synthetic Aperture Radar ◽  
Subsidence Area ◽  
Persistent Scatterers ◽  
Before And After ◽  
Delay Correction

Abstract Atmospheric phase delay is one of the most significant errors limiting the accuracy of Interferometric Synthetic Aperture Radar (InSAR) results. In this research, we used the Generic Atmospheric Correction Online Service for InSAR (GACOS) data to correct the tropospheric delay modeling from the persistent scatterers’ InSAR monitoring. Eighty-one (81) Sentinel-1A images and tropospheric delay maps from GACOS monitored land subsidence in Surabaya city between 2017 and 2019. InSAR processing was carried out using the GMTSAR software, continued with StaMPS and TRAIN, which were used to correct the tropospheric delay of PSInSAR-derived deformation measurements. The results before and after the atmospheric phase delay correction using GACOS were confirmed and analyzed in the main subsidence area. The findings of the experiments reveal that the atmospheric phase affects the mean LOS velocity results to some extent. The average difference between PS-InSAR before and after tropospheric correction is 1.734 mm/year with a standard deviation of 0.550 mm/year. The significance test of the two variables, 95%, showed that the tropospheric correction with GACOS data could affect the PS-InSAR results. Furthermore, GACOS correction may increase the error at some points, which could be due to its turbulence data’s low accuracy.

scholarly journals Urban climate and registration of thermal anomalies of territories using satellite imagery

2021 ◽  
Vol 266 ◽  
pp. 08008
Author(s):  
A.D. Biryukov ◽  
V.D. Olenkov ◽  
A.О. Kolmogorova
Keyword(s):  
Satellite Images ◽  
Urban Climate ◽  
Atmospheric Correction ◽  
Optimization Methods ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Before And After ◽  
Landsat 7 ◽  
Automated Processing ◽  
Urban Heat

This paper describes a simplified method of mapping of the ur-ban environment surface to obtain a map of the thermal anomalies distribu-tion and study the structure of the urban heat island. Those maps allow evaluating or planning the urban microclimate optimization methods and studying the effect of land cover type on the site temperature. The article discusses the processing of five satellite images for the summer and winter from 2002 to 2019. We propose a simpler and more automated processing of thermal images for Landsat 7 and Landsat 8. The stages of automatic atmospheric correction according to the DOS1 method and calculation of the emissivity with surface classification are considered. Image processing was carried out in the QGIS software package using the Semiautomatic Classification Plugin extension. As a result, thermal anomalies in Chelya-binsk were localized and a comparison of the thermal map for the specific region before and after urbanization was made.

Atmospheric Correction of Landsat ETM+ Remote Sensing Data Using 6S Code and its Validation

2010 ◽  
Vol 29-32 ◽  
pp. 2365-2368
Author(s):  
Xiao Feng Yang ◽  
Xing Ping Wen
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Atmospheric Correction ◽  
Solar Spectrum ◽  
Quantitative Information ◽  
Visual Interpretation ◽  
Quantitative Remote Sensing ◽  
Before And After ◽  
Ground Object

Atmospheric correction is one of the most important pre-processing steps in quantitative remote sensing. To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. Furthermore, multi-temporal images after atmospheric correction can be compared to each other quantitatively. The Second simulation of satellite signal in the solar spectrum (6S) radiative code can process many types of satellite data and provide several standard atmosphere and aerosol models for atmospheric correction. This paper demonstrates atmospheric correction of Landsat ETM+ data using 6S code. Comparing images before and after atmospheric correction, the different of image before and after correction was not obvious using visual interpretation. Therefore, different ground object spectral curves after atmospheric correction are illustrated. They were similar with the standard ground object spectra. The correlation coefficient of ETM+ band 1 to band 4 and NDVI (Normalized Difference Vegetation Index) after atmospheric correction increases. The atmospheric correction removed the atmosphere effect, so the inherent relevant increased. 6S code was an effective tool for atmospheric correction of remote senisng data.

Sign in / Sign up

Export Citation Format

Share Document