scholarly journals The use of an improved atmospheric correction algorithm for removing atmospheric effects from remotely sensed images using an atmosphere-surface simulation and meteorological data

2008 ◽  
Vol 15 (3) ◽  
pp. 381-387 ◽  
Author(s):  
D. G. Hadjimitsis ◽  
C. R. I. Clayton
Keyword(s):  
Meteorological Data ◽  
Atmospheric Correction ◽  
Remotely Sensed ◽  
Atmospheric Effects ◽  
Correction Algorithm ◽  
Remotely Sensed Images

scholarly journals CAAS: an atmospheric correction algorithm for the remote sensing of complex waters

2012 ◽  
Vol 30 (1) ◽  
pp. 203-220 ◽  
Author(s):  
P. Shanmugam
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Near Infrared ◽  
Atmospheric Correction ◽  
Atmospheric Effects ◽  
Correction Algorithm ◽  
Ocean Colour ◽  
In Situ Data ◽  
Correction Scheme ◽  
The Impact

Abstract. The current SeaDAS atmospheric correction algorithm relies on the computation of optical properties of aerosols based on radiative transfer combined with a near-infrared (NIR) correction scheme (originally with assumptions of zero water-leaving radiance for the NIR bands) and several ancillary parameters to remove atmospheric effects in remote sensing of ocean colour. The failure of this algorithm over complex waters has been reported by many recent investigations, and can be attributed to the inadequate NIR correction and constraints for deriving aerosol optical properties whose characteristics are the most difficult to evaluate because they vary rapidly with time and space. The possibility that the aerosol and sun glint contributions can be derived in the whole spectrum of ocean colour solely from a knowledge of the total and Rayleigh-corrected radiances is developed in detail within the framework of a Complex water Atmospheric correction Algorithm Scheme (CAAS) that makes no use of ancillary parameters. The performance of the CAAS algorithm is demonstrated for MODIS/Aqua imageries of optically complex waters and yields physically realistic water-leaving radiance spectra that are not possible with the SeaDAS algorithm. A preliminary comparison with in-situ data for several regional waters (moderately complex to clear waters) shows encouraging results, with absolute errors of the CAAS algorithm closer to those of the SeaDAS algorithm. The impact of the atmospheric correction was also examined on chlorophyll retrievals with a Case 2 water bio-optical algorithm, and it was found that the CAAS algorithm outperformed the SeaDAS algorithm in terms of producing accurate pigment estimates and recovering areas previously flagged out by the later algorithm. These findings suggest that the CAAS algorithm can be used for applications focussing in quantitative assessments of the biological and biogeochemical properties in complex waters, and can easily be extended to other sensors such as OCM-2, MERIS and GOCI.

scholarly journals Atmospheric Error Correction of the Laser Beam Ranging

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
J. Saydi ◽  
A. Lotfalian ◽  
M. Abedi ◽  
J. Khalilzadeh ◽  
H. Saghafifar
Keyword(s):  
Laser Beam ◽  
Meteorological Data ◽  
Atmospheric Correction ◽  
Weather Conditions ◽  
Emission Angle ◽  
Laser Ranging ◽  
Atmospheric Effects ◽  
Atmospheric Models ◽  
Ranging Error ◽  
Surface Measurements

Atmospheric models based on surface measurements of pressure, temperature, and relative humidity have been used to increase the laser ranging accuracy by ray tracing. Atmospheric refraction can cause significant errors in laser ranging systems. Through the present research, the atmospheric effects on the laser beam were investigated by using the principles of laser ranging. Atmospheric correction was calculated for 0.532, 1.3, and 10.6 micron wavelengths through the weather conditions of Tehran, Isfahan, and Bushehr in Iran since March 2012 to March 2013. Through the present research the atmospheric correction was computed for meteorological data in base of monthly mean. Of course, the meteorological data were received from meteorological stations in Tehran, Isfahan, and Bushehr. Atmospheric correction was calculated for 11, 100, and 200 kilometers laser beam propagations under 30°, 60°, and 90° rising angles for each propagation. The results of the study showed that in the same months and beam emission angles, the atmospheric correction was most accurate for 10.6 micron wavelength. The laser ranging error was decreased by increasing the laser emission angle. The atmospheric correction with two Marini-Murray and Mendes-Pavlis models for 0.532 nm was compared.

A Method for Deriving All-Sky Evapotranspiration From the Synergistic Use of Remotely Sensed Images and Meteorological Data

2017 ◽  
Vol 122 (24) ◽  
pp. 13,263-13,277 ◽  
Author(s):  
Pei Leng ◽  
Zhao-Liang Li ◽  
Si-Bo Duan ◽  
Ronglin Tang ◽  
Mao-Fang Gao
Keyword(s):  
Meteorological Data ◽  
Remotely Sensed ◽  
Remotely Sensed Images

scholarly journals Atmospheric correction for satellite remotely sensed data intended for agricultural applications: impact on vegetation indices

2010 ◽  
Vol 10 (1) ◽  
pp. 89-95 ◽  
Author(s):  
D. G. Hadjimitsis ◽  
G. Papadavid ◽  
A. Agapiou ◽  
K. Themistocleous ◽  
M. G. Hadjimitsis ◽  
...  
Keyword(s):  
Satellite Images ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Remotely Sensed ◽  
Correction Method ◽  
Atmospheric Effects ◽  
Surface Reflectance ◽  
Remotely Sensed Data ◽  
Agricultural Applications ◽  
Multi Temporal

Abstract. Solar radiation reflected by the Earth's surface to satellite sensors is modified by its interaction with the atmosphere. The objective of applying an atmospheric correction is to determine true surface reflectance values and to retrieve physical parameters of the Earth's surface, including surface reflectance, by removing atmospheric effects from satellite images. Atmospheric correction is arguably the most important part of the pre-processing of satellite remotely sensed data. Such a correction is especially important in cases where multi-temporal images are to be compared and analyzed. For agricultural applications, in which several vegetation indices are applied for monitoring purposes, multi-temporal images are used. The integration of vegetation indices from remotely sensed images with other hydro-meteorological data is widely used for monitoring natural hazards such as droughts. Indeed, the most important task is to retrieve the true values of the vegetation status from the satellite-remotely sensed data. Any omission of considering the effects of the atmosphere when vegetation indices from satellite images are used, may lead to major discrepancies in the final outcomes. This paper highlights the importance of considering atmospheric effects when vegetation indices, such as DVI, NDVI, SAVI, MSAVI and SARVI, are used (or considered) and presents the results obtained by applying the darkest-pixel atmospheric correction method on ten Landsat TM/ETM+ images of Cyprus acquired from July to December 2008. Finally, in this analysis, an attempt is made to determine evapotranspiration and to examine its dependence on the consideration of atmospheric effects when multi-temporal image data are used. It was found that, without applying any atmospheric correction, the real daily evapotranspiration was less than the one found after applying the darkest pixel atmospheric correction method.

scholarly journals iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2

2021 ◽  
Vol 13 (4) ◽  
pp. 654
Author(s):  
Erwin Wolters ◽  
Carolien Toté ◽  
Sindy Sterckx ◽  
Stefan Adriaensen ◽  
Claire Henocq ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Negative Bias ◽  
Surface Reflectance ◽  
Temporal Consistency ◽  
Correction Algorithm ◽  
Land Surfaces ◽  
Relative Differences ◽  
Level 2

To validate the iCOR atmospheric correction algorithm applied to the Sentinel-3 Ocean and Land Color Instrument (OLCI), Top-of-Atmosphere (TOA) observations over land, globally retrieved Aerosol Optical Thickness (AOT), Top-of-Canopy (TOC) reflectance, and Vegetation Indices (VIs) were intercompared with (i) AERONET AOT and AERONET-based TOC reflectance simulations, (ii) RadCalNet surface reflectance observations, and (iii) SYN Level 2 (L2) AOT, TOC reflectance, and VIs. The results reveal that, overall, iCOR’s statistical and temporal consistency is high. iCOR AOT retrievals overestimate relative to AERONET, but less than SYN L2. iCOR and SYN L2 TOC reflectances exhibit a negative bias of ~−0.01 and −0.02, respectively, in the Blue bands compared to the simulations. This diminishes for RED and NIR, except for a +0.02 bias for SYN L2 in the NIR. The intercomparison with RadCalNet shows relative differences < ±6%, except for bands Oa02 (Blue) and Oa21 (NIR), which is likely related to the reported OLCI “excess of brightness”. The intercomparison between iCOR and SYN L2 showed R2 = 0.80–0.93 and R2 = 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR’s higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations.

Sign in / Sign up

Export Citation Format

Share Document