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

Hadjimitsis, Diofantos G.; Clayton, C. R. I.

doi:10.1002/met.80

Cited by 24 publications

(12 citation statements)

References 15 publications

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“…The separation of contributions is not known a priori, so the objective of atmospheric correction is to quantify these two components so that the main analysis can made on the correct target reflectance or radiance values. The darkest pixel (DP) atmospheric correction method, also termed histogram minimum method (Hadjimitsis 1999(Hadjimitsis , 2008Hadjimitsis et al 2000bHadjimitsis et al , 2003Hadjimitsis et al , 2004Hadjimitsis et al , 2006Hadjimitsis and Clayton 2008), was applied to the eight satellite images of the Lower Thames Valley reservoirs. The DP method was found to produce reservoir reflectance values within the range of ground measurements acquired in the reservoirs using a field GER1500 field spectro-radiometer Hadjimitsis et al (2004).…”

Section: Fig 3 Overall Methodologymentioning

confidence: 99%

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Assessment of temporal variations of water quality in inland water bodies using atmospheric corrected satellite remotely sensed image data

Hadjimitsis

Clayton

2008

Environ Monit Assess

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Although there have been many studies conducted on the use of satellite remote sensing for water quality monitoring and assessment in inland water bodies, relatively few studies have considered the problem of atmospheric intervention of the satellite signal. The problem is especially significant when using time series multi-spectral satellite data to monitor water quality surveillance in inland waters such as reservoirs, lakes, and dams because atmospheric effects constitute the majority of the at-satellite reflectance over water. For the assessment of temporal variations of water quality, the use of multi-date satellite images is required so atmospheric corrected image data must be determined. The aim of this study is to provide a simple way of monitoring and assessing temporal variations of water quality in a set of inland water bodies using an earth observation- based approach. The proposed methodology is based on the development of an image-based algorithm which consists of a selection of sampling area on the image (outlet), application of masking and convolution image processing filter, and application of the darkest pixel atmospheric correction. The proposed method has been applied in two different geographical areas, in UK and Cyprus. Mainly, the method has been applied to a series of eight archived Landsat-5 TM images acquired from March 1985 up to November 1985 of the Lower Thames Valley area in the West London (UK) consisting of large water treatment reservoirs. Finally, the method is further tested to the Kourris Dam in Cyprus. It has been found that atmospheric correction is essential in water quality assessment studies using satellite remotely sensed imagery since it improves significantly the water reflectance enabling effective water quality assessment to be made.

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Section: Fig 3 Overall Methodologymentioning

confidence: 99%

“…Optical remote sensing data are affected by the atmosphere. These effects can be removed using suitable atmospheric corrections and bi-directional reflectance models (Zhang et al 2003;Hadjimitsis and Clayton 2008).…”

Section: Introductionmentioning

confidence: 99%

Assessment of temporal variations of water quality in inland water bodies using atmospheric corrected satellite remotely sensed image data

Hadjimitsis

Clayton

2008

Environ Monit Assess

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“…As it was found (Table 2), the relative humidity during the satellite overpass was similar (≈57-67%) for all days except 19/03/2010 (33%) and 15/06/2010 (77%). Relative humidity and temperature as shown by Forster [34] and by Hadjimitsis and Clayton [30] can be used to provide a measure of the equivalent mass of liquid water or water vapour thickness. Although DP algorithm can be applied without any auxiliary meteorological data, as a fully image-based technique, the authors used these data to investigate any possible water vapour absorption effect in atmospheric correction.…”

Section: Meteorological Datamentioning

confidence: 99%

“…Other than water bodies, atmospheric effects, account for the majority of the at-satellite measured radiance in the visible bands [29], and therefore such targets provide an opportunity to assess the effectiveness of the varying atmospheric correction methods [30,31]. This has been shown by Hadjimitsis et al [25], who provided a critical assessment of the effectiveness of most of the available algorithms using Landsat TM satellite imagery and in situ spectroradiometric measurements.…”

Section: Introductionmentioning

confidence: 99%

The Importance of Accounting for Atmospheric Effects in the Application of NDVI and Interpretation of Satellite Imagery Supporting Archaeological Research: The Case Studies of Palaepaphos and Nea Paphos Sites in Cyprus

et al. 2011

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This paper presents the findings of the impact of atmospheric effects when applied on satellite images intended for supporting archaeological research. The study used eleven multispectral Landsat TM/ETM+ images from 2009 until 2010, acquired over archaeological and agricultural areas. The modified Darkest Pixel (DP) atmospheric correction algorithm was applied, as it is considered one of the most simple and effective atmospheric corrections algorithm. The NDVI equation was applied and its values were evaluated before and after the application of atmospheric correction to satellite images, to estimate its possible effects. The results highlighted that atmospheric correction has a significant impact on the NDVI values. This was especially true in seasons where the vegetation has grown. Although the absolute impact on NDVI, after applying the DP, was small (0.06), it was considered important if multi-temporal time series images need to be evaluated and cross-compared. The NDVI differences, before and after atmospheric correction, were assessed using student's t-test and the statistical differences were found to be significant. It was shown that relative NDVI difference can be as much as 50%, if OPEN ACCESSRemote Sens. 2011, 3 2606 atmosphere effects are ignored. Finally, the results had proven that atmospheric corrections can enhance the interpretation of satellite images (especially in cases where optical thickness of water vapour is minimized ≈ 0). This fact can assist in the detection and identification of archaeological crop marks. Therefore, removal of atmospheric effects, for archaeological purposes, was found to be of great importance in improving the image enhancement and NDVI values.

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“…However, the atmosphere transformation processes such as NO X to nitrates (Wang et al 2006) and SO 2 to sulfates (Quan et al 2008) at urban and industrial areas may also cause SPM. The study of particulate matter (PM) is important because of its effects on human health (Adamson et al 1999;Williams et al 2003;WHO 2005), atmospheric visibility (Seinfeld and Pandis 1998), climate change (Haywood and Boucher 2000), satellite imagery (Hadjimitsis et al 2004;Hadjimitsis and Clayton 2008), as well as the nutrient balance and acidity of soil (Yun et al 2002;Odabasi and Bagiroz 2002). The PM with sizes of less than 10 and 2.5 M 10 and PM 2.5 , respectively (Krewski et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Development of the models to estimate particulate matter from thermal infrared band of Landsat Enhanced Thematic Mapper

Amanollahi

Tzanis

Abdullah

et al. 2013

Int. J. Environ. Sci. Technol.

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Particulate matter concentration and assessment of its movement pattern is crucial in air pollution studies. However, no study has been conducted to determine the PM 10 concentration using atmospheric correction of thermal band by temperature of nearest dark pixels group (TNDPG) of this band. For that purpose, 16 Landsat Enhanced Thematic Mapper plus ETM? images for Sanandaj and Tehran in Iran were utilized to determine the amount of PM 10 concentration in the air. Thermal infrared (band 6) of all images was also used to determine the ground station temperature (GST b6) and temperature of nearest dark pixels group. Based on atmospheric correction of images using temperature retrieval from Landsat ETM?, three empirical models were established. Nonlinear correlation coefficient with polynomial equation was used to analyze the correlations between particulate matter concentration and the ground station temperature for the three models. Similar analyses were also undertaken for three stations in Klang Valley, Malaysia, using 11 Landsat ETM? images to show the effectiveness of the model in different region. The data analysis indicated a good correlation coefficient R = 0.89 and R = 0.91 between the trend of the result of temperature of nearest dark pixels group b6 -(GST b6 -GST) model and the trend of PM 10 concentration in Iran and Malaysia, respectively. This study reveals the applicability of the thermal band of Landsat TM and ETM? to determine the PM 10 concentration over large areas.

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

Cited by 24 publications

References 15 publications

Assessment of temporal variations of water quality in inland water bodies using atmospheric corrected satellite remotely sensed image data

Assessment of temporal variations of water quality in inland water bodies using atmospheric corrected satellite remotely sensed image data

The Importance of Accounting for Atmospheric Effects in the Application of NDVI and Interpretation of Satellite Imagery Supporting Archaeological Research: The Case Studies of Palaepaphos and Nea Paphos Sites in Cyprus

Development of the models to estimate particulate matter from thermal infrared band of Landsat Enhanced Thematic Mapper

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