The Importance of Accounting for Atmospheric Effects in Satellite Remote Sensing: A Case Study from the Lower Thames Valley Area, UK

Hadjimitsis, Diofantos G.; Clayton, C. R. I.; Hope, V.S.

doi:10.1061/40479(204)19

Cited by 20 publications

(22 citation statements)

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“…From the literature, it was found that atmospheric effects contribute significantly to the signal received by a multi-spectral scanner. This is more significant over water areas, in which atmospheric effects account for the major proportion of the at-satellite received signal (Hadjimitsis, 1999;Hadjimitsis et al, 1999Hadjimitsis et al, , 2000aHadjimitsis et al, , 2003Hadjimitsis et al, , 2004. It has been reported that these effects range from 38% up to 100% of the received signal in the visible bands for inland and ocean water bodies (Hadjimitsis, 1999;Hadjimitsis et al, 2000aHadjimitsis et al, , 2004.…”

Section: Atmospheric Correctionmentioning

confidence: 94%

Determination of Turbidity in Kourris Dam in Cyprus Utilizing Landsat TM Remotely Sensed Data

Hadjimitsis

Clayton

et al. 2006

Water Resour Manage

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Determination of turbidity is a common component of water-quality assessments. In regions where there are a lot of inland waters such as dams, sampling even a small proportion of those dams for monitoring and assessing water quality is cost prohibitive. Satellite remote sensing has the potential to be a powerful tool for assessing water quality over large spatial scales. The overall objective of this study was to examine whether Landsat-5 TM (Thematic Mapper) and Landsat-7 ETM+ (Enhanced Thematic Mapper) could be used to measure turbidity across the Kourris Dam, which is the biggest dam in Cyprus. This paper presents the results obtained by applying the linear regression analysis in order to examine the relationship between the turbidity measurements measured in-situ during the satellite overpass against at-satellite atmospheric corrected reflectance values. It has been found that the reflectance, after atmospheric correction, at Landsat TM Bands 1 and 3 is strongly related with turbidity levels after linear regression analysis. The most significant correlation was occurred when reflectance in TM band 3 and logarithmic reflectance in TM band 3 were correlated with turbidity measurements. Indeed, the correlation coefficient (R) when atmospheric corrected reflectance (ρ) in the Landsat TM band 3 were correlated against turbidity, before atmospheric correction was R = 0.38 and after atmospheric correction was R = 1; and when atmospheric corrected logarithmic reflectance (Log ρ) in the Landsat TM band 3 were correlated against turbidity, before atmospheric correction was R = 0.46 and after atmospheric correction was R = 1.

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Section: Atmospheric Correctionmentioning

confidence: 94%

Determination of Turbidity in Kourris Dam in Cyprus Utilizing Landsat TM Remotely Sensed Data

Hadjimitsis

Clayton

et al. 2006

Water Resour Manage

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“…The effect of a linear stretch is to increase contrast, whilst brightening the image. In order to retrieve visual information regarding the temporal variations in the reservoirs and to find evidence for the need to apply atmospheric correction, the automatic and uniform stretching was carried out after applying the masking window algorithm (Hadjimitsis et al 2000b) (see Fig. 4a).…”

Section: Fig 3 Overall Methodologymentioning

confidence: 99%

“…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%

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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“…Calibration for high altitude images is needed because there are distortions in the atmosphere such as dust, small particles, smoke, water vapor and etc (Hadjimitsis et al, 2000). These distortions can cause a change in the intensity of each band.…”

Section: Calibrationmentioning

confidence: 99%

<title>Use of remote sensing to determine plant health and productivity</title>

et al. 2002

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This project seeks to assess plant productivity and health in time and space by measuring spectral reflectance from soybean canopies using remote sensing images that do not require ground assessment. Aerial images and reflectance measurements from a multi-spectral radiometer were obtained simultaneously from a soybean field located in Story County, Iowa. The multi-spectral radiometer has eight wavelength bands, ranging from 460-nm to 810-nm and was used as a ground reference for the data analysis. Aerial images were obtained from altitudes ranging from 152 to 427 meters from the ground during summer 2000.Aerial images were analyzed using Matlab  , ArcView  and Imagine  . Difficulties in image analysis and interpretation may occur as the sensing equipment increases in altitude because atmospheric influences become more pronounced. Scattering and absorption of electromagnetic waves in the atmosphere change the spectrum of the reflected wave emitting from the plants as it propagates from the plants to the sensors. Color calibration procedures were used with red, green and blue ground cloths to correct aerial images in the respective red, blue and green bands. Regression analysis was carried out to quantify the relationships between multi-spectral radiometer data and aerial image data.

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The Importance of Accounting for Atmospheric Effects in Satellite Remote Sensing: A Case Study from the Lower Thames Valley Area, UK

Cited by 20 publications

References 0 publications

Determination of Turbidity in Kourris Dam in Cyprus Utilizing Landsat TM Remotely Sensed Data

Determination of Turbidity in Kourris Dam in Cyprus Utilizing Landsat TM Remotely Sensed Data

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

<title>Use of remote sensing to determine plant health and productivity</title>

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