Role of adjacency effect in the remote sensing of aerosol

Lyapustin, A.; Kaufman, Yoram J.

doi:10.1029/2000jd900647

Cited by 50 publications

(52 citation statements)

References 9 publications

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“…The reflectance of DT, ρ c , is considered to range from 0.01 to 0.06, since there will exist various degrees of darkness of DT used in remote sensing AOD. The definitions of DT and these two contrasts over DT are similar to those of Lyapustin and Kaufman (2001). In the following discussion, ASE_AOD and RSE_AOD represent the ASE and RSE of the retrieved τ a .…”

Section: Resultsmentioning

confidence: 90%

“…When θ s increases to 60°, the RSE_AOD due to AE is decreased. This is because the increasing atmospheric reflectance over DT dominates the ρ TOA (Lyapustin and Kaufman 2001). When θ s and φ are 60°a nd 180°, ASE_AOD are almost negligible (less than 0.1) except for VIS = 5 km and MC in both bands.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, it becomes very important for remote sensing applications when dark or bright targets are used. Significant errors of retrieved surface reflectance at the red wavelength due to AE can be up to 0.07 at the viewing zenith angle of 53.1° in a hazy sky even at 1 km resolution (Lyapustin and Kaufman 2001). Therefore, it is suggested that AE be taken into account not only in the atmospheric correction algorithms but also in aerosol retrieval.…”

Section: Introductionmentioning

confidence: 99%

“…The adjacency effect (AE) has a systematic nature, which brightens the dark target and darkens the bright target at the TOA level (Lyapustin 2001;Lyapustin and Kaufman 2001). It originates from the reflected radiance of the surrounding areas, out of the field of view, and is scattered by the atmosphere into the field of view (Kaufman 1989), thus causing a blurring effect and reducing the contrast of an image.…”

Section: Introductionmentioning

confidence: 99%

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Error Analysis of Retrieved Aerosol Optical Depth due to Adjacency Effect for ROCSAT-2 RSI Bands

Liu¹

2005

Terr. Atmos. Ocean. Sci.

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The adjacency effect (AE), caused by the scattering of reflected radiance from an inhomogeneous surface, is known to introduce a blurring effect on remotely sensed data, especially for high spatial resolution data. The retrieval of aerosol optical depth (AOD) by the dark target (DT) method usually neglects the AE. Hence, significant errors may be induced in aerosol retrieval. In this study, the errors of retrieved AOD due to AE by the DT method for very high spatial resolution data of 8 m in the multi-spectral bands of ROCSAT-2 RSI are investigated. A fast, accurate simple atmospheric correction model is applied. Target size is considered the same as the spatial resolution. The errors are mainly studied for both low and medium contrasts, in both clear and hazy skies for both continental and urban aerosol models. The results indicate that for the continental model, AE may cause significant retrieval errors for AOD over DTs for medium contrast in both skies in blue and red bands. Error becomes negligible if the inhomogeneous surface is for low contrast in clear sky. It is larger for the urban model where there is more absorption than for the continental model, due to the less sensitivity of top-of-atmosphere reflectance to AOD for DTs in inhomogeneous surfaces. This suggests that AE be considered for medium contrast in both skies, especially for urban-industrial regions for ROCSAT-2 RSI.

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Section: Resultsmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Error Analysis of Retrieved Aerosol Optical Depth due to Adjacency Effect for ROCSAT-2 RSI Bands

Liu¹

2005

Terr. Atmos. Ocean. Sci.

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“…With respect to the retrieval of the aerosol optical depth (AOD), surface albedo heterogeneity can lead to an overestimation of AOD due to the so-called adjacency effect (Lyapustin and Kaufman, 2001). This effect influences satellite or airborne radiance measurements.…”

Section: E Jäkel Et Al: Airborne Retrieval Of Heterogeneous Surfacementioning

confidence: 99%

Influence of spatial heterogeneity of local surface albedo on the area-averaged surface albedo retrieved from airborne irradiance measurements

2013

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Spectral airborne upward and downward irradiance measurements are used to derive the area-averaged surface albedo. Real surfaces are not homogeneous in their reflectivity. Therefore, this work studies the effects of the heterogeneity of surface reflectivity on the area-averaged surface albedo to quantify how well aircraft measurements can resolve the small-scale variability of the local surface albedo. For that purpose spatially heterogeneous surface albedo maps were input into a 3-dimensional (3-D) Monte Carlo radiative transfer model to simulate 3-D irradiance fields. The calculated up- and downward irradiances in altitudes between 0.1 and 5 km are used to derive the area-averaged surface albedo using an iterative retrieval method that removes the effects due to atmospheric scattering and absorption within the layer beneath the considered level. For the case of adjacent land and sea surfaces, parametrizations are presented which quantify the horizontal distance from the coastline that is required to reduce surface heterogeneity effects on the area-averaged surface albedo to a given limit. The parametrization which is a function of altitude, aerosol optical depth, single scattering albedo, and the ratio of local land and sea albedo was applied for airborne spectral measurements. In addition, the deviation between area-averaged and local surface albedo is determined for more complex surface albedo maps. For moderate aerosol conditions (optical depth less than 0.4) and a wavelength range between 400 and 1000 nm, the altitude and the heterogeneity of the surface albedo are the dominant factors determining the mean deviation between local and area-averaged surface albedo. A parametrization of the mean deviation is applied to an albedo map that was derived from a Landsat image of an area in East Anglia (UK). Parametrization and direct comparison of local and area-averaged surface albedo show similar mean deviations (20% vs. 25%) over land

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What is the minimum river width for the estimation of water clarity using medium-resolution remote sensing images?

Zhao

Zou

et al. 2014

Water Resour. Res.

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Numerous studies have demonstrated the feasibility of remotely sensing water clarity of lakes, reservoirs, and larger rivers using medium-resolution spatial images. However, addressing relatively small rivers or river sections is very challenging due to the adjacency effect from the riverbanks. The objectives of this study are to quantify the minimum river width for water quality remote sensing and to validate the feasibility of using medium-resolution spatial images for estimating the Secchi disk depth (SDD). A methodology was developed to quantify the minimum river width for water quality remote sensing using highresolution spatial images from WorldView-2 and Pl eiades. Our results suggest that the influential distance of the adjacency effect from the riverbank is 17.3 m, i.e., water pixels with a distance of more than 17.3 m from the shoreline experienced a minimal disturbance from the riverbank. For the 30 m spatial resolution HJ-1A image (one of Chinese civilian satellites launched in 2008), the minimum river width is 64.6-98.5 m (the variation was determined according to the river flow direction and the pixel position relative to the shoreline). Using the sections that satisfied the minimum river width requirement, a significant estimation model was established between the spectral reflectance and the SDD (R 2 5 0.94), demonstrating that the minimum river width recommended in this study is practical. This work is the first study to quantify the minimum river width for water quality remote sensing and thus provides a valuable reference for remote sensing of relatively small rivers.

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Role of adjacency effect in the remote sensing of aerosol

Cited by 50 publications

References 9 publications

Error Analysis of Retrieved Aerosol Optical Depth due to Adjacency Effect for ROCSAT-2 RSI Bands

Error Analysis of Retrieved Aerosol Optical Depth due to Adjacency Effect for ROCSAT-2 RSI Bands

Influence of spatial heterogeneity of local surface albedo on the area-averaged surface albedo retrieved from airborne irradiance measurements

What is the minimum river width for the estimation of water clarity using medium-resolution remote sensing images?

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