Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods

Liang, Shunlin; Fang, Hongliang; Chen, M.

doi:10.1109/36.964986

Cited by 323 publications

(185 citation statements)

References 36 publications

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“…In this study we did not consider sensor differences due to different viewing geometry conditions combined with surface reflectance anisotropy [15,16], or sensor spectral band pass differences [60,61], or differences in the interaction of radiation with the atmosphere across high contrast edges ("adjacency effects") that are dependent on the atmospheric contents and the sensor point spread function [62,63]. These sensor differences are scene dependent and are likely to cause systematic distortions between the Sentinel-2A and Landsat-8 data.…”

Section: Discussionmentioning

confidence: 99%

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Zhang

Roy

et al. 2017

Remote Sensing

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Abstract:The Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) method to downscale Landsat-8 Operational Land Imager (OLI) 30-m data to Sentinel-2 multi-spectral instrument (MSI) 20-m resolution is presented. The method first downscales the Landsat-8 30-m OLI bands to 15-m using the spatial detail provided by the Landsat-8 15-m panchromatic band and then reprojects and resamples the downscaled 15-m data into registration with Sentinel-2A 20-m data. The LPAD method is demonstrated using pairs of contemporaneous Landsat-8 OLI and Sentinel-2A MSI images sensed less than 19 min apart over diverse geographic environments. The LPAD method is shown to introduce less spectral and spatial distortion and to provide visually more coherent data than conventional bilinear and cubic convolution resampled 20-m Landsat OLI data. In addition, results for a pair of Landsat-8 and Sentinel-2A images sensed one day apart suggest that image fusion should be undertaken with caution when the images are acquired under different atmospheric conditions. The LPAD source code is available at GitHub for public use.

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Section: Discussionmentioning

confidence: 99%

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Zhang

Roy

et al. 2017

Remote Sensing

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“…The calculation of the FVC is based on the NDVI values, which may be calculated using the spectral reflectance data. The atmospheric correction of the Landsat TM/ETM data was conducted with a combination of the look up table (LUT) and dark-object method (DOM) Sendra 1988, Kaufman et al 1997;Kaufman, Karnieli, and Tanre 2000;Liang et al 1997Liang et al , 2002Liang, Fang, and Chen 2001). The algorithm developed by Gutman and Ignatov (1998) was applied to compute the FVC as expressed below:…”

Section: Calculations For the Ndvi And Fvcmentioning

confidence: 99%

The responses of vegetation water content (EWT) and assessment of drought monitoring along a coastal region using remote sensing

Gao

Wang

Cao

et al. 2014

GIScience & Remote Sensing

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This article retrieved the vegetation water content equivalent water thickness (EWT) information and the relevant parameters for the land surface from full-band TM remote sensing data. The effects of surface water heat flux and surface covering on the EWT were analyzed via studies of the regional land cover status and the combined EWT with land surface parameters. This article also analyzed the roles and limitations of EWT in drought monitoring combined with classification of the regional drought and regional water stress index (RWSI). From the results, the following conclusions were reported. (1) The spatial distribution of the EWT is closely related to the vegetation, and the EWT is able to monitor the regional water conditions to a certain extent. (2) The distribution of the EWT is affected significantly by the density of vegetation cover, land surface temperature and evapo-transpiration. (3) The correlation between the NDVI (or fractional vegetation cover) and the EWT differs under different vegetation coverage conditions. (4) The evapo-transpiration of the ecological environment is closely tied to the EWT such that the changes in evapo-transpiration affect the EWT significantly. (5) The ability of the EWT to monitor regional drought is conditional, and therefore no significant indication exists that can be used to monitor moderate to severe drought conditions.

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“…In order to remove or alleviate the impact of haze on images, several haze removing approaches have been developed during the past 20 years (Chavez, 1988;Liang et al, 2001;Liang et al, 2002;Zhang et al, 2002;He et al, 2010;Liu et al, 2011). Generally, the image-based haze removal technique can be grouped into two algorithms：1) spatial filtering (i.e.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Haze Removal Algorithms and Their Impacts on Classification Accuracy for Landsat Imagery

et al. 2017

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Abstract:The quality of Landsat images in humid areas is considerably degraded by haze in terms of their spectral response pattern, which limits the possibility of their application in using visible and near-infrared bands. A variety of haze removal algorithms have been proposed to correct these unsatisfactory illumination effects caused by the haze contamination. The purpose of this study was to illustrate the difference of two major algorithms (the improved homomorphic filtering (HF) and the virtual cloud point (VCP)) for their effectiveness in solving spatially varying haze contamination, and to evaluate the impacts of haze removal on land cover classification. A case study with exploiting large quantities of Landsat TM images and climates (clear and haze) in the most humid areas in China proved that these haze removal algorithms both perform well in processing Landsat images contaminated by haze. The outcome of the application of VCP appears to be more similar to the reference images compared to HF. Moreover, the Landsat image with VCP haze removal can improve the classification accuracy effectively in comparison to that without haze removal, especially in the cloudy contaminated area. Keywords: haze contamination; HF; VCP; classification accuracy Resumo:A qualidade de imagens Landsat em áreas úmidas é consideravelmente degradada pela neblina em termos do seu padrão de resposta espectral, o que limita a possibilidade da sua aplicação usando as bandas do visível e infravermelho próximo. Uma variedade de algoritmos de remoção de neblina têm sido propostos para corrigir estes efeitos indesejados da iluminação, causados pela contaminação por neblina. O propósito deste estudo foi demonstrar a diferença entre os dois principais algoritmos (a filtragem homomórfica melhorada (HF) e a nuvem virtual de pontos (VCP)), considerando sua efetividade na solução da contaminação da neblina com variação espacial, e para avaliar os impactos da remoção da neblina na classificação da cobertura do solo. Um estudo de caso com utilização de grande quantidade de imagens Landsat TM e climáticas (claras e com neblina) em áreas muito úmidas da China comprovou que estes algoritmos de remoção têm bom desempenho no processamento das imagens contaminadas por neblina. O resultado da aplicação do filtro VCP parece ser mais similar às imagens de referência quando comparado ao filtro HF. Além disso, a imagem Landsat com a remoção de neblina por VCP pode efetivamente melhorar a acurácia da classificação em comparação àquelas sem a remoção da neblina, especialmente em áreas contaminadas por nuvens. Palavras-chave:Contaminação por neblina; HF; VCP; Acurácia da classificação.

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Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods

Cited by 323 publications

References 36 publications

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

The responses of vegetation water content (EWT) and assessment of drought monitoring along a coastal region using remote sensing

A Comparison of Haze Removal Algorithms and Their Impacts on Classification Accuracy for Landsat Imagery

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