Conterminous United States demonstration and characterization of MODIS-based Landsat ETM+ atmospheric correction

Roy, David P.; Qin, Yixuan; Kovalskyy, V.; Vermote, Éric; Ju, J.; Egorov, Alexey; Hansen, Matthew C.; Kommareddy, Indrani; Yan, Lin

doi:10.1016/j.rse.2013.09.012

Cited by 78 publications

(43 citation statements)

References 53 publications

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“…The impact of the atmospheric correction is most apparent comparing the true color TOA reflectance (a) and surface reflectance (b). The atmospheric correction increases the true color image spatial contrast and reduces the blue appearance, which is expected from comparable Landsat atmospheric correction experiments [64,65]. The distinct smoke plume is not well corrected, which as noted earlier is a problem for most atmospheric correction methods.…”

Section: Resultsmentioning

confidence: 84%

“…The distinct smoke plume is not well corrected, which as noted earlier is a problem for most atmospheric correction methods. The impact of the atmospheric correction is visually less apparent in the longer wavelength false color bands, i.e., comparing (c) and (d), which is expected as atmospheric effects are smaller at longer wavelengths [65,66]. The false color longer wavelength bands are more sensitive to the effects of fire on vegetation and consequently the extensive burned area in the east side of the image has more contrast with the surrounding unburned vegetation than is apparent in the true color images.…”

Section: Resultsmentioning

confidence: 88%

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Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination

et al. 2016

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Biomass burning is a global phenomenon and systematic burned area mapping is of increasing importance for science and applications. With high spatial resolution and novelty in band design, the recently launched Sentinel-2A satellite provides a new opportunity for moderate spatial resolution burned area mapping. This study examines the performance of the Sentinel-2A Multi Spectral Instrument (MSI) bands and derived spectral indices to differentiate between unburned and burned areas. For this purpose, five pairs of pre-fire and post-fire top of atmosphere (TOA reflectance) and atmospherically corrected (surface reflectance) images were studied. The pixel values of locations that were unburned in the first image and burned in the second image, as well as the values of locations that were unburned in both images which served as a control, were compared and the discrimination of individual bands and spectral indices were evaluated using parametric (transformed divergence) and non-parametric (decision tree) approaches. Based on the results, the most suitable MSI bands to detect burned areas are the 20 m near-infrared, short wave infrared and red-edge bands, while the performance of the spectral indices varied with location. The atmospheric correction only significantly influenced the separability of the visible wavelength bands. The results provide insights that are useful for developing Sentinel-2 burned area mapping algorithms.

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Section: Resultsmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 88%

Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination

et al. 2016

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“…However, the same pattern of the Q2 n values considering just the red, green, and blue bands (Table 3) and considering all six spectral bands (Table 4) was found. Perhaps this reflects the reduced atmospheric scattering that occurs at longer wavelengths [57,58]. The results of this study indicate that the LPAD approach can be applied to downscale all of the Landsat-8 30-m OLI bands.…”

Section: Discussionmentioning

confidence: 81%

“…Specifically, atmospheric and perhaps surface differences (due to biomass burning) between the Landsat-8 and Sentinel-2A images meant that differences between the downscaled Landsat-8 20 m and Sentinel-2A 20 m were not due only to the downscaling. The Sentinel-2A image was acquired a day after the Landsat-8 image and contained spatially extensive aerosols that are known to scatter and absorb radiation and that effectively reduce the image contrast [57,58]. Atmospheric correction methodologies that use radiative transfer algorithms and atmospheric characterization data are most suitable for large area and/or repeat atmospheric correction [25].…”

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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“…The various methods of identifying and removal of such artefacts are referred to as atmospheric correction, and involve extensive use of external reference data sources, such as sun photometers (Vermote et al 1995;Fedosejevs et al 2000), operational atmospheric radiance models (Trishchenko et al 2002) and weather simulation models (Vermote et al 2002). Thus far operational atmospheric correction methods which use data provided by the sensor itself have only been presented for the MODIS sensor, which offers high short-wave infrared resolution (Wang, Shi 2007;Okin, Gu 2015;Roy et al 2014). This being said, the process of removing atmospheric artefacts from an image is a complex one, and even the availability of reliable reference data does not guarantee successful correction of the image.…”

Section: Introductionmentioning

confidence: 99%

Operational algae bloom detection in the Baltic Sea using GIS and AVHRR data

Kulawiak¹

2016

Baltica

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During the blooming season, algal colonies can, in extreme cases, cover up to 200 000 square kilometres of the Baltic Sea water surface. Because the position and shape of the blooms may significantly change in a very short time due to the influence of wind and waves, regular monitoring of the blooms' development is necessary. Currently, the desired monitoring frequency may only be achieved by means of remote sensing. The article presents a novel method of AVHRR data processing for the purpose of detection of algal blooms in the Baltic Sea. Instead of analysing the value of spectral reflectance of the algae, the algorithm analyses the frequency distribution of normalized difference in reflectance between the visible and near-infrared spectral bands. The proposed method has been implemented and tested as part of an operational Geographic Information System.

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Conterminous United States demonstration and characterization of MODIS-based Landsat ETM+ atmospheric correction

Cited by 78 publications

References 53 publications

Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination

Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination

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

Operational algae bloom detection in the Baltic Sea using GIS and AVHRR data

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