A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance

Roy, David P.; Zhang, Hankui K.; Ju, Junchang; Gómez-Dans, José; Lewis, P.; Schaaf, Crystal B.; Sun, Qingsong; Li, Jian; Huang, Haiyan; Kovalskyy, V.

doi:10.1016/j.rse.2016.01.023

Cited by 274 publications

(194 citation statements)

References 77 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%

“…There are a number of pre-processing issues that need to be addressed before the well calibrated Landsat-8 [10,11] and Sentinel-2A [2,12] data can be used together or treated as effectively being sensed from the same sensor. These include handling the different sensor spectral response functions and correction for atmospheric effects [13,14], correction of surface reflectance anisotropy [15,16], and handling image tiling and geolocation differences [17][18][19]. This paper is concerned with the Landsat-8 and Sentinel-2 spatial resolution differences.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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“…At the same time, it also led to the introduction of a slight uncertainty due to different observation angles. However, when estimating the AOT, a directional correction was applied to account for the different viewing conditions of the adjacent orbits, using the method from [43] and the coefficients from [44].…”

Section: Datasetmentioning

confidence: 99%

“…Before computing the noise criterion, the surface reflectances have been corrected for the directional effects, using the method from [43] and the coefficients from [44].…”

mentioning

confidence: 99%

Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA

et al. 2017

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Abstract:The quantitative use of space-based optical imagery requires atmospheric correction to separate the contributions from the surface and the atmosphere. The MACCS (Multi-sensor Atmospheric Correction and Cloud Screening)-ATCOR (Atmospheric and Topographic Correction) Joint Algorithm, called MAJA, is a numerical tool designed to perform cloud detection and atmospheric correction. For the correction of aerosols effects, MAJA makes an estimate of the aerosol optical thickness (AOT) based on multi-temporal and multi-spectral criteria, but there is insufficient information to infer the aerosol type. The current operational version of MAJA uses an aerosol type which is constant with time, and this assumption impacts the quality of the atmospheric correction. In this study, we assess the potential of using an aerosol type derived from the Copernicus Atmosphere Monitoring Service (CAMS) operational analysis. The performances, with and without the CAMS information, are evaluated. Firstly, in terms of the aerosol optical thickness retrievals, a comparison against sunphotometer measurements over several sites indicates an improvement over arid sites, with a root-mean-square error (RMSE) reduced by 28% (from 0.095 to 0.068), although there is a slight degradation over vegetated sites (RMSE increased by 13%, from 0.054 to 0.061). Secondly, a direct validation of the retrieved surface reflectances at the La Crau station (France) indicates a reduction of the relative bias by 2.5% on average over the spectral bands. Thirdly, based on the assumption that surface reflectances vary slowly with time, a noise criterion was set up, exhibiting no improvement over the spectral bands and the validation sites when using CAMS data, partly explained by a slight increase in the surface reflectances themselves. Finally, the new method presented in this study provides a better way of using the MAJA processor in an operational environment because the aerosol type used for the correction is automatically inferred from CAMS data, and is no longer a parameter to be defined in advance.

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“…BRDF effects have even been found in imagery collected by narrower-swath, medium -resolution sensors such as Landsat and Systeme Probatoire d'Observation de la Terre (SPOT) [39]. After correcting multi-source and/or time-serial data to consistent illumination and viewing geometries, these effects could be minimized, and spectral variations can be better assumed to reflect real changes in surface properties [40]. Furthermore, if directional reflectance can be derived from coarse, medium-and higher-resolution sensors, their increased comparability could result in fusion of multi-temporal and multi-resolution data [23], further facilitating long-term monitoring of land-cover change [41,42].…”

Section: The Value Of Directional Reflectance Correctionmentioning

confidence: 99%

Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval

et al. 2017

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Measurements of solar radiation reflected from Earth's surface are the basis for calculating albedo, vegetation indices, and other terrestrial attributes. However, the "bi-directional" geometry of illumination and viewing (i.e., the Bi-directional Reflectance Distribution Function (BRDF)) impacts reflectance and all variables derived or estimated based on these data. The recently released MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) dataset enables retrieval of directional reflectance at arbitrary solar and viewing angles, potentially increasing precision and comparability of data collected under different illumination and observation geometries. We quantified the ability of MCD43A1 Collection 6 for retrieving directional reflectance and compared the daily Collection 6 retrievals to those of MCD43A1 Collection 5, which are retrieved on an eight-day basis. Correcting MODIS-based estimates of surface reflectance from the illumination and viewing geometry of the Terra satellite (MOD09GA) to that of the MODIS Aqua (MYD09GA) overpass, as well as MCD43A4 Collection 6 and Landsat-5 TM images show that the BRDF correction of MCD43A1 Collection 6 results in greater consistency among datasets, with higher R 2 (0.63-0.955), regression slopes closer to unity (0.718-0.955), lower root mean squared difference (RMSD) (0.422-3.142), and lower mean absolute error (MAE) (0.282-1.735) compared to the Collection 5 data. Smaller levels of noise (observed as high-frequency variability within the time series) in MCD43A1 Collection 6 in comparison to Collection 5 corroborates the improvement of BRDF parameters time series. These results corroborates that the daily MCD43A1 Collection 6 product represents the anisotropy of surface features and results in more precise directional reflectance derivation at any solar and viewing geometry than did the previous Collection 5.

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A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance

Cited by 274 publications

References 77 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

Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA

Assessment of MODIS BRDF/Albedo Model Parameters (MCD43A1 Collection 6) for Directional Reflectance Retrieval

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