A sensor-invariant atmospheric correction method: application to Sentinel-2/MSI and Landsat 8/OLI

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.

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EVALUATING THE IMPACT OF LASRC AND SEN2COR ATMOSPHERIC CORRECTION ALGORITHMS ON LANDSAT-8/OLI AND SENTINEL-2/MSI DATA OVER AERONET STATIONS IN BRAZILIAN TERRITORY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2021-271-2021 ◽

2021 ◽

Vol V-3-2021 ◽

pp. 271-277

Author(s):

R. F. B. Marujo ◽

J. G. Fronza ◽

A. R. Soares ◽

G. R. Queiroz ◽

K. R. Ferreira

Keyword(s):

Atmospheric Correction ◽

Open Data ◽

Image Data ◽

Data Cube ◽

Landsat 8 ◽

Surface Reflectance ◽

Landsat 8 Oli ◽

Continental Scale ◽

The Impact ◽

Sentinel 2

Abstract. Accurate and consistent Surface Reflectance estimation from optical remote sensor observations is directly dependant on the used atmospheric correction processor and the differences caused by it may have implications on further processes, e.g. classification. Brazil is a continental scale country with different biomes. Recently, new initiatives, as the Brazil Data Cube Project, are emerging and using free and open data policy data, more specifically medium spatial resolution sensor images, to create image data cubes and classify the Brazilian territory crops. For this reason, the purpose of this study is to verify, on Landsat-8 and Sentinel-2 images for the Brazilian territory, the suitability of the atmospheric correction processors maintained by their image providers, LaSRC from USGS and Sen2cor from ESA, respectively. To achieve this, we tested the surface reflectance products from Landsat-8 processed through LaSRC and Sentinel-2 processed through LaSRC and Sen2cor comparing to a reference dataset computed by ARCSI and AERONET. The obtained results point that Landsat-8/OLI images atmospherically corrected using the LaSRC corrector are consistent to the surface reflectance reference and other atmospheric correction processors studies, while for Sentinel-2/MSI images, Sen2cor performed best. Although corrections over Sentinel-2/MSI data weren’t as consistent as in Landsat-8/OLI corrections, in comparison to the surface reflectance references, most of the spectral bands achieved acceptable APU results.

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Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors

Remote Sensing ◽

10.3390/rs13183550 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3550

Author(s):

David Moravec ◽

Jan Komárek ◽

Serafín López-Cuervo Medina ◽

Iñigo Molina

Keyword(s):

Satellite Imagery ◽

Vegetation Index ◽

Atmospheric Correction ◽

Correction Method ◽

Landsat 8 ◽

Substantial Impact ◽

Corrected Image ◽

Normalised Difference Vegetation Index ◽

Urban Rural ◽

Sentinel 2

Sentinel-2 and Landsat 8 satellites constitute an unprecedented source of freely accessible satellite imagery. To produce precise outputs from the satellite data, however, proper use of atmospheric correction methods is crucial. In this work, we tested the performance of six different atmospheric correction methods (QUAC, FLAASH, DOS, ACOLITE, 6S, and Sen2Cor), together with atmospheric correction given by providers, non-corrected image, and images acquired using an unmanned aerial vehicle while working with the normalised difference vegetation index (NDVI) as the most widely used index. We tested their performance across urban, rural, and vegetated land cover types. Our results show a substantial impact from the choice of the atmospheric correction method on the resulting NDVI. Moreover, we demonstrate that proper use of atmospheric correction methods can increase the intercomparability between data from Landsat 8 and Sentinel-2 satellite imagery.

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Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters

European Journal of Remote Sensing ◽

10.1080/22797254.2018.1457937 ◽

2018 ◽

Vol 51 (1) ◽

pp. 525-542 ◽

Cited By ~ 43

Author(s):

L. De Keukelaere ◽

S. Sterckx ◽

S. Adriaensen ◽

E. Knaeps ◽

I. Reusen ◽

...

Keyword(s):

Atmospheric Correction ◽

Inland Waters ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Algorithm Validation ◽

Sentinel 2

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Operational Validation of Space-based Albedo Products from Upscaled Tower-based Albedometer Measurements

10.5194/egusphere-egu2020-2731 ◽

2020 ◽

Author(s):

Rui Song ◽

Jan-Peter Muller

Keyword(s):

High Resolution ◽

Surface Albedo ◽

Atmospheric Correction ◽

Ground Level ◽

Correction Method ◽

Landsat 8 ◽

Surface Reflectance ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Surfaces ◽

Sentinel 2

Surface albedo is a fundamental radiative parameter which controls the Earth&#8217;s energy budget by determining the amount of solar radiation which is either absorbed by the surface or reflected back to atmosphere. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their repeated global coverage. In this work, a new method of upscaling surface albedo from ground level footprints of a few tens of metres to coarse satellite scales (&#8776;1km) is reported [1]. Tower-mounted albedometer measurements are upscaled and used to validate global space-based albedo products, including Copernicus Global Land Service (CGLS) 1km albedo data (from Proba-V and previously form VEGETATION-2), MODerate resolution Imaging Spectroradiometer (MODIS) 500m albedo data, and Multi-angle Imaging SpectroRadiometer (MISR) 1.1km albedo data. MODIS albedo retrievals show the closest agreement with tower measurements, followed by the MISR retrievals, and then followed by the CGLS retrievals. The upscaling method uses high-resolution surface reflectance retrievals (from Landsat-8, Sentinel-2) to fill the spatial gaps between the albedometer&#8217;s field-of-view (FoV) and coarse satellite scales. High-resolution surface albedo products are generated by combining high-resolution surface reflectance data and MODIS bi-directional reflectance distribution function (BRDF) climatology data. This upscaling framework also uses a novel Sensor Invariant Atmospheric Correction (SIAC) method [2] to improve the accuracy of upscaled tower albedo values. Total uncertainties of upscaled albedo products are estimated by considering uncertainties from both the tower albedometer raw measurements and SIAC atmospheric corrections. This surface albedo upscaling method can be used over both heterogenous and homogenous land surfaces, and has been examined at the SURFRAD, BSRN and FLUXNET tower sites.Keywords: surface albedo, upscale, CGLS, MODIS, MISR, SIAC[1] Song, R.; Muller, J.-P.; Kharbouche, S.; Woodgate, W. Intercomparison of Surface Albedo Retrievals from MISR, MODIS, CGLS Using Tower and Upscaled Tower Measurements. Remote Sens. 2019, 11, 644, doi:10.3390/rs11060644.[2] Yin, F., Lewis, P. E., Gomez-Dans, J., & Wu, Q. A sensor-invariant atmospheric correction method: application to Sentinel-2/MSI and Landsat 8/OLI. EarthArXiv 2019, https://doi.org/10.31223/osf.io/ps957.

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The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors

Agronomy ◽

10.3390/agronomy9120846 ◽

2019 ◽

Vol 9 (12) ◽

pp. 846

Author(s):

Mbulisi Sibanda ◽

Onisimo Mutanga ◽

Timothy Dube ◽

John Odindi ◽

Paramu L. Mafongoya

Keyword(s):

Food Security ◽

Precision Agriculture ◽

Leaf Spot ◽

Gray Leaf Spot ◽

Economic Losses ◽

Leaf Spot Disease ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Maize Crop ◽

Sentinel 2

Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security.

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Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

Remote Sensing ◽

10.3390/rs13101927 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1927

Author(s):

Fuqin Li ◽

David Jupp ◽

Thomas Schroeder ◽

Stephen Sagar ◽

Joshua Sixsmith ◽

...

Keyword(s):

Coastal Waters ◽

Atmospheric Correction ◽

Correction Method ◽

In Situ Measurements ◽

Landsat 8 ◽

Correction Algorithm ◽

Simple Method ◽

North East ◽

The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

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USABILITY, STRENGTH AND PRACTICALITY OF THE UPCOMING HYSPIRI IN DETECTING MAIZE GRAY LEAFY SPOT IN RELATION TO SENTINEL-2 MSI, VENμS AND LANDSAT 8 OLI SPECTRAL BAND-SETTINGS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-1015-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 1015-1022

Author(s):

M. Sibanda ◽

O. Mutanga ◽

T. Dube ◽

J. Odindi ◽

P. L. Mafongoya

Keyword(s):

Disease Progression ◽

Precision Agriculture ◽

Spectral Band ◽

Maize Yield ◽

Gray Leaf Spot ◽

Added Value ◽

Landsat 8 ◽

Landsat 8 Oli ◽

To Come ◽

Sentinel 2

Abstract. Considering the high maize yield loses that are caused by diseases incidences as well as incomprehensive monitoring initiatives in the crop farming sector of agriculture, there is a need to come up with spatially explicit, cheap, fast and consistent approaches for monitoring as well as forecasting food crop diseases, such as maize gray leaf spot. This study, therefore, we sought to investigate the usability, strength and practicality of the forthcoming HyspIRI in detecting disease progression of Maize Gray leafy spot infections in relation to the Sentinel-2 MSI, Landsat 8 OLI spectral configurations. Maize Gray leafy spot disease progression that were discriminated based on partial least squares –discriminant analysis (PLS-DA) algorithm were (i) healthy, (ii) intermediate and (ii) severely infected maize crops. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93 and 0.89 exhibited by Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor sensors, respectively. Further, the results showed that the visible section the red-edge and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray leafy spot infections. These findings underscore the added value and potential scientific breakthroughs likely to be brought about by the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop disease epidemics to ensure food security.

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Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

PLoS ONE ◽

10.1371/journal.pone.0232962 ◽

2020 ◽

Vol 15 (5) ◽

pp. e0232962 ◽

Cited By ~ 4

Author(s):

Fiona Ngadze ◽

Kudzai Shaun Mpakairi ◽

Blessing Kavhu ◽

Henry Ndaimani ◽

Monalisa Shingirayi Maremba

Keyword(s):

Burned Area ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Burned Area Mapping ◽

Sentinel 2

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Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8020056 ◽

2019 ◽

Vol 8 (2) ◽

pp. 56 ◽

Cited By ~ 9

Author(s):

Maliheh Arekhi ◽

Cigdem Goksel ◽

Fusun Balik Sanli ◽

Gizem Senel

Keyword(s):

Vegetation Index ◽

Data Retrieval ◽

Forest Monitoring ◽

Landsat 8 ◽

Spring Season ◽

Landsat 8 Oli ◽

Atmospheric Effect ◽

Spatial Consistency ◽

The Mean ◽

Sentinel 2

This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.

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