Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data

Roy, David P.; Li, Jian; Zhang, Hankui K.; Yan, Lin

doi:10.1080/2150704x.2016.1212419

Cited by 65 publications

(36 citation statements)

References 13 publications

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“…In particular, L1C tiles from the same MSI swath overlap spatially and a significant proportion may be defined in different UTM zones, i.e., in separate map projections, making reprojection and resampling of the data quite complicated [40,41]. However, in this study, only pairs of Sentinel-2A images sensed over the same L1C tile location were used, so no reprojection was necessary.…”

Section: Sentinel-2 Image Pre-processingmentioning

confidence: 99%

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: Sentinel-2 Image Pre-processingmentioning

confidence: 99%

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

et al. 2016

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Section: Data Acquisition and Processingmentioning

confidence: 99%

“…The Sentinel-2 MSI image was downloaded from the Sentinel Scientific Data Hub web site as a Level 1C (L1C) product. The Sentinel-2 L1C products were provided in Standard Archive Format for Europe (SAFE) file format [49], and the products were Top Of Atmosphere (TOA) reflectance data [50,51], with 12-bit dynamic range, and each image was in the Universal Transverse Mercator (UTM) map projection. We used the Sen2Cor algorithm on the SentiNel Application Platform (SNAP) developed by European Space Agency (ESA) to perform atmospheric correction to convert the images into product 2A which is the Bottom Of Atmosphere (BOA) reflectance data [48,52].…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Zhou

Dong

Xiao

et al. 2017

Water

179

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Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index-and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

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“…The stored Landsat-8 digital numbers were converted to top of atmosphere (TOA) spectral reflectance (ρ λ , unitless) using metadata conversion coefficients and then dividing by the sine of the scene center solar elevation angle (θ s ). Sentinel-2A L1C data that are available in 109 km × 109 km tiles with the UTM projection and US Military Grid Reference System (MGRS) [17,28] were used. The Sentinel-2A L1C data have geo-location accuracy specifications of less than 12.5 m and band-to-band registration accuracies of less than 3 m, 6 m, and 18 m for the 10-m, 20-m, and 60-m bands, respectively [29].…”

Section: Data and Study Areasmentioning

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%

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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Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data

Cited by 65 publications

References 13 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

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

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

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