Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing

Abstract

Sentinel-2A MSI is the Landsat-like spatial resolution (10-60 m) super-spectral instrument of the European Space Agency (ESA), aimed at additional data continuity for global land surface monitoring with Landsat and Satellite Pour l'Observation de la Terre (SPOT) missions. Several simulation studies have been conducted in the last several years to show the potential of Sentinel-2A MSI (MultiSpectral Instrument). Now that real data are available, the first confirmations of this potential and comparisons with other operational systems are being made. This paper aims at evaluating Sentinel-2A MSI band ratio products that are relevant for geological remote sensing. A Sentinel-2A MSI and a Landsat 8 OLI (Operational Land Imager) scene were processed from their respective levels L1C and L1T to level L2A (bottom of atmosphere reflectance). Then, three band ratios originally defined for Landsat TM (Thematic Mapper) were used to map mineralogy associated with a hydrothermal alteration system in southeast Spain. The results obtained with Sentinel-2A MSI were compared with those obtained with Landsat 8 OLI and a simulated Sentinel-2A MSI dataset that was used before actual data were released. Results show that the images appear similar to the human eye having a correlation of approximately 0.8 and higher, but that the associated data ranges differ significantly. The resulting products are also compared to a published geologic map of the study area, and it is shown that the resulting maps correspond with the conceptual geologic model of the epithermal deposit.

Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

“…As pointed out by recent works [8], although a clear similarity between the image products of Landsat and Sentinel sensors can be observed by a visual inspection, when numerical values are compared, some band combinations can show differences, which are to be evaluated.…”

“…Clearly, the importance of these differences depends on the application and on the approach adopted to perform time series analyses or change detection. Methods based on physical quantities retrieved by remote sensing reflectance or empirical approaches based on multispectral indices are more affected by the problem [8]. Conversely, methods based on separate classification of every image are less affected, if the training is also independent [9,10].…”

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

“…For example, a root mean square error (RMSE) greater than 8% in the red band was found when comparing Sentinel and Landsat simulated data, due to the discrepancies in the nominal relative spectral response functions (RSRF) [70]. Werff compared Sentinel-2A MSI and Landsat-8 OLI Data [71], finding the correlation of their TOA reflectance products is higher than their bottom-of-atmosphere reflectance products. Besides, the combined use of multi-temporal images requires an accurate geometric registration, i.e., pixel-to-pixel correspondence for terrain-corrected products.…”

Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

“…Fernandez-Manso et al (2016) and Navarro et al (2017), among others, have evaluated burn severity based on Sentinel-2 MSI data successfully. Additionally, recent studies (Shoko and Mutanga, 2017;van der Werff and van der Meer, 2016) have showed the suitability and even superiority of Sentinel-2 MSI data in natural resources applications.…”