Preliminary Evaluation of the Consistency of Landsat 8 and Sentinel-2 Time Series Products in An Urban Area—An Example in Beijing, China

Abstract

Global urbanization is occurring rapidly, and numerous moderate resolution remote sensing data are being used to monitor this process. Landsat 8 OLI and Sentinel-2 MSI data are combined in many applications but few studies haves focused on either urban change or consistency between these two data in time series. To evaluate the varying correlation between the two sensors in a time series, the correlation coefficient (R) and root-mean-square deviation (RMSD) of seven band pairs and three indices (NDVI, NDBI, and MNDWI) were calculated in this study and the results of the built-up area identified by IBI derived from the above three indices were compared. It was found that the correlation between the two sensors (R > 0.8534, p < 0.0001) was good in most bands but not as good for indices (in half of the results, R < 0.9). Meanwhile, the correlation of the two sensors of both bands and indices fluctuated between seasons and the comparative results of built-up area identification between the two data are relative to this variation. Therefore, when the OLI and MSI data are used in future collaboration applications, the data and threshold selection should consider the consistency and the fluctuation between the two data, especially in both time series studies and urban detection.

Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

“…However, they did not use simultaneously acquired image pairs from L8 and S2. Better NDVI consistency statistics between L8 and S2 were reported by Nie et al [38] in an urban area in which RMSE values from 0.035 to 0.081 were computed. Li et al [46] reported that the NDVI calculated using the Sen2Cor surface reflectance of bands B8A and B04 was also reliable.…”

“…The sun glint effect observed in the present work was related to the sun elevation, appearing with sun elevation higher than 52 degrees (i.e., mainly in summer season). Working in urban areas (generally bright and anthropic areas such as PCG ones), Nie et al [38] reported worse consistency in summer data pairs, presenting the major discrepancies in the SWIR2 band. In addition, an interesting relationship between the sun glint on some plastic sheets and the satellite orbit (orbits leaving the sun on their back minimized this solar reflections) was found.…”

“…The mean surface reflectance values for the six common L8 and S2 bands were extracted from the 100 polygons and the three types of data tested (i.e., S2 L2A, L8 L2 and L8 PANSH) for each study site. In addition, and in the same way as in other investigations [34,38,46], the effects of image consistency on the normalized difference vegetation index (NDVI) [47] values were also studied in this work, since it is the most used spectral index in agriculture applications. At this point, it is important to note that the S2 MSI sensor is capable of capturing two NIR bands, Near Infrared (B8) and Narrow NIR (B8a), with the Relative Spectral Response Function (RSRF) of B8a being much more similar to the L8 NIR band [33,35,36].…”

“…The use of coefficient of determination (r 2 ) or coefficient of correlation (r), and root mean square error (RMSE) or root mean square difference (RMSD), is very usual when dealing with radiometric consistency studies between satellite sensors [34][35][36][37][38][39]46]. In this case, and according to Padró et al [35], the comparison between surface reflectance values from the different satellite data simultaneously acquired was carried out by computing r 2 and RMSE, the last expressed in reflectance units (%) when comparing bands.…”

Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

“…However, Arekhi et al did investigate sensor fusion across seasons for both individual bands and vegetation indices and concluded that absent systematic errors in data retrieval or random errors in atmospheric interference, L8 OLI and S2A MSI can be used for long term monitoring [36]. Other studies into sensor fusion did not consider more than two satellite systems of similar spectral and spatial features [37] or focused on categorically different land cover types such as urban areas [38].…”