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

Abstract

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.

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

“…Therefore, to set different threshold for OLI and MSI data should be considered in collaboration applications of the two data. Although many studies utilized various applications with different dates data and showed that a difference of less than three days does not lead to significant changes in the correlation coefficient [28,32,47], the compared result of the only one pair from the different dates in this study shows the lowest correlation and largest scatter and RMSD among all comparisons. This may be due to the presence of thin clouds which were not completely excluded by cloud removal or atmospheric correction [48] and influenced the comparison of these data pairs in summer [37].…”

“…The result analysis of the comparison between Landsat 8 OLI and Sentinel-2 MSI shows that these two data have good consistency while the value of R is little lower than previous studies in both bands and indices pairs [32][33][34][35][36][37]. The difference in the complex land surface in the urban area might have been caused by the influence from heterogeneity on the correlation of the two data [24].…”

“…This characteristic is bound to have an impact on land cover detection that relies on time series data for identification and extraction, such as farmland [34] and mangrove forests [44]. Therefore, this should be noted in joint applications of long time series and work to explore this issue has already started in Longos forest [32]. Meanwhile, the observation capability of S2 10 m data and L8 30 m data is different [31], which is reflected in Figure 5, in which error is located mostly at both ends of the data range in NDBI and MNDWI and [−0.1, 0.1] in NDVI which is the mixed zone of water, impervious surface and bare land [45].…”

“…Sentinel-2 and Landsat 8 data have been well combined to observe the urban area [31] and the temporal consistency of these two data has been evaluated for the Igneada Longos Forest [32]. However, there is still a lack of focus on the temporal consistency between these two datasets when applied in an urban area.…”

“…Therefore, the surface reflectance from Band 2 to Band 7 (Landsat heritage bands of the OLI) and three common indices in urban study (normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and modified normalized difference water index (MNDWI)) with the corresponding MSI bands and indices were investigated. Sentinel-2 and Landsat 8 data have been well combined to observe the urban area [31] and the temporal consistency of these two data has been evaluated for the Igneada Longos Forest [32]. However, there is still a lack of focus on the temporal consistency between these two datasets when applied in an urban area.…”