Comparison of sentinel-2 multispectral imager (msi) and landsat 8 operational land imager (oli) for vegetation monitoring

Abstract

The availability of the coarse to moderate resolution no-cost remote sensing data and advances in image processing algorithms have exponentially increased the usage of geo-spatial technology in the last few decades. The latest Sentinel-2 Multispectral Imager (MSI) provides the surface reflectance data in VNIR and SWIR ranges since 2015 at higher spatial, temporal, and spectral resolution compared to the Landsat multispectral sensors, which have been providing such data since 1970s. The symmetry in spectral bands, sensor’s spectral response, spatial and radiometric resolution of Sentinel-2 MSI with the Landsat-8 OLI (Operational Land Imager) enables their integrated and complimentary use. In this study, we have compared the surface reflectance and vegetation indices (such as NDVI and EVI) values obtained from MSI and OLI sensors in four homogeneous land use land cover (LULC) features as cropland, agriculture fallow land, dense forest and open forest. The assessment is carried out for pre- and post-monsoon seasons over the Banki sub-division region of Cuttack district, Odisha, India. For all the LULC classes, high similarity is observed in the surface reflectance values in each band except NIR, green and red band. Similarly, for both the vegetation indices derived from Landsat 8 and Sentinel-2 data, high correlation with lower RMSE is observed for all the LULC classes. The correlation (R2) for cropland varied between 0.87 and 0.96, which varied between 0.56 and 0.97 for agriculture fallow, between 0.58 and 0.9 for dense forest, and between 0.68 and 0.87 for open forest. The surface reflectance pattern obtained for different vegetated features are similar for Landsat 8 and Sentinel-2. However, significantly a higher surface reflectance is observed for Landsat 8 in NIR band followed by red and green bands, where the differences are low for blue, SWIR1 and SWIR2 bands. The comparative assessment of indices suggests a higher correlation in values between Landsat 8 and Sentinel-2 for homogeneous features compared to heterogeneous class as forest. Thus, the integrated or complementary use of Landsat 8 and Sentinel-2 for heterogeneous features may induce some biases with a limited accuracy.