Characterization of Sentinel-MSI and Landsat-OLI Filters Responsivities Differences for Soil Salinity Dynamic Monitoring in an Arid Landscape

Abstract

The Multi Spectral Instrument (MSI) onboard Sentinel satellites and the Operational Land Imager (OLI) installed on Landsat-8 satellite are designed to be similar. However, relative spectral response profiles characterizing the filters responsivities of the both instruments are not identical between the homologous bands, so some differences are probably expected in the recorded land-surface reflectance values. This paper analyse and compare the difference between the reflectances of the homologous spectral bands in the VNIR and SWIR of MSI and OLI sensors for soil salinity dynamic monitoring in arid landscapes. In addition, comparisons were carried out in term of conversion of these surface reflectances to the Soil Salinity and Sodicity Index (SSSI) and in term of the Semi-Empirical Predictive Model (SEPM) for salt-affected soil mapping. Analyses were performed on two images acquired with 1 day difference over the same area for a wide range of soil salinity degrees. The images were radiometrically calibrated, atmospherically corrected, and BRDF normalized. To generate data for comparison analysis, similarly to OLI, MSI images were resampled systematically in 30-m pixel size considering UTM projection and WGS84 datum. The comparisons were undertaken using regression analysis and root mean square difference (RMSD). The results obtained demonstrate that the two used images exhibited very significant fits (R2 of 0.93 for the costal and R2 ≥ 0.96 for the other bands of land-surface reflectances, and R2 of 0.95 for SSSI and SEPM). Moreover, excellent consistency was observed between the products of the two sensors, yielding a RMSD values less than 0.029 (reflectance units) for the bands and less than 0.004 for SSSI. For the SEPM, the calculated RMSD was varied between 0.12 and 2.65 dS.m-1, respectively, of non-saline and extreme salinity classes. While, the relative errors were varied between 0.046 and 0.005 for the considered soil salinity classes. Therefore, MSI and OLI sensors can be used jointly to characterize and to monitor accurately the soil salinity and it’s dynamic in time and space in arid landscape; indicating that rigorous preprocessing issues must be addressed before.