Cross-Sensors Comparison of Popular Vegetation Indexes From Landsat TM, ETM=, OLI, and Sentinel MSI for Time-Series Analysis Over Europe

In the perspective of long and dense time-series analyses for environmental monitoring applications, this article discusses a cross-comparison analysis between the different instruments of Landsat and Sentinel missions [thematic mapper (TM), enhanced thematic mapper plus (ETM+), operational land imager (OLI), and multispectral instrument (MSI)]. The level-2 surface reflectance (SR) products were considered (in particular, the reprocessed Collection-2 for Landsat). The calibration coefficients for four of the most popular vegetation indexes [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI), and normalized difference moisture index (NDMI)] were estimated, with the aim of harmonizing and minimizing radiometric differences for the combined use of these sensors. For this purpose, more than 20000 pairs of images almost simultaneously acquired (±1-day tolerance window) were selected over a period of several years (depending on the lifespan overlap of every sensor pair). Vegetation indices (VIs) were computed for each image, and for each cross comparison, 100 random extractions of 300 000 sample pixels were performed all over the European continent. Linear transformation functions for each VI and between each sensor couple were computed by regression analyses, also assessing the repeatability of the estimation. Furthermore, the stability over time of the obtained coefficients was assessed when enough years of corresponding observations are available.