Harmonizing surface reflectance between Landsat-7 ETM + , Landsat-8 OLI, and Sentinel-2 MSI over China

Remote sensing dynamic monitoring methods often benefit from a dense time series of observations. To enhance these time series, it is sometimes necessary to integrate data from multiple satellite systems. In particular, the Landsat and Sentinel series provide a rich source of data for Earth observations. National Aeronautics and Space Administration (NASA) scientists proposed a method that creates global fixed per-band transformation coefficients to reduce the reflectance difference between Landsat-8 and Sentinel-2 for the harmonized Landsat and Sentinel-2 (HLS) surface reflectance product. However, the coefficient has yet to be further validated in the target study area and the coefficient can only be used for Landsat-8 and Sentinel-2, and is not useful for other sensors. The purpose of this study is to evaluate the potential of integrating surface reflectance data from Landsat-7, Landsat-8, and Sentinel-2. Some differences in the surface reflectance of the sensor pairs were identified, based upon which a cross-sensor conversion model was proposed, i.e., a suitable adjustment equation was fitted using an ordinary least squares (OLS) linear regression method to convert the Sentinel-2 reflectance values closer to the Landsat-7 or Landsat-8 values. The results show that the model adjusted the Sentinel-2 surface reflectance to match Landsat-7 or Landsat-8. The maximum MRE of the adjusted sensor for surface reflectance was reduced from 17.96 to 12.15%. Differences in reflectance produce corresponding differences in estimates of biophysical quantities, such as NDVI, with MRE as high as 18.33%. However, adjusting the Sentinel-2 sensor was able to reduce this part of the discrepancy to about 12.56%. The study believes that despite the differences in these datasets, it appears feasible to integrate these datasets by applying a linear regression correction between the bands.