## 5 Current Status and Limitations of Data Interoperability with Landsat and Sentinel
### Harmonized Landsat/Sentinel-2 (HLS) Project  
The NASA Harmonized Landsat/Sentinel-2 (HLS) Project has been working to create a seamless surface reflectance record using input data from Landsat 8 OLI and Sentinel-2A/B MSI. In this context, \"harmonized\" means that that sensor-specific radiometric and geometric differences are adjusted and removed, such that it should be transparent to end users which sensor originated any specific reflectance observation within an HLS time series. Specifically, the HLS processing stream includes a common atmospheric correction for each sensor based on the Landsat 8 Surface Reflectance Code (LaSRC) approach [26], BRDF adjustment to nadir view angle and constant solar elevation [27; 28], spectral bandpass adjustment of Sentinel-2 MSI to better match Landsat 8 OLI, and cloud and shadow masking. HLS products are geo-registered to Sentinel-2 clear images, and regridded using the Sentinel-2 Universal Transverse Mercator (UTM) tiling system at 30-m resolution. The overall goal is to create dense time series of reflectance observations suitable for use in mapping highly dynamic surface processes, including crop type, condition, and management practice, vegetation phenology, and surface water extent (Figure 12). To date, the HLS project has not directly evaluated how calibration uncertainty in Level 1 inputs propagates to output reflectance time series. In part, this reflects initial findings from the NASA/USGS and ESA instrument teams reporting that each sensor met its own calibration requirements, and that their radiometric responses were comparable within measurement uncertainties [20]. Instead, the main effort has gone toward assessing the absolute reflectance uncertainty of HLS products, and assessing short-term temporal stability (using reflectance retrievals from pseudo-invariant calibration sites or PICS). Short-term relative reflectance variability evaluated from bright PICS sites are generally in the range of \(\sim\)3% for both visible and near-infrared bands. Residual variability is likely due to a combination of unmasked thin clouds, errors in aerosol retrieval, errors in the BRDF adjustment, and errors in the spectral bandpass adjustment, in roughly that order. Figure 11: The collected absolute calibration results presented as a difference between the instrument ratios for each model. The error bars are a measure of the uncertainty in terms of the standard deviation of the mean. Assessment of absolute accuracy for HLS products has relied on independent studies of the LaSRC atmospheric correction, as well as comparisons with Moderate Resolution Imaging Spectrometer (MODIS) nadir-adjusted products and in situ data collected by Surface Radiation Budget Network (SURFRAD) broadband radiometers [29]. The LaSRC algorithms for Landsat 8 and Sentinel-2 have been validated by comparing output surface reflectance with results derived from running the 6S radiative transfer model using aerosol inputs from in situ AERONET (Aerosol Robotic Network) measurements. These comparisons indicate absolute surface uncertainty (root-mean-square error (RMSE)) varying from \(\sim\)0.006 (darker targets) to 0.009 (bright targets), which convert to relative errors of 2-6%. Comparisons with SURFRAD indicate higher uncertainties, with RMSE of \(\sim\)0.02 or \(\sim\)10% relative. It should be noted that these validation approaches do not measure the same sources of error. The AERONET comparisons only include errors associated with the LaSRC image-based aerosol retrieval, while the SURFRAD comparisons include the full range of HLS processing. At the same time, the SURFAD measurements require HLS spectral reflectance values to be interpolated to broader spectral bandpasses, which is another potential source of error. A key point is that the land remote sensing community still lacks an established network of ground observations of surface spectral reflectance for use in validating moderate-resolution products such as HLS.