Data coverage
The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis. The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product. Known Issues Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the User Guide); the corresponding spectral data should be discarded from analysis. Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes. Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset. Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands. Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes. Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NODATA value of -9999 is used. In Figure 1, the pixels in front of the glaciers have surface reflectance values that are too low. Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NODATA value of -9999 is used. Unusually low reflectance around other bright land targets: While the HLS atmospheric correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT over bright targets can be unrealistically high in some cases, similar to cloud or snow. If this unrealistically high AOT is used to derive the surface reflectance of the neighboring pixels, very low surface reflectance values can result as shown in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NODATA value of -9999 is used. These types of bright targets are mostly man-made, such as buildings, parking lots, and roads. Dark plumes over water: The HLS atmospheric correction does not attempt aerosol retrieval over water. For water pixels, the AOT retrieved from the nearest land pixels is used to derive the surface reflectance, but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create dark stripes over water, as shown in Figure 3. This happens more often over large water bodies, such as lakes and bays, than over narrow rivers. Landsat WRS-2 Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before the derived surface reflectance is reprojected into Military Grid Reference System (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining clear pixels might be used for the atmospheric correction of the entire image. The AOT that is used can be quite different from the value for the adjacent row in the same path, which results in an artificial abrupt change from one row to the next, as shown in Figure 4. This occurrence is very rare. Landsat WRS2 path/row boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying thresholds to the histograms of some metrics for each path/row independently. If two adjacent rows in the same path have distinct distributions within the metrics, abrupt changes in masking patterns can appear across the row boundary, as shown in Figure 5. This occurrence is very rare. Fmask configuration was deficient for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water Occurrence data for a 2-3 month run in 2021. This impacted the masking results over water and in mountainous regions. The reflectance “scalefactor” and “offset” for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF (COG) files of some bands for a small number of granules. The lack of this information creates a problem for automatic conversion of the reflectance data, requiring explicit scaling in applications. The problem has been corrected, but the affected granules have not been reprocessed. Incomplete map projection information: For a time, HLS imagery was produced with an incomplete coordinate reference system (CRS). The metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary to geolocate pixels within the image
Access & permitted use
Review the publisher’s documentation for availability, eligibility and permitted uses, including commercial analysis, model training and redistribution.
Creative Commons BY 4.0
Need help sourcing data?
Tell us your required fields, population, geography and time period. We can review suitable sources and licensing options.
Request sourcing help