N: 90 S: -90 E: 180 W: -180
Description
The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 1.6 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 HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI 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 HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 11 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. See the User Guide for a more detailed description of the individual bands provided in the HLSL30 product.
The HLS project is funded by NASA’s Satellite Needs Working Group (SNWG) which provides data products developed to meet the needs of stakeholders from US government agencies.
For information on known issues see the anomalies link under the Documents and Resources tab.
Version Description
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
Copy Citation
File Naming Convention
The file name begins with the Product Identifier (HLS.L30) followed by T plus the 5-character MGRS Tile Identifier (T17SLU), the Julian Date and Time of Production designated as YYYYDDDTHHMMSS (2020209T155956), the Version of the data collection (v1.5), the Variable/Band (SAA), and the Data Format (tif).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
GENERAL DOCUMENTATION
Dataset Resources
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Preliminary evaluation of remote sensing evapotranspiration models for field-scale agricultural water management in arid central Iran | Sima, Somayeh, Dehkordi, Iman Raissi, Taghikhani, Mohammadhosein, Karimi, Neamat | Reflectance | |
| Monitoring snow cover dynamics at 30-m resolution in higher latitude regions using Harmonized Landsat Sentinel-2 | Bonney, Mitchell T., Zhang, Yu | Albedo, Snow Cover, Reflectance | |
| A 30 m Multi-Year Dataset of Major Crop Distributions in Xinjiang, China (20132024) Based on Harmonized LandsatSentinel-2 Data | Liang, Qixiang, Di, Yanfeng, Hao, Xingming, Zhang, Jingjing, Ci, Mengtao, Sun, Fun, Wang, Chuan, Fan, Xue, Guo, Xinran | Reflectance, Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps | |
| Assessing Long-Term Spatiotemporal Dynamics of Microphytobenthos in a Korean Intertidal Flat Using Harmonized Landsat and Sentinel-2 (HLS) Data | Koh, Sooyoon, Baek, Seungil, Lee, Jong Hyuk, Noh, Jaehoon, Lee, Howon, Hyun, Myung Jin, Kim, Wonkook | Reflectance | |
| Exploring the potential of Harmonized Landsat-Sentinel-2 in predicting boreal forest structure from UAV-LiDAR data in Northwestern America | Enguehard, Lea, Kruse, Stefan, Hansch, Ronny, Herzschuh, Ulrike, Panda, Santosh, Heim, Birgit | Vegetation Cover, Forests, Canopy Characteristics, Land Use/Land Cover Classification, Alpine/Tundra, Reflectance, Dominant Species, Plant Phenological Changes, Normalized Difference Vegetation Index (NDVI), Plant Phenology, Evergreen Vegetation, Vegetation Index, Terrain Elevation, Vegetation Height | |
| A Cloud-Native Alternative Correction for Landsat-8/9 Collection 2 Surface Reflectance over Inland Waters | Bi, Shun, Shi, Kun, Xu, Jie | Reflectance | |
| A concise real-time identification method of maize phenological period | Zhu, Bingxue, Wu, Huizhu, Li, Sijia, Chen, Liwen, Song, Kaishan | Reflectance | |
| L. Duncanson a,* PM Montesano b,c,A. Neuenschwander d, A. | Duncanson, L., Montesano, P.M., Neuenschwander, A., Zarringhalam, A., Thomas, N., Minor, D.M., Wulder, M.A., White, J.C., Guenther, E., Feng, T., Leitold, V., Hancock, S., Armston, J., Puliti, S., Mandel, A.I., Shah, S., Silva, C., Purslow, M., Bruening, J., Breidenbach, J., Nsset, E., Saarela, S., Hunka, N., Kellner, J.R., Healey, S.P., Schepaschenko, D., Wallerman, J., Neigh, C.S.R., Carvalhais, N., Dubayah, R. | Land Use/Land Cover Classification, Reflectance, Biomass, Terrestrial Ecosystems, LIDAR WAVEFORM, Evergreen Vegetation, Shrubland/Scrub, Grasslands, Forests, Canopy Characteristics, Deciduous Vegetation | |
| Multi-source remote sensing of insect defoliation events in Abisko from | Feng, Shunan, Laursen, Simon Nyboe, Smart, Amy, Srensen, Katrine Stadsholt, Lund, Monika, Grillini, Federico, Rieksta, Jolanta, Jiao, Yi, Rinnan, Riikka, Westergaard-Nielsen, Andreas | Reflectance | |
| Wheat yield estimation at field level over France using Sentinel 2, Landsat-8, and meteorological time series | Houdmont, Pierre Loup, Claverie, Martin, Defourny, Pierre | Reflectance | |
| Mapping grain crop sowing date in smallholder systems using optical imagery | Prudente, Victor Hugo Rohden, Garcia-Medina, Mariana, Krishna, Vijesh, Euler, Michael, Bhattarai, Nishan, Lerner, Amy M., McDonald, Andrew James, Sherpa, Sonam, Rajan, Harshit, Urfels, Anton, Carneiro de Santana, Cleverton Tiago, Jain, Meha | Reflectance | |
| Mapping Reservoir Water Surface Area in the Contiguous United States | Yadav, Anshul, Zhang, Shuai, Zhao, Bingjie, Allen, George H., Pearson, Christopher, Huntington, Justin, Holman, Kathleen, McQuillan, Katie, Gao, Huilin | Reflectance | |
| Integrating Sparse LiDAR and Multisensor Time-Series Imagery From | Goel, Arnav, Song, Hunsoo, Jung, Jinha | Reflectance | |
| HLSWI: A Simple Yet Effective Water Index Using Harmonized Landsat-Sentinel-2 Data for Complex Scenarios | Meng, Sihan, Hu, Xin, Wang, Manlin, Li, Yao, Wang, Jie, Yin, Zhixiang, Wu, Penghai | Reflectance | |
| Plant functional traits as key regulators of interannual phenological variability in temperate forests | Zhao, Yingyi, Wang, Zhihui, Dronova, Iryna, Yan, Zhengbing, Moon, Minkyu, Meng, Lin, Wang, Jing, Song, Guangqin, Guo, Zhengfei, Su, Yanjun, Liu, Lingli, Wu, Jin | Reflectance, Maximum/Minimum Temperature, 24 Hour Precipitation Amount, Snow Water Equivalent, Vapor Pressure, Shortwave Radiation | |
| Near-Real-Time Field-Level Monitoring of Soybean and Corn Harvested Fields Using Satellite Imagery | Shao, Bosen, Di, Liping, Zhang, Chen, Li, Hui, Liu, Ziao | Reflectance | |
| Multi-source remote sensing for large-scale biomass estimation in | Contreras, Francisco, Cayuela, Maria L., Sanchez-Monedero, Miguel A., Perez-Cutillas, Pedro | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Reflectance | |
| Spatial-Temporal Dynamics of Vegetation Indices in Response to Drought | Crespo, Nazaret, Padua, Luis, Paredes, Paula, Rebollo, Francisco J., Moral, Francisco J., Santos, Joao A., Fraga, Helder | Reflectance | |
| Data-driven identification of high-nature value grasslands using Harmonized Landsat Sentinel-2 time series data | Groschler, Kim-Cedric, Martens, Tjark, Schrautzer, Joachim, Oppelt, Natascha | Reflectance | |
| Early Deforestation Detection in the Tropics using L-band SAR and | Flores-Anderson, Africa I., Cardille, Jeffrey A., Kellndorfer, Josef, Meyer, Franz J., Olofsson, Pontus | Reflectance | |
| Detecting the onset of rice field inundation in the Lower Mississippi River Basin via Harmonized Landsat Sentinel-2 (HLS) satellite time series | Deng, Yawen, Peng, Bin, Guan, Kaiyu, Runkle, Benjamin R.K., Moreno-Garcia, Beatriz, Wu, Xiaocui, Wang, Sheng, Zhou, Qu, Reba, Michele L. | Reflectance, Brightness Temperature, Surface Soil Moisture | |
| Elevation-Based Clustering and Spatiotemporal Analysis of Coffee Crop Agro-Environmental Dynamics | da Silva, Tamires Lima, Romani, Luciana Alvim Santos, Massruha, Silvia Maria F. Silveira | Reflectance | |
| Advancing winter wheat yield anomaly prediction with high-resolution | Bazzi, Hassan, Ciais, Philippe, Makowski, David, Baghdadi, Nicolas | Reflectance | |
| A review of crop yield estimation on pixel and field scales from remotely sensed data | Zhang, Fengjiao, Liang, Shunlin, Ma, Han, Li, Wenyuan, Chen, Yongzhe, He, Tao, Tian, Feng, Xu, Jianglei, Fang, Husheng, Liang, Hui, Ma, Yichuan, Jia, Aolin, Zhang, Yuxiang | Reflectance | |
| A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite | Armannsson, Sveinn E., Ulfarsson, Magnus O., Sigurdsson, Jakob | Reflectance |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| Band 1 | Coastal Aerosol | N/A | int16 | -9999 | N/A | 0.0001 | N/A |
| Band 2 | Blue | N/A | int16 | -9999 | N/A | 0.0001 | N/A |
| Band 3 | Green | N/A | int16 | -9999 | N/A | 0.0001 | N/A |
| Band 4 | Red | N/A | int16 | -9999 | N/A | 0.0001 | N/A |
| Band 5 | NIR | N/A | int16 | -9999 | N/A | 0.0001 | N/A |
| Band 6 | SWIR1 | N/A | int16 | -9999 | N/A | 0.0001 | N/A |
| Band 7 | SWIR2 | N/A | int16 | -9999 | N/A | 0.0001 | N/A |
| Band 9 | Cirrus | N/A | int16 | -9999 | N/A | 0.0001 | N/A |
| Band10 | TIRS1 | N/A | int16 | -9999 | N/A | 0.01 | N/A |
| Band11 | TIRS2 | N/A | int16 | -9999 | N/A | 0.01 | N/A |
| Fmask | Quality Bits | Bit Field | uint8 | 255 | N/A | N/A | N/A |
| SAA | Sun Azimuth Angle | Degree | uint16 | 40000 | N/A | 0.01 | N/A |
| SZA | Sun Zenith Angle | Degree | uint16 | 40000 | N/A | 0.01 | N/A |
| VAA | View Azimuth Angle | Degree | uint16 | 40000 | N/A | 0.01 | N/A |
| VZA | View Zenith Angle | Degree | uint16 | 40000 | N/A | 0.01 | N/A |