The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are onboard the Landsat 8 satellite, have acquired images of the Earth since February 2013. The sensors collect images of the Earth with a 16-day repeat cycle, referenced to the Worldwide Reference System-2. The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). Landsat 8 image data files consist of 11 spectral bands with a spatial resolution of 30 meters for bands 1-7 and bands 9-11; 15-meters for the panchromatic band 8. Delivered Landsat 8 Level-1 data typically include both OLI and TIRS data files; however, there may be OLI-only and/or TIRS-only scenes in the USGS archive. A Quality Assurance (QA.tif) band is also included. This file provides bit information regarding conditions that may affect the accuracy and usability of a given pixel – clouds, water or snow, for example.

This dataset contains the European coverage of Landsat-8 Collection 2 data, both Level-1 and Level-2, acquired since the beginning of the mission. Landsat-8 Collection 2 is the result of a reprocessing effort on the archive and on newly acquired products with significant improvement with respect to Collection 1 on data quality, obtained by means of advancements in data processing and algorithm development. Primarily, this involves a relevant improvement in the absolute geolocation accuracy (now re-baselined to the European Space Agency Copernicus Sentinel-2 Global Reference Image (GRI) but also includes updated digital elevation modelling sources, improved Radiometric Calibration (even correction for the TIRS striping effect), enhanced Quality Assessment Bands, updated and consistent metadata files and usage of Cloud Optimised Georeferenced (COG) Tagged Image File Format. Landsat-8 Level-1 products combine data from the two Landsat instruments, OLI and TIRS. The Level-1 products generated can be either L1TP or L1GT: L1TP - Level-1 Precision Terrain (Corrected) (L1T) products: Radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation model (DEM) data to correct for relief displacement. The highest quality Level-1 products suitable for pixel-level time series analysis. GCPs used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000) data set. L1GT - Level-1 Systematic Terrain (Corrected) (L1GT) products: L1GT data products consist of Level-0 product data with systematic radiometric, geometric and terrain corrections applied and resampled for registration to a cartographic projection, referenced to the WGS84, G873, or current version. The dissemination server contains three different classes of Level-1 products Real Time (RT): Newly acquired Landsat-8 OLI/TIRS data are processed upon downlink but use an initial TIRS line-of-sight model parameters; the data are made available in less than 12 hours (4-6 hours typically). Once the data have been reprocessed with the refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier (in 14-16 days). Tier 1 (T1): Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP) corrected data that have well-characterized radiometry and are inter-calibrated across the different Landsat instruments. The georegistration of Tier 1 scenes is consistent and within prescribed image-to-image tolerances of ≦ 12-metre radial root mean square error (RMSE). Tier 2 (T2): Landsat scenes not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do not meet the Tier 1 geometry specification due to less accurate orbital information (specific to older Landsat sensors), significant cloud cover, insufficient ground control, or other factors. This includes Systematic Terrain (L1GT) and Systematic (L1GS) processed data. Landsat-8 Level-2 products are generated from L1GT and L1TP Level-1 products that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. The data are available a couple of days after the Level-1 T1/T2. The Level-2 products generated can be L2SP or L2SR: L2SP - Level-2 Science Products (L2SP) products: include Surface Reflectance (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients file, and Quality Assessment (QA) Bands. L2SR - Level-2 Surface Reflectance (L2SR) products: include Surface Reflectance (SR), an angle coefficients file, and Quality Assessment (QA) Bands; it is generated if ST could not be generated. Two different categories of Level-1 products are offered: LC with Optical, Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal not available). For the Level-2 data, only LC combined products are generated.

Since 1972, the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites have continuously acquired images of the Earth’s land surface, providing uninterrupted data to help land managers and policymakers make informed decisions about natural resources and the environment.

Landsat is a part of the USGS National Land Imaging (NLI) Program. To support analysis of the Landsat long-term data record that began in 1972, the USGS. Landsat data archive was reorganized into a formal tiered data collection structure. This structure ensures all Landsat Level 1 products provide a consistent archive of known data quality to support time-series analysis and data “stacking”, while controlling continuous improvement of the archive, and access to all data as they are acquired. Collection 1 Level 1 processing began in August 2016 and continued until all archived data was processed, completing May 2018. Newly-acquired Landsat 8 and Landsat 7 data continue to be processed into Collection 1 shortly after data is downlinked to USGS EROS.

Acknowledgement or credit of the USGS as data source should be provided by including a line of text citation such as the example shown below. (Product, Image, Photograph, or Dataset Name) courtesy of the U.S. Geological Survey Example: Landsat-8 image courtesy of the U.S. Geological Survey

This dataset shows the tiling grid and their Row and Path IDs for Landsat 4 - 9 satellite imagery. The IDs are useful for selecting imagery of an area of interest. Landsat 4 - 9 are a series of Earth observation satellites, part of the US Landsat program aimed at monitoring Earth's land surfaces since 1982.

The Worldwide Reference System (WRS) is a global notation system used for cataloging and indexing Landsat imagery. It employs a grid-based system consisting of path and row numbers, where the path indicates the longitude and the row indicates the latitude, allowing users to easily locate and identify specific scenes covering a particular area on Earth.

Landsat satellites 4,5,7, 8, and 9 follow WRS-2 which this dataset describes.

This dataset corresponds to the descending Path Row identifiers as these correspond to day time scenes.

eAtlas Notes:
It should be noted that the extent boundaries of the scene polygons in this dataset are only indicative of the imagery extent. For Landsat 5 images the individual images move around by about 10 km and the shape of the Landsat 8 and 9 images do not match the shape of the WRS-2 polygons. The angle of the top and bottom edges are at a different angle to the imagery, where the imagery is more square in shape. The left and right edges of the polygons are also smaller than the imagery. As a result of this, this dataset is probably not suitable as a clipping mask for the imagery for these satellites.

This dataset is suitable for determining the approximate extent of the imagery and the associated Row and Path IDs for a given scene.

Why is this dataset in the eAtlas?:
Landsat imagery is very useful for the studying and mapping of reef systems. Selecting imagery for study often requires knowing the Path and Row numbers for the area of interest. This dataset is intended as a reference layer. This metadata is included to link to from the associated mapping layer. The eAtlas is not the custodian of this dataset and copies of the data should be obtained from the original sources. The eAtlas does however keep a cached version of the dataset from the time this dataset was setup to make available should the original dataset no longer become available.

eAtlas Processing:
The original data was sourced from USGS (See links). No modifications to the underlying data were performed.

Location of the data:
This dataset is filed in the eAtlas enduring data repository at: data
on-custodian\2020-2024\World_USGS_Landsat-WRS-2

The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally-dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm makes use of predictors derived from individual ARD Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions. Scene-level products include pixel-level burn probability (BP) and burn classification (BC) images, corresponding to each Landsat image in the ARD time series. Annual composite products are also available by summarizing the scene level products. Prior to generating annual composites, individual scenes that had > 0.010 burned proportion were visually assessed as part of a quality assurance check. Scenes with obvious commission errors were removed. The annual products include the maximum burn probability (BP), burn classification count (BC) or the number of scenes a pixel was classified as burned, filtered burn classification (BF) with burned areas persistent from the previous year removed, and the burn date (BD) or the Julian date of the first Landsat scene a burned areas was observed in. Vectorized versions of the BF raster are also provided as shapefiles (BF_labeled) with attributes including summary statistics of BC, BD, BP, as well as the majority level 3 ecoregion (Omernik and Griffith, 2014) and count of pixels by each National Land Cover Database Category (Vogelmann et al., 2001; Yang et al., 2018) for each burned area polygon. These products were generated for the conterminous United States for 1984 through 2019 individually for Landsat TM (5), Landsat ETM+ (7), OLI/TIRS (8), and for all sensors combined. The products for each sensor combination and year are contained in a compressed tar file are available through the USGS Science Base Catalog (https://doi.org/10.5066/P9QKHKTQ) and also at https://gec.cr.usgs.gov/outgoing/baecv/LBA/LBA_CU_C01_V01/ Additional details about the algorithm used to generate these products are described in Hawbaker, T.J., Vanderhoof, M.K., Schmidt, G.L., Beal, Y, Takacs, J.D., Falgout, J.T., Picotte, J.J., and Dwyer, J.L. 2020. The Landsat Burned Area algorithm and products for the conterminous United States. Remote Sensing of Environment, Vol. 244, https://doi.org/10.1016/j.rse.2020.111801

This dataset contains Landsat-derived images of Evaporative Fraction (ETf), Reference Evapotranspiration (ETo), and Actual Evapotranspiration (ETa) over a portion of California’s Central Valley for 15 dates in 2016. Each of the 15 images used in this study had three corresponding Tif files representing ETf, ETo, and ETa. Data used in this project was sourced from Landsat 8 Surface Reflectance Tier 1 images processed in Google Earth Engine (GEE). These images contain five visible and near-infrared (VNIR) bands and two short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature. To determine thermal properties of images to aid in ET calculation, the TIR Band 10 (B10) containing brightness temperature was chosen to determine Land Surface Temperature (LST).

This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m2 for 2017; 13 m2 for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.

All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in Table 1. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.

Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey

For an interactive map demo of these datasets via Google Earth Engine Apps, visit: https://ekyzivat.users.earthengine.app/view/super-resolution-demo

Table 1: File naming scheme based on region, with some regions requiring two-scene mosaics.

Region

Landsat ID

Mosaic name

Yukon Flats Basin

LC08_L2SP_068014_20170708_20200903_02_T1

LC08_20170708_yflats_cog.tif

“

LC08_L2SP_068013_20170708_20201015_02_T1

“

Old Crow Flats

LC08_L2SP_067012_20170903_20200903_02_T1

-

Mackenzie River Delta

LC08_L2SP_064011_20170728_20200903_02_T1

LC08_20170728_inuvik_cog.tif

“

LC08_L2SP_064012_20170728_20200903_02_T1

“

Canadian Shield Margin

LC08_L2SP_050015_20170811_20200903_02_T1

LC08_20170811_cshield-margin_cog.tif

“

LC08_L2SP_048016_20170829_20200903_02_T1

“

Canadian Shield near Baker Creek

LC08_L2SP_046016_20170831_20200903_02_T1

-

Canadian Shield near Daring Lake

LC08_L2SP_045015_20170723_20201015_02_T1

-

Peace-Athabasca Delta

LC08_L2SP_043019_20170810_20200903_02_T1

-

Prairie Potholes North 1

LC08_L2SP_041021_20170812_20200903_02_T1

LC08_20170812_potholes-north1_cog.tif

“

LC08_L2SP_041022_20170812_20200903_02_T1

“

Prairie Potholes North 2

LC08_L2SP_038023_20170823_20200903_02_T1

-

Prairie Potholes South

LC08_L2SP_031027_20170907_20200903_02_T1

-

Fairbanks

LT05_L2SP_070014_19850831_20200918_02_T1

-

References:

Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. Remote Sensing, 11(18), 2163. https://doi.org/10.3390/rs11182163

Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1707

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. Environ. Res. Lett, 16, 35010. https://doi.org/10.1088/1748-9326/abc848

Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1859

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The near-real-time data dissemination service is delivered by Earth Observation from Space - a Program of Geoscience Australia responsible for acquiring, curating and analysing remotely sensed data from satellites orbiting the earth. Landsat 7 and Landsat 8 data is scene based and comprises preview images in JPG format (one at high and one at low resolution) and individual files per spectral band in TIFF format. Included with the data are licence and product descriptions where applicable. All files are in folders sorted according to date of acquisition and are made available within 3 to 6 hours of the receipt of source information. Only the last few days of data is held on the server due to the size of the imagery files. Downloading data requires an FTP enabled browser. ftp://ftp.ga.gov.au/outgoing-emergency-imagery Landsat ETM+, TM and MSS data is available under Creative Commons Licence 3.0 ## Dataset History Geoscience Australia receives and processes data from the Landsat series of satellites. The Landsat Program is the longest running satellite series for imaging Earth from space. The first satellite in the series was launched in 1972, and since then seven satellites have been launched. The eighth satellite, the Landsat Data Continuity Mission, is due to be launched early 2013. The Landsat Program has produced one of the most successful satellite ventures in space history with Landsat 5. Commencing in March 1984, the satellite had an expected life of 3 years. As of 2012 Geoscience Australia no longer processes or distributes Multispectral Scanner (MSS) data. Of the sensors carried, the Multispectral Scanner (MSS) with 80-metre pixels and four spectral bands was found to provide information of unforeseen value. In July 1982, the launch of Landsat 4 saw the inclusion of the Thematic Mapper (TM) sensor with a 30-metre resolution and 7 spectral bands. Both sensors are on Landsat 5. The newest in this series of remote sensing satellites is Landsat 7. Launched on 15 April 1999, Landsat 7 has the new Enhanced Thematic Mapper Plus (ETM+) sensor. This sensor has the same 7 spectral bands as its predecessor, TM, but has an added panchromatic band with 15-metre resolution and a higher resolution thermal band of 60 metres. The ETM+ sensor also has a five percent absolute radiometric calibration. Landsat MSS data was recorded over Australia by USGS from 1972 to 1979. Geoscience Australia (then ACRES) began acquisition of this data in September 1979. Acquisition of Landsat MSS image data ceased in December 1997. From late 1979 we have archived nearly every pass over Australia and continue to receive and archive data from Landsat 7 daily. ## Dataset Citation Geoscience Australia (2015) HUN Historical Landsat Images Mine Foot Prints v01. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/28de7771-28f5-4d24-943f-0addea07c8c4.

This is a thin cloud removal dataset (NUAA-CR4L8/9) for Landsat 8 and 9 images. If you find this useful, consider citing our work:

[1] Li, J., Wang, Y., Sheng, Q., Wu, Z., Wang, B., Ling, X., Liu, X., Du, Y., Gao, F., Camps-valls, G., Molinier, M., 2025. CloudRuler : Rule-based transformer for cloud removal in Landsat images. Remote Sens. Environ. 328, 114913. https://doi.org/10.1016/j.rse.2025.114913

[2] Du, Y., Li, J., Sheng, Q., Zhu, Y., Wang, B., Ling, X., 2024. Dehazing Network: Asymmetric Unet Based on Physical Model. IEEE Trans. Geosci. Remote Sens. 62, 1–12. https://doi.org/10.1109/TGRS.2024.3359217

The Collection 2 Level 1 data served as the source data for the NUAA-CR4L8/9 dataset. There are 20 paired images, consisting of both cloudy and cloud-free scenes, from Landsat 8 and 9, acquired between 2022 and 2024, with an 8-day time interval for the same region in each image pair. In each image pair, if the Landsat 8 or 9 image is cloudy, the cloud-free image is chosen from the other satellite. The ratio of training and testing image pairs is set to 4:1. In this way, 16 image pairs are used for training, and four image pairs are used for testing, respectively. All the images are located in Southeast of USA. Both training and testing datasets contain different types of land cover. This makes the NUAA-CRL8/9 dataset representative.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Landsat TM, and ETM+ data are provided in GeoTIFF for Level 1T (terrain corrected) products, or for either Level 1Gt (systematic terrain corrected) or Level 1G (systematic corrected) products, if Level 1T processing is not available. GeoTIFF defines a set of publicly available TIFF tags that describe cartographic and geodetic information associated with TIFF images. GeoTIFF is a format that enables referencing a raster image to a known geodetic model or map projection.

The initial tags are followed by image data that, in turn, may be interrupted by more descriptive tags. By using the GeoTIFF format, both metadata and image data can be encoded into the same file. The Landsat 7 ETM+ GeoTIFF file format is described in detail in the"Landsat 7 ETM+ Level 1 Product Data Format Control Book (DFCB), LSDS-272": http://landsat.usgs.gov/documents/LSDS-272.pdf. The Landsat 4-5 TM GeoTIFF file format is described in detail in the "Landsat Thematic Mapper (TM) Level 1 (L1) Data Format Control Book (DFCB), LS-DFCB-20": http://landsat.usgs.gov/documents/LS-DFCB-20.pdf.

For more information on GeoTIFF visit: http://trac.osgeo.org/geotiff

Dataset History

ORGANIZATION

Each band of Landsat data in the GeoTIFF format is delivered as a grayscale, uncompressed, 8-bit string of unsigned integers. A metadata (MTL) file is included with data processed through the Level-1 Product Generation System (LPGS). A file containing the ground control points (GCP) used during image processing is also included. A processing history (WO) file is included with data processed through the National Landsat Archive Production System (NLAPS). Landsat 7 ETM+ SLC-off products processed after December 11, 2008, will include an additional directory (gap_mask) that contains a set of flat binary scan gap mask files (one per band). (Please note that the processing date and acquisition date are not necessarily the same.)

* DATA FILE NAMES

The file naming convention for Landsat LPGS-processed GeoTIFF data

is as follows:

LMSppprrrYYYYDOYGSIVV_BN.TIF where:

 L      = Landsat 

 M      = Mission (E for ETM+ data; T for TM data; M for MSS)

 S      = Satellite (7 = Landsat 7, 5 = Landsat 5, 4 = Landsat 4)

 ppp     = starting path of the product

 rrr     = starting and ending rows of the product

 YYYY    = acquisition year

 DOY     = Julian date

 GSI     = Ground Station Identifier 

 VV     = 2 digit version number

 BN     = file type:

   B1     = band 1

   B2     = band 2

   B3     = band 3

   B4     = band 4

   B5     = band 5

   B6_VCID_1 = band 6L (low gain) (ETM+)

   B6_VCID_2 = band 6H (high gain) (ETM+)

   B6     = band 6 (TM and MSS)

   B7     = band 7 

   B8     = band 8 (ETM+)

   MTL    = Level-1 metadata

   GCP    = ground control points

 TIF     = GeoTIFF file extension

The file naming convention for Landsat NLAPS-processed GeoTIFF data

is as follows:

LLNppprrrOOYYDDDMM_AA.TIF where:

 LL     = Landsat sensor (LT for TM data)

 N      = satellite number

 ppp     = starting path of the product

 rrr     = starting row of the product

 OO     = WRS row offset (set to 00)

 YY     = last two digits of the year of 

        acquisition

 DDD     = Julian date of acquisition

 MM     = instrument mode (10 for MSS; 50 for TM)

 AA     = file type:

   B1     = band 1

   B2     = band 2

   B3     = band 3

   B4     = band 4

   B5     = band5

   B6     = band 6

   B7     = band 7

   WO     = processing history file 

 TIF     = GeoTIFF file extension

* GAP MASKS

All Landsat 7 ETM+ SLC-off imagery processed on or after December 11, 2008, will include gap mask files. (Please note the difference between acquisition date and processing date, files dates are not necessarily the same.) The gap mask files are bit mask files showing the locations of the image gaps (areas that fall between ETM+ scans). One tarred and gzip-compressed gap mask file is provided for each band in GeoTIFF format. The file naming convention for gap mask files is identical to that described above for LPGS-processed GeoTIFF data, with "_GM" inserted before file type.

If gap mask files are not included with the data, a tutorial for creating them can be found at: http://landsat.usgs.gov/gap_mask_files_are_not_provided_can_I_create_my_own.php

* README

The README_GTF.TXT (or README.GTF) is an ASCII text file and is this file.

* READING DATA

Delivered via file transfer protocol (FTP): data files are tarred and g-zip compressed and will need to be unzipped and untarred before the data files can be used. UNIX systems should have the "gunzip" and "tar"

commands available for uncompressing and accessing the data. For PC users, free software can be downloaded from an online source. Otherwise, check your PC, as you may already have appropriate software available.

No software is included on this product for viewing Landsat data.

GENERAL INFORMATION and DOCUMENTATION

Landsat Project Information:

http://landsat.usgs.gov

Landsat data access:

* USGS Global Visualization Viewer (GloVis): http://glovis.usgs.gov

* USGS EarthExplorer: http://earthexplorer.usgs.gov

* USGS LandsatLook Viewer: http://landsatlook.usgs.gov

* Landsat International Ground Station (IGS) network:

         http://landsat.usgs.gov/about_ground_stations.php

FGDC metadata:

http://www.fgdc.gov/metadata

Data restrictions and citation:

https://lta.cr.usgs.gov/citation

* National Snow and Ice Data Center (NSIDC)

Radarsat Antarctic Mapping Project (RAMP) elevation data citation:

Liu, H., K. Jezek, B. Li, and Z. Zhao. 2001.

Radarsat Antarctic Mapping Project digital elevation model version 2.

Boulder, CO: National Snow and Ice Data Center. Digital media.

For information on the data, please refer to the data set documentation

available at the following web site:

http://nsidc.org/data/nsidc-0082.html

PRODUCT SUPPORT

For further information on this product, contact USGS

EROS Customer Services:

Customer Services (ATTN: Landsat)

U.S. Geological Survey

Earth Resources Observation and Science (EROS) Center

47914 252nd Street

Sioux Falls, SD 57198-0001

Tel: 800-252-4547

Tel: 605-594-6151

Email: custserv@usgs.gov

For information on other products from USGS EROS:

http://eros.usgs.gov/ or https://lta.cr.usgs.gov/

For information on other USGS products:

http://ask.usgs.gov/

or call 1-888-ASK-USGS (275-8747)

DISCLAIMER

Any use of trade, product, or firm names is for descriptive

purposes only and does not imply endorsement by the U.S.

Government.

Publication Date: July 2014

Dataset Citation

U.S. Geological Survey (2014) SYD Landsat raw data v01. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/fe7aa98d-ea2a-48fc-bc09-1d5ce3a50246.

The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally-dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm makes use of predictors derived from individual ARD Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions. Scene-level products include pixel-level burn probability (BP) and burn classification (BC) images, corresponding to each Landsat image in the ARD time series. Annual composite products are also available by summarizing the scene level products. Prior to generating annual composites, individual scenes that had > 0.010 burned proportion were visually assessed as part of a quality assurance check. Scenes with obvious commission errors were removed. The annual products include the maximum burn probability (BP), burn classification count (BC) or the number of scenes a pixel was classified as burned, filtered burn classification (BF) with burned areas persistent from the previous year removed, and the burn date (BD) or the Julian date of the first Landsat scene a burned areas was observed in. Vectorized versions of the BF raster are also provided as shapefiles (BF_labeled) with attributes including summary statistics of BC, BD, BP, as well as the majority level 3 ecoregion (Omernik and Griffith, 2014) and count of pixels by each National Land Cover Database Category (Vogelmann et al., 2001; Yang et al., 2018) for each burned area polygon. These products were generated for the conterminous United States for 1984 through 2019 individually for Landsat TM (5), Landsat ETM+ (7), OLI/TIRS (8), and for all sensors combined. The products for each sensor combination and year are contained in a compressed tar file are available through the USGS Science Base Catalog (https://doi.org/10.5066/P9QKHKTQ) and also at https://gec.cr.usgs.gov/outgoing/baecv/LBA/LBA_CU_C01_V01/ Additional details about the algorithm used to generate these products are described in Hawbaker, T.J., Vanderhoof, M.K., Schmidt, G.L., Beal, Y, Takacs, J.D., Falgout, J.T., Picotte, J.J., and Dwyer, J.L. 2020. The Landsat Burned Area algorithm and products for the conterminous United States. Remote Sensing of Environment, Vol. 244, https://doi.org/10.1016/j.rse.2020.111801

The PixBox-L8-CMIX dataset was used as a validation reference within the first Cloud Masking Inter-comparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) in 2019. The PixBox-L8-CMIX pixel collection was existing prior to CMIX and conducted already in 2015.

The overarching idea of PixBox is a quantitative assessment of the quality of a pixel classification which is the result of an automated algorithm/procedure. Pixel classification is defined as assigning a certain number of attributes to an image pixel, such as cloud, clear sky, water, land, inland water, flooded, snow etc. Such pixel classification attributes are typically used to further guide higher level processing.

The PixBox dataset production: trained experienced expert(s) manually classify pixels of an image sensor into a pre-defined detailed set of classes. These are typically different cloud transparencies, cloud shadow, condition of underlying surface (“semi-transparent clouds over snow”, “clouds over bright scattering water”). An average collected dataset includes several 10-thousands of pixels because it has to be representative for all classes, and for various observation and environmental conditions, such as climate zones, sun illumination etc. Quality control of the collected pixels is important in order to detect misclassifications and systematic errors. An auto-associative neural network is trained for this purpose.

The PixBox-L8-CMIX dataset is a pixel collection containing 18,830 pixels manually collected from 11 Landsat 8 Level 1 products. The dataset is temporally well distributed. Spatially it is focused on coastal areas, mainly in Europe. Thematically it is focused on coastal zones, but still representing land and water surfaces.

PixBox-L8-CMIX dataset

The PixBox-L8-CMIX dataset consists of two two main ZIP files, one holding the pixel collection and description, and another one with all used Landsat 8 L1 data. The dataset is structured as follows:

PixBox-L8-CMIX.zip

The collected features (CSV file).

A description to all categories and classes, incl. linkage to the used Landsat 8 L1 products.

Landsat8_L1.zip

11 zipped Landsat 8 Level 1 products [1], used to produce the dataset.

Files

pixbox_landsat8_cmix_20150527.csv - This file contains all collected pixel information in CSV format. All collected classes are stored as integer values. A description of the categories and definition of the integers to class names is given in the additional description file.

pixbox_landsat8_cmix_20150527_description.txt - This file gives a clear description of the categories and classes. It can be used to convert the class ID numbers, stored in the CSV, to class strings. Additionally, it links the satellite product ID, given in the CSV, to the Sentinel-2 L1C product names.

11 Landsat 8 L1 products in ZIP format.

References

[1] Landsat 8 products courtesy of the U.S. Geological Survey

This is a NASA Landsat 8 image of the South-east of Scotland which was acquired on 06/07/2013. You can view the metadata for this record here: http://glovis.usgs.gov/ImgViewer/showmetadata.cgi?scene_id=LC82040212013187LGN00 The image has 3.8% cloud cover and a quality rating of 9. This image is 32bit and will load in many GIS but may not load in standard image viewers. Downloaded from glovis.usgs.gov portal and manipulated into a true-color image using QGIS 2.2. Bands 2/3/4 where used to make the true-color image. Please reference Landsat NASA as the data source when using this dataset using the following: Landsat8 image (LC82040212013187LGN00), NASA 2013. Aerial or Satellite Imagery. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-05-29 and migrated to Edinburgh DataShare on 2017-02-22.

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 NO_DATA 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 NO_DATA 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 NO_DATA 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 “scale_factor” 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 but might not be in a standard form, especially for granules produced early in the HLS mission. As a result, an error will occur in certain image processing packages due to the incomplete CRS. The simplest solution is to update to the latest version of Geospatial Data Abstraction Library (GDAL) and/or rasterio, which use the available information without error.

• False northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false northing for the UTM projection, and the angle data are supposed to follow the same convention. However, the L30 angle data incorrectly uses a false northing of 10^7. There is no problem with the angle data itself, but the false northing needs to be set to 0 for it to be aligned with the reflectance.

• L30 from Landsat L1GT scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. However, some scenes made it through screening for a short period of HLS production. L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 granule by examining the HLS cmr.xml metadata file.

• The UTC dates in the L30/S30 filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological Survey (USGS) in naming their Level 1 images, and HLS processing retains this information to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired in the 22nd hour of day 117 of

Many applications that target dynamic land surface processes require a temporal observation frequency that is not easily satisfied using data from a single optical sensor. Sentinel-2 and Landsat provide observations of similar nature and offer the opportunity to combine both data sources to increase time-series temporal frequency at high spatial resolution. Multi-sensor image compositing is one way for performing pixel-level data integration and has many advantages for processing frameworks, especially if analyses over larger areas are targeted. Our compositing approach is optimized for narrow temporal-intervals and allows the derivation of time-series of consistent reflectance composites that capture field level phenologies. We processed more than a years' worth of imagery acquired by Sentinel-2A MSI and Landsat-8 OLI as available from the NASA Harmonized Landsat-Sentinel dataset. We used all data acquired over Germany and integrated observations into composites for three defined temporal intervals (10-day, monthly and seasonal). Our processing approach includes generation of proxy values for OLI in the MSI red edge bands and temporal gap filling on the 10-day time-series. We then derive a national scale crop type and land cover map and compare our results to spatially explicit agricultural reference data available for three federal states and to the results of a recent agricultural census for the entire country. The resulting map successfully captures the crop type distribution across Germany at 30m resolution and achieves 81% overall accuracy for 12 classes in three states for which reference data was available. The mapping performance for most classes was highest for the 10-day composites and many classes are discriminated with class specific accuracies >80%. For several crops, such as cereals, maize and rapeseed our mapped acreages compare very well with the official census data with average differences between mapped and census area of 11%, 2% and 3%, respectively. Other classes (grapevine and forest classes) perform slightly less well, likely, because the available reference data does not fully capture the variability of these classes across Germany. The inclusion of the red edge bands slightly improved overall accuracies in all cases and improved class specific accuracies for most crop classes. Overall, our results demonstrate the valuable potential of approaches that utilize data from Sentinel-2 and Landsat which allows for detailed assessments of agricultural and other land-uses over large areas.

This .zip file will provide row and path number for the imagery of interest. Provided in Georeferenced TIFF format, projection WGS84.

This dataset contains the China terrace map at 30 m resolution in 2018. The map values and their corresponding classes are as follows:

0: Non-terrace 1: Terrace 255: No data

The 30 m China terrace map can also be viewed online at https://cbw.users.earthengine.app/view/chinaterracemap

Citations:

When using this dataset, please cite both the dataset and the following data description article:

Cao, B., Yu, L., Naipal, V., Ciais, P., Li, W., Zhao, Y., Wei, W., Chen, D., Liu, Z., and Gong, P.: A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine, Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, 2021.

The Essential thermaL Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

This dataset is the ELITE Landsat narrowband (NBE) emissivity dataset (Landsat5 Channel 6) over the continental United States (Cheng et al. 2021). For the non-vegetated surfaces, the LSEs were estimated using an empirical method that establishes the linkages between the ASTER NBE product and the Landsat SR product. For the vegetated surfaces, the LSEs were derived by using the 4SAIL model established lookup table (LUT) provided with the NBE of the leaf emissivity, soil background emissivity, and leaf area index (LAI).

This is the ELITE Landsat NBE dataset from Landsat 5 in July 2005. Please click here to download the ELITE Landsat NBE dataset from Landsat 5 in January 2005.

Dataset Characteristics:

• Spatial Coverage: The Continental United States
• Temporal Coverage: 2005.7
• Spatial Resolution: 30m
• Temporal Resolution: 16 days
• Data Format: Geotiff
• Scale: 0.001

Citation (Please cite these papers when using the data):

1. Cheng, J., Meng, X., Dong, S., & Liang, S. (2021). Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data. Science of Remote Sensing, 4, 100032

If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).

The Essential thermaL Infrared remoTe sEnsing (ELITE) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn).

This dataset is the Global ELITE Landsat LST dataset generated by the radiative transfer method (Cheng et al., 2021). Firstly, a new scheme was used to determine the real-time Landsat 5/7/8 narrowband emissivity . Then, the MERRA2 reanalysis product was used for thermal infrared data atmospheric correction (Meng and Cheng, 2018). Finally, an LST product with 30m spatial resolution was generated using the radiative transfer equation method.

This is the ELITE Landsat LST dataset for Landsat 8 from July 25, 2020 to July 28, 2020. Please click here to download the ELITE Landsat LST for Landsat 8 from July 21, 2020 to July 24, 2020 and click here to download the ELITE Landsat LST for Landsat 8 from July 29, 2020 to July 31, 2020.

Dataset Characteristics:

Spatial Coverage: Global landmsss

Temporal Coverage: 2020.7.25-2020.7.28

Spatial Resolution: 30m

Temporal Resolution: 16 days

Data Format: Geotiff

Scale: 0.01

Citation (Please cite these papers when using the data):

Cheng, J., Meng, X., Dong, S., & Liang, S. (2021). Generating the 30-m land surface temperature product over continental China and USA from landsat 5/7/8 data. Science of Remote Sensing, 4, 100032

Meng, X., & Cheng, J. (2018). Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data. Remote Sensing, 10, 474

If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).

This data publication contains a set of files in which areas affected by fire or by harvest from 1984 to 2015 are identified at the level of individual 30m pixels on the Landsat grid. Details of the product development can be found in Guindon et al (2018). The change detection is based on reflectance-corrected yearly summer (July and August) Landsat mosaics from 1984 to 2015 created from individual scenes developed from USGS reflectance products (Masek et al, 2006; Vermote et al, 2006). Briefly, the change detection method uses a six-year temporal signature centered on the disturbance year to identify fire, harvest and no change. The signatures were derived from visually-interpreted disturbance or no-change polygons that were used to fit a decision tree model. The method detects about 91% of the areas harvested and 85% of the areas burned across Canada’s forests over the study period, but overestimates areas disturbed in the two initial and mostly in the two final years of the 1985 to 2015 time series. This is caused by the absence of appropriate pre-disturbance and post-disturbance data for the model-based detection and attribution. Disturbance coverage in those four years should therefore be used with caution. As in Guindon et al (2014), the method was designed to minimize commission errors and has a disturbance class attribution success rate of about 98%. The attribution success rate of disturbance year for fire is of about 69% for the exact year and of about 99% when attribution to the following year is also considered as a success. This common one-year lag is mostly due to the use of mid-summer Landsat mosaics for the analysis that will cause spring and fall events of the same year to be attributed to successive years. For example, a fire that occurred in the fall of 2004 (after July and August), will be detected and attributed to 2005, while for a fire that occurred in the spring of 2004 will be detected and attributed to 2004. The presence of clouds and shadows or image availability causes 10% of missing data annually and therefore can too delay the capture of events. The data provides uniform spatial and temporal information on fire and harvest across all provinces and territories of Canada and is intended for strategic-level analysis. Since no attention was given to other minor disturbances such as mining, road or flooding, the product should not be used for their identification. Finally, calibration datasets were developed for only three major forest pests (mountain pine beetle, eastern spruce budworm and forest tent caterpillar), but were folded within the “no-change” class in order to minimize commission errors for fire and harvest . Less common pests for which validation datasets are hard to develop were not considered and therefore could in rare circumstances generate false fire events. Considering that area having two (3.3%) to three disturbances (less than 1%) events are not common, only the most recent disturbance is provided, overlapping older disturbances in these rare case. ## Please cite this dataset as: Guindon, L., P. Villemaire, R. St-Amant, P.Y. Bernier, A. Beaudoin, F. Caron, M. Bonucelli and H. Dorion. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad ## Scientific article citation: The creation, validation and limitations of the CanLaD product are described in the Supplementary Material file associated with the following article: Guindon, L.; Bernier, P.Y.; Gauthier, S.; Stinson, G.; Villemaire, P.; Beaudoin, A. 2018. Missing forest cover gains in boreal forests explained. Ecosphere, 9 (1) Article e02094. doi:10.1002/ecs2.2094. ## Cited references: Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.