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 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 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.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. NDVI generation spike difference: There is a spike difference in HLSL30 and HLSS30 when generating NDVI index from granules after 2021 which was resolved with the integration of Landsat 9 in January 2023; however, it was not back processed. The HLS team is aware of this issue and is currently working on a fix. * 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. 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

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.

The dataset consist of three categories; image subsets, burned area masks and quicklooks. The image subsets are derived from Landsat-8 scenes taken during the years 2019 and 2021. Each image has a size of 512x512 pixels and consists of 8 multispectral. The sequence of band names from band 1 to band 7 of the image subset is same as the sequence of band names of landsat-8 scene, except for band 8 of the image subset which is band 9 (cirrus band) in the original landsat-8 scene. The image subsets are saved in GeoTIFF file format with the latitude longitude coordinate system and WGS 1984 as the datum. The spatial resolution of image subsets is 0.00025 degree and the pixel values are stored in 16 bit unsigned integer with the range of value from 0 to 65535. The total of the dataset is 227 images which containing object of burned area surrounded by various ecological diversity backgrounds such as forest, shrub, grassland, waterbody, bare land, settlement, cloud and cloud shadow. In some cases, there are some image subsets with the burned areas covered by smoke due to the fire is still active. Some image subsets also overlap each other to cover the area of burned scar which the area is too large. The burned area mask is a binary annotation image which consists of two classes; burned area as the foreground and non-burned area as the background. These binary images are saved in 8 bit unsigned integer where the burned area is indicated by the pixel value of 1, whereas the non-burned area is indicated by 0. The burned area masks in this dataset contain only burned scars and are not contaminated with thick clouds, shadows, and vegetation. Among 227 images, 206 images contain burned areas whereas 21 images contain only background. The highest number of images in this dataset is dominated by images with coverage percentage of burned area between 0 and 10 percent. Our dataset also provides quicklook image as a quick preview of image subset. It offers a fast and full size preview of image subset without opening the file using any GIS software. The quicklook images can also be used for training and evaluating the model as a substitute of image subsets. The image size is 512x512 pixels same as the size of image subset and annotation image. It consists of three bands as a false color composite quicklook images, with combination of band 7 (SWIR-2), band 5 (NIR), and band 4 (red). These RGB composite images have been performed contrast stretching to enhance the images visualizations. The quicklook images are stored in GeoTIFF file format with 8 bit unsigned integer.

This work was financed by Riset Inovatif Produktif (RISPRO) fund through Prioritas Riset Nasional (PRN) project, grant no. 255/E1/PRN/2020 for 2020 - 2021 contract period.

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.

• NDVI generation spike difference: There is a spike difference in HLSL30 and HLSS30 when generating NDVI index from granules after 2021 which was resolved with the integration of Landsat 9 in January 2023; however, it was not back processed. The HLS team is aware of this issue and is currently working on a fix.

• 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

The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.

Harmonized Landsat satellite image mosaic timeseries

The purpose of this data is to provide harmonized dataset of yearly satellite image mosaics based on Landsat imagery and starting from year 1984. Both yearly reflectance mosaics of visible, near and shortwave infrared bands, as well as several image index mosaics like Normalized Difference Vegetation Index are provided. So far, years 1984 - 2024 are processed; years 1984 – 2014 are based on Landsat-5 Thematic Mapper (launched 1.3.1984, decommissioned 5.6.2013) and Landsat-7 Enhanced Thematic Mapper Plus (launched 15.4.1999, scan line corrector failure 31.5.2003, imaging suspended 19.1.2024), and years 2013 – 2024 based on Landsat-8 (launched 11.2.2013) and Landsat-9 (launched 27.9.2021) Operational Land Imager imagery.

The USGS provides several differently processed Landsat datasets; Landsat Collection-2 Level-2 imagery has been used in this timeseries. These images provide surface reflectance estimates combined with the best globally available geometric orthocorrection for Landsat imagery. Mosaics have been done using visible B1-B4, near B5 and shortwave infrared B6 & B7 bands (numbering of bands is based Landsat-9 Operational Land Imager bands, wavelengths in micrometers):

• B1: 0.433-0.453
• B2: 0.450-0.515
• B3: 0.525-0.600
• B4: 0.630-0.680
• B5: 0.845-0.885
• B6: 1.560-1.660
• B7: 2.100-2.300

The number of bands and their numbering varies between instruments; Landsat-8 OLI and -9 OLI-2 has additional blue band B1 that Landsat-7 ETM+ and Landsat-5 TM do not have. Also, the widths of bands are a bit different in different instruments, the details of band numbering and widths can be found from following WWW-pages:

• Landsat-9 OLI-2: https://landsat.gsfc.nasa.gov/satellites/landsat-9/landsat-9-instruments/landsat-9-spectral-specifications/
• Landsat-8 OLI: https://landsat.gsfc.nasa.gov/satellites/landsat-8/
• Landsat-7 ETM+: https://www.usgs.gov/landsat-missions/landsat-7
• Landsat-5 TM: https://www.usgs.gov/landsat-missions/landsat-5

Individual images have been downloaded to National Satellite Data Centre (NSDC: https://nsdc.fmi.fi/). The mosaicking was performed using CalFin-processing cluster at NSDC. The cloud masks provided by images (based on CFmask v3.3.1) and some other checks were used to select valid values for mosaicking. In case of Landsat-5 and -7, pixel has been selected for processing if it was outside masks cloud, cloud shadow, fill and cloud and cloud shadow confidences are low, distance to cloud was greater than 0.6 km, NDVI was between -1 and 1, and surface reflectance of bands more than 0.005. In case of Landsat-8 and -9 there were some small changes; pixel was selected for processing if it was outside masks cloud, cloud_shadow, cirrus, dillated_cloud and designated_fill, if distance to cloud was greater than 1 km and surface reflectance of bands more than 0.005.

The time window used to select observations for mosaicking varied in different parts of Finland, because growing season length is different. The time windows and areas were:

• South (lat. 59-64 deg.): 1.5.-30.9.
• Central (lat. 64-68 deg.): 15.5.-15.9.
• North (lat. 68-70 deg.): 1.6.-31.8.

The mosaicking was based on 95% percentile of NDVI-values. This was used as an attempt to avoid shadow areas in final mosaic.

Postprocessing GDAL/Python-script merged parts together and made coordinate transformation to TM35Fin-coordinate system (EPSG 3067) with 20 meter pixel size. Area of mosaics is (TM35Fin, EPSG 3067):

• ULE: 50000
• ULN: 7800000
• LRE: 842000
• LRN: 6600000

Following image indices have been computed:

• Normalized Difference Moisture Index NDMI = ( TM_B4 – TM_B5 ) / ( TM_B4 + TM_B5 ) = ( OLI_B5 – OLI_B6 ) / ( OLI_B5 + OLI_B6 )
• Normalized Difference Tillage Index NDTI = ( TM_B5 – TM_B7 ) / ( TM_B5 + TM_B7 ) = ( OLI_B6 – OLI_B7 ) / ( OLI_B6 + OLI_B7 )
• Normalized Difference Vegetation Index NDVI = ( TM_B4 – TM_B3 ) / ( TM_B4 + TM_B3 ) = ( OLI_B5 – OLI_B4 ) / ( OLI_B5 + OLI_B4 )
• Normalized Difference Water Index NDWI = ( TM_B2 – TM_B4 ) / ( TM_B2 + TM_B4 ) = ( OLI_B3 – OLI_B5 ) / ( OLI_B3 + OLI_B5 )
• TM_Bx: Landsat-5 TM and -7 ETM+ band x
• OLI_Bx: Landsat-8 OLI and -9 OLI-2 band x

Other indices can be computed by downloading individual bands and making the computations by him/herself.

Other published imagery consists of

• META: The pixel value is the number of day of selected observation.
• NUMOBS: The pixel value is the number of available valid observations. NOTE: The common area of succeeding images from same day is counted twice.

The mosaics are available by downloading from the archive of the National Satellite Data Centre (https://nsdc.fmi.fi/):

Reflectance band mosaics of Landsat-5 and -7: https://pta.data.lit.fmi.fi/lans/landsat57/bands/landsat57_refl_bn_year.tif

• bn: b1, b2, b3, b4, b5 or b7
• year: between 1984 - 2014

Index mosaics of Landsat-5 and -7: https://pta.data.lit.fmi.fi/lans/landsat57/index/landsat57_ind_year.tif

• ind: ndmi, ndti, ndvi, ndwi
• year: between 1984 - 2014

Metadata mosaics of Landsat-5 and -7:

• https://pta.data.lit.fmi.fi/lans/landsat57/meta/landsat57_numobs_year.tif
• https://pta.data.lit.fmi.fi/lans/landsat57/meta/landsat57_refl_meta_year.tif
• year: between 1984 - 2014

Reflectance band mosaics of Landsat-8 and -9: https://pta.data.lit.fmi.fi/lans/landsat89/bands/landsat89_refl_bn_year.tif

• bn: b1, b2, b3, b4, b5, b6 or b7
• year: between 2013 - 2024

Index mosaics of Landsat-8 and -9: https://pta.data.lit.fmi.fi/lans/landsat89/bands/landsat89_ind_year.tif

• ind: ndmi, ndti, ndvi, ndwi
• year: between 2013 - 2024

Metadata mosaics of Landsat-8 and -9:

• https://pta.data.lit.fmi.fi/lans/landsat89/bands/landsat89_numobs_year.tif
• https://pta.data.lit.fmi.fi/lans/landsat89/bands/landsat89_refl_meta_year.tif
• year: between 2013 - 2024

The drawbacks of this timeseries are that there are gaps of data due to lack of images, and there are atmospheric effects still visible in made mosaics like clouds that the cloud masking has not been detected, or cloud shadow areas. It was noticed that the NDVI of cloud shadow areas can be higher than neighboring shadowless areas, leading to situations that shadow areas are selected instead of shadowless areas into final mosaic. It seems that the atmospheric correction corrects visible bands, especially red, too much leading to too low reflectance of red band and therefore increased NDVI. Also, water areas can have negative reflectance, and these have been treated as outliers and removed from mosaicking process.

Reference: Landsat Collection 2 Level-2 Science Products, https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products

Original Landsat satellite images by USGS, processing by SYKE. This SYKE’s dataset can be used according to open data license (CC BY 4.0).

Suomeksi:

Harmonisoitu Landsat satelliittikuvamosaiikkiaikasarja

Tämän datasetti käsittää Landsat-satelliittisarjan kuviin perustuvia vuosittaisia kuvamosaiikkeja alkaen vuodesta 1984. Vuosittainen data koostuu näkyvän valon, lähi- ja keski-infrapuna-alueen kanavien maanpinnan reflektanssin mosaiikeista, sekä muutamasta kuvaindeksimosaiikista kuten NDVI (Normalized Difference Vegetation Index). Tällä hetkellä (24.10.2024), vuodet 1984-2024 on prosessoitu; vuosien 1984-2014 mosaiikit perustuvat Landsat-5 Thematic Mapper (laukaistu kiertoradalle 1.3.1984, käyttö lopetettu 5.6.2013) ja Landsat-7 Thematic Mapper Plus (laukaistu kiertoradalle 15.4.1999, keilauslinjan korjaaja rikkoutui 31.5.2003, käyttö lopetettu 19.1.2024) kuviin, ja vuosien 2013-2024 mosaiikit perustuvat Landsat-8 Operational Land Imager (laukaistu 11.2.2013) ja Landsat-9 Operational Land Imager-2 (laukaistu 27.9.2021) kuviin.

Yhdysvaltojen Geologian tutkimuslaitos USGS hallinnoi Landsat-ohjelmaa ja hoitaa kuvien jakelun. He tuottavat eri lailla prosessoituja datasettejä, joista Landsat Collection-2 Level-2 datasettiä on käytetty tässä aikasarjassa. Näiden kuvien pikselien arvot ovat maanpinnan reflektanssin estimaatteja, eli kuville on tehty ilmakehäkorjaus. Kuville on tehty orto-oikaisu, eli maaston pinnanmuotojen vaikutus pikselien paikkaan on huomioitu. Kuvamosaiikit on tehty näkyvän valon B1-B4, lähi-infrapuna- B5 ja keski-infrapunakanaville B6 ja B7 (numerointi perustuu Landsat-9 Operational Land Imager-2 instrumentin kanavien numerointiin, aallonpituudet on ilmaistu mikrometreinä)

• B1: 0.433-0.453
• B2: 0.450-0.515
• B3: 0.525-0.600
• B4: 0.630-0.680
• B5: 0.845-0.885
• B6: 1.560-1.660
• B7: 2.100-2.300

Kanavien lukumäärä ja numerointi vaihtelee instrumenttien välillä; Landsat-8 OLI ja -9 OLI-2 instrumenteissa on sinisen aallonpituuden kanava B1 jota ei ole Landsat-7 ETM+ tai Landsat-5 TM-instrumenteissa. Lisäksi, kanavien aallonpituudet eri instrumenteissa vaihtelevat hieman. Lisätietoja eri instrumenttien kanavien aallonpituuksista ja numeroinnista löytyy seuraavilta WWW-sivuilta:

• Landsat-9 OLI-2: https://landsat.gsfc.nasa.gov/satellites/landsat-9/landsat-9-instruments/landsat-9-spectral-specifications/
• Landsat-8 OLI: https://landsat.gsfc.nasa.gov/satellites/landsat-8/
• Landsat-7 ETM+: https://www.usgs.gov/landsat-missions/landsat-7
• Landsat-5 TM: https://www.usgs.gov/landsat-missions/landsat-5

Yksittäiset Landsat-kuvat on ladattu Ilmatieteen laitoksen hallinnoiman Kansallisen Satelliittidatakeskuksen (NSDC) arkistoon. Varsinainen mosaiikkien tuotanto tehdään CalFin-prosessointiklusterilla. Pilvimaskaukseen käytetään kuvien mukana tulevia pilvimaskeja (menetelmä CFmask v3.3.1) ja näiden lisäksi käyttökelpoisten havaintojen valintaan käytetään myös muita testejä. Landsat-5 ja -7 kuvien tapauksessa pikseli valitaan mosaikointiin, mikäli se ei kuulu maskien cloud, cloud shadow ja fill alueeseen, pilven ja pilvivarjon todennäköisyys on alhainen, etäisyys pilven reunaan on yli 0.6 km, NDVI on vöälillä -1 ja 1, sekä

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2B Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration.The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document for a more detailed description of the individual layers provided in the DIST-ALERT product.

Grasslands provide a wide range of important ecosystem services. Mapping and assessing the status and use intensity of grasslands is thus important for environmental monitoring. We here provide maps with detected mowing events, as a proxy for grassland use intensity, for grassland areas across Germany for the year 2022.

The dataset contains maps of grassland mowing activity in Germany, which have been produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire grassland area, i.e. permanent grassland, potentially permanent grassland (e.g. fodder crops) and other extensive areas. They are derived from dense time series of Sentinel-2, Landsat 8 (and 9) data. Map production is based on the methods described in Schwieder et al. (2022). The algorithm used to derive the maps is available as a user-defined function for the FORCE environment (Frantz, D., 2019).

The dataset includes seven layers: (1) the number of detected mowing events, (2) the day of year (DOY) of the first to sixth detected mowing event. Ancillary data layers are available on request. The maps include all areas that have at least once been classified as permanent grassland, cultivated grassland or fallow in the maps of agricultural land use between 2017 and 2021 that are provided by Thünen Institute. Please consider to use the respective annual agricultural land use map or any other data source to generate a mask for your purpose.

We provide this dataset "as is" without any warranty regarding the quality or completeness and exclude all liability. Please refer to Schwieder et al. (2022) for the related accuracy assessment and potential limitations and / or contact the authors directly.

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed.

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References

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795.

_Grassland mowing events across Germany © 2022 by Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

This data publication contains a set of files in which different variables related to fire burned severity (Canada Landsat Burned Severity, CanLaBS) were computed for all events in Canada between 1985 and 2015 as detected by the Canada Landsat Disturbance (CanLaD (Guindon et al. 2017 and 2018) product. Details on the creation of this product are available in Guindon et al. 2020 (https://doi.org/10.1139/cjfr-2020-0353) and in supplementary materials accompanying the publication. The current document is therefore a complement to the article and supplementary materials. The supplementary materials are referenced in the publication (cjfr-2020-0353suppla, cjfr-2020-0353supplb etc.). This is the first Canada-wide product that aims to promote nationwide research on fire severity by making available the data used in the article. The data is in the form of grids composed of pixels at a resolution of 30m. To simplify the distribution and manipulation of the data and considering that two or three fire occurrences within a given location is rare (respectively 2.3% and less than 0.01%), only the most recent fire data are considered in the final product. For these very rare cases, from 2015 to 1985, the most recent burned areas overlap the older data. Overlapping fire count can be found in layer “CanLaBS_Nbdisturb_v0”, multiple fire events in same areas have values equal to or greater than two. Landsat radiometric values for calculating the NBR index were derived from summer Landsat mosaics (July and August), for years 1984 to 2015 (Guindon et al. 2018). These mosaics were developed from individual USGS Landsat scenes with surface reflectance correction (Masek et al., 2006; Vermote et al., 2006). For each annual compound, the pixel with the less atmospheric opacity was selected. An algorithm was also developed to remove clouds that were not detected by the cloud masks provided with the USGS data. Here is a general description of the layers provided and a more technical description can be found in Table 1 (see "Ressources" section below): 1. NBR and dNBR. All these values are multiplied by 1000. The value of dNBR represents the value obtained for NBRpre - NBRpost. It is calculated for each pixel that was classified as a fire in CanLaD, according to the corrected year (see cjfr-2020-0353suppla). 2. Year of fire. The fire years detected in CanLaD (Guindon et al. 2018) was corrected using different fire databases, this layer contains the correct year. (see cjfr-2020-0353suppla) 3. Julian Days of the Fire, based on various high-resolution products. However, this variable is only available from 1989 onwards. 4. Presence of salvage logging one year after the fire. Classification of satellite images detecting scarified soils (see cjfr-2020-0353suppld). 5. Pre-fire forest attributes: Pre-fire forest attributes values were calculated for median mosaics, from 1985 to 2000. These attributes values were derived from NFI (national forest inventory) photo-plot attributes and were spatialized. Pre-fire attribute values were created to stratify the analyses (see cjfr-2020-0353supplc). The predicted variables are as follows: • Canopy density in percent. • Predicted living biomass in tonnes per hectare. • Percentage coniferous biomass proportion of total biomass. • Percentage hardwood biomass proportion of total biomass. • Percentage unknown species biomass proportion of total biomass. Note, as unknown species are found especially in northern areas, they are considered coniferous for the purpose of the article. 6. Missing remote sensing data, one year after the fire. The estimation of burned severity needs NBR data (NBRpost) in the next year after fire occurrences. NBRpost is available for 91% of the cases, but for the remaining 9%, no data were available due to the presence of clouds. For these cases, satellite data from the years following the fire were used with a regression radiometry correction. This gives values to missing data for year following the fire. This layer flags the areas that have derived data. The values of 1= one year after the fire (no regression), 2= two years after the fire (regression), 3= three years after the fire (regression) and 4= four years after the fire (no regression, set as missing data). (see cjfr-2020-0353supplb). 7. Areas with more than one fire disturbance between 1985 and 2015 (1=one single disturbance, 2=two or more, 3=three or more). ## Data citation: 1. Guindon, L., Villemaire P., Manka F., Dorion H. , Skakun R., St-Amant R., Gauthier S. : Canada Landsat Burned Severity (CanLaBS): a Canada-wide Landsat-based 30-m resolution product of burned severity since 1985 https://doi.org/10.23687/b1f61b7e-4ba6-4244-bc79-c1174f2f92cd 2. The creation, the validation and the limits of the CanLaBS product are describe in the text and supplementary material: Guindon, L., Gauthier, S., Manka, F., Parisien, MA, Whitman, E., Bernier, P., Beaudoin, A., Villemaire P., Skakun R. Trends in wildfire burn severity across Canada, 1985 to 2015 https://doi.org/10.1139/cjfr-2020-0353 ## References cited: 1. Guindon, L., Villemaire, P., St-Amant, R., Bernier, P.Y., Beaudoin, A., Caron, F., Bonucelli, M., and Dorion, H. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad 2. Guindon, L., Bernier, P., Gauthier, S., Stinson, G., Villemaire, P., & Beaudoin, A. (2018). Missing forest cover gains in boreal forests explained. Ecosphere, 9(1), e02094. https://doi.org//10.1002/ecs2.2094 3. 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. 4. 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.

[FI] Itämeren alueen ja erityisesti Suomea ympäröivien merialueiden pintalevälauttoja kuvaava neliluokkainen tulkintakartta. Tulkintakartta kuvaa todennäköisyyttä, että tietyllä alueella esiintyy pintalevää. Kartan luokat ovat 1) ei pintalevää, 2) mahdollista, 3) todennäköistä sekä 4) varmaa pintalevää. Pintalevää ei kyetä havainnoimaan optisilla satelliitti-instrumeneilla pilvien läpi, joten pintaleväkartta puuttuu pilvisiltä alueilta.

Tulkintoja tehdään kesäkuun lopulta syyskuun alkuun, tarvittaessa pidempäänkin. Pääasiassa levälauttoja esiintyy eniten heinä-elokuussa, joten kyseiseltä ajanjaksolta pintalevähavaintojen määrä on suurin. Tulkintamenetelmä perustuu satelliitin eri aallonpituusalueiden havaitseman heijastuksen voimakkuuteen, joka on sinileväalueilla erilainen kuin levättömillä vesialueilla. Yksittäisten pikselien alueelta tehty pintalevätulkinta yleistetään karkeammaksi, jotta kartalla näkyvät leväalueet ovat selkeämmät. Kartta-alueelta poistetaan pilviset alueet automaattisella pilventunnistusmenetelmällä, jota täydennetään tarvittaessa ennen julkaisua myös manuaalisesti.

Tulkintamenetelmä on kehitetty Sykessä ja se hyödyntää kolmen eri satelliitti-instrumentin havaintoja. Tämä aineisto koostuu NASAn Landsat-8 -satelliitin OLI-instrumentin havainnoista vuodesta 2017 lähtien sekä Landsat-9 -satelliitin OLI-2 -instrumentin havainnoista vuodesta 2022 lähtien. Tulkinta tehdään 60 m tarkkuudella, joten tulkinta voidaan ulottaa saaristoalueille ja rantojen läheisille alueille. Näin tarkkoja havaintoja saadaan eri merialueilta muutaman päivän välein, mutta suurempi osa havainnoista tehdään Sentinel-2 sarjan MSI-instrumenttien havainnoista (erillinen metadata).

Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0).

[EN] A four-class interpretation map describing surface floating algae blooms in the Baltic Sea region and especially in the sea areas surrounding Finland. The map describes the probability that algae accumulates in the surface layer of the sea in a particular area. The map categories are 1) no surface algae, 2) possible, 3) probable, and 4) certain surface algae. Surface algae cannot be detected by optical satellite instruments through clouds, so cloudy areas are not included in the map.

Interpretations made annually from late June to early September, and longer if necessary. Algae rafts mainly occur in July-August, so the number of surface algae observations is the highest during that period. The interpretation method is based on the intensity of the reflection detected by the different wavelength ranges of the satellite, which is different in cyanobacterial areas than in areas without cyanobacteria. A surface algae estimation by individual pixels is generalized so that the areas of algae displayed on the map are clearer. Cloudy areas are removed from the map area by an automatic cloud detection method, which is also completed manually before publication, if necessary.

The interpretation method has been developed at Syke and utilizes the observations of three different satellite instruments. This data is based on observations from NASA's Landsat-8 satellite OLI instrument starting from year 2017 and OLI-2 instrument of the NASA's Landsat-9 satellite starting from year 2022. The interpretation is made with an spatial resolution of 60 m, so the interpretation can be extended to archipelago areas and areas close to the shores. Thus, accurate observations are obtained from different sea areas every few days, but the greater part of the observations are made from observations of Sentinel-2 series MSI instruments (separate metadata).

WMS-palvelin / WMS service endpoint: https://geoserver2.ymparisto.fi/geoserver/eo/wms

WMS-taso / WMS layer: EO_HR_WQ_LC8_ALGAE

Kaukokartoitusseurantojen tuloksena syntynyt levälauttatulkinta, joka pohjautuu Landsat-8 OLI instumentin ja Landsat-9 OLI-2 instumentin satelliittihavaintoihin. Vuodesta 2017 eteenpäin koostuva päivittäinen levälautta-aineisto.

Prosessointihistoria: Levälautat on tulkittu Landsat-8 OLI satelliitti-instrumentin ja Landsat-9 OLI-2 satelliitti-instrumentin aineistoilta. Alkuperäinen satelliittidata on ladattu USGS/NASA latauspalveluista. Sykessä niistä on laskettu levätulkinnat.

LANDFIRE's Annual Disturbance products track how landscapes change across space and time on an annual basis. The Annual Disturbance (Dist) product identifies satellite-detected areas larger than 4.5 hectares (11 acres) that underwent natural or human-caused changes within a specific year (for Dist23, October 1, 2022 – September 30, 2023), or represent fire activity/field treatments as small as 80 square meters. While creating the Annual Disturbance product a variety of data sources are leveraged. 1) National fire mapping programs: This includes information from Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), which offer severity information for fire-caused disturbances. 2) Agency-reported events: There are 18 designated classes for contributed polygon "Event" types such as disease, insects, development, harvest, etc. that are reported by government agencies for inclusion into the disturbance product. 3) Remotely sensed imagery: Harmonized Landsat Sentinel (HLS) satellite images offer a comprehensive-uninterrupted view of the landscape covering all lands, public and private, to fill in the gaps inherent in the previous data sources. These data are reviewed and edited by a team of image analysts to ensure and maintain high quality standards. To create the LF Annual Disturbance product, individual Landsat scenes are stacked and made into composites representing the 15th, 50th, and 90th percentiles of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year and the two prior years serves as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are most commonly caused by differences in annual or seasonal phenology, artifacts in the image composites, or difficult to map classes such as wetlands and grasses. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from modeling are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed as fire by using Burned Area (BA) Level-3 science products derived from Landsat 8 and 9. BA data is only available in the lower 48 states (CONUS). Causality information assigned to annual disturbance products are prioritized by source, with the highest priorities reserved for fire mapping program data (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, satellite image-based change. Severity is assigned directly from fire program data. For events and satellite-detected change, severity is derived from pre- and post-burn standard deviation values of the differenced Normalized Burn Ratio (dNBR). When mapping the LF Annual Disturbance product, the start date is utilized for disturbances from fire program data whereas all other disturbances utilize the end date.

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) provisional data product Version 0 maps vegetation disturbance alerts from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). Vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes to provide information about more general disturbance trends. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration, or small magnitude/long duration.

The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within in the DIST-ALERT product: vegetation disturbance status, current vegetation cover indicator, current vegetation anomaly value, historical vegetation cover indicator, max vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, and vegetation disturbance duration. See the Product Specification for a more detailed description of the individual layers provided in the DIST-ALERT product.

Known Issues

• Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).