Details for Harvest Job Id: 5fb67e50-313f-4f93-8a65-f594bd14b844
Job Info
| Harvest Source: | nasa-data-json |
| status: | complete |
| job_type: | harvest |
| date_created: | 2025-10-31 23:53:03.709393 |
| date_finished: | 2025-11-01 01:27:11.179423 |
| records_total: | 27051 |
| records_added: | 19 |
| records_updated: | 769 |
| records_deleted: | 0 |
| records_errored: | 13 |
| records_unchanged: | 26250 |
| records_validated: | 788 |
| id: | 5fb67e50-313f-4f93-8a65-f594bd14b844 |
Job Error Table
No job errors foundRecord Error Details
| Error type | Number of errors |
| ValidationError | 13 |
74afa068-8de3-4cf4-abf1-c1a103d0f5bc
Identifier: 10.5067/HLS/HLSS30.002
Title: HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0
Harvest Record ID: 2294b39c-cbe4-4f0d-8682-12e13465848d
Error Message:
- <ValidationError: "$.description, '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](https://doi.org/10.5067/HLS/HLSL30.002) products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) 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](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)); 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 year 2016 in UTC, but the time was 10:15:52 of day 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 was acquired in the next orbit in eastern Australia. This issue also occurs for Landsat. For example, HLS.L30.T59HQS.2016117T221209.v2.0 was acquired on the same day as the first S30 example given above, but both on day 118 of 2016 locally. Adding to the confusion for L30, in the same region, Landsat 8 and 9 can each overpass once in one of the two adjacent WRS-2 Paths (91/92/93) over a two-day period on a local calendar, but based on UTC time, the two overpasses can appear to be on the same day. For example, in the following seemingly same-day pair, the second L30 is actually for day 168 locally: HLS.L30.T55GCN.2023167T000407.v2.0 HLS.L30.T55GCN.2023167T235747.v2.0 Bear in mind, the date peculiarity for the data occurs when the overpass time is during the late hours of a UTC day. * The atmospheric ancillary data from the wrong date was used for LaSRC: Related to the above, for eastern Australia and New Zealand, L30 and S30 surface reflectance on certain days was created using the atmospheric ancillary data from a date that was one day too early. The exact geographic extent of the affected HLS products and the impact on the surface reflectance quality are under investigation. Practice caution when using data with overpass times during the late hours of a UTC day.* Duplicates in L30: The Landsat 9 acquisitions from October 2021 to March 2023 in Landsat Collection 2 were reprocessed by USGS in March 2023. This reprocessing updated the overpass time by a fraction of a second for some scenes. Since HLS uses overpass time as part of the L30 filename, the older L30 granules were not automatically overwritten due to the different filenames. For example, the first L30 granule in the following pair originated from an older version of L1TP of Landsat 9 with the second granule originating from the reprocessed version. HLS.L30.T11SLC.2022166T182646.v2.0 HLS.L30.T11SLC.2022166T182645.v2.0 There are other causes of duplicate L30 granules, but the overall number of duplicates is very small.* Poor Geolocation: A large amount of granules that were processed for May through July 2023 were created with L1GT input scenes which were deemed undesirable due to a poor geolocation issue. These granules were removed from the archive. (see the full list of removed [granules](https://lpdaac.usgs.gov/documents/2161/L30_L1GT_granules_May_July_2023.csv))Improvements/Changes from Previous Versions* Aerosol QA bits from the USGS Land Surface Reflectance Code (LaSRC) model output have been added into the Function of Mask (Fmask) data layer. The added two bits indicate the aerosol levels: high, medium, low, and climatology aerosol.' does not match any of the acceptable formats: max string length requirement">
Type: ValidationError
Date Created: 2025-11-01 00:27:51.939941
4645f9c4-7b95-4acc-9e63-45957e1e9a83
Identifier: C1214343609-ASF
Title: UAVSAR_POLSAR_ML_COMPLEX_SLANT
Harvest Record ID: 2edb8d7a-0bb9-4844-9b42-888d6c65fede
Error Message:
- <ValidationError: "$.theme[0], '' does not match any of the acceptable formats: non-empty">
Type: ValidationError
Date Created: 2025-11-01 01:25:56.508659
aa02a295-9da1-437c-b866-10fe54519747
Identifier: C1214354235-ASF
Title: UAVSAR_POLSAR_ML_COMPLEX_GRD_5X5
Harvest Record ID: a17a35b4-358a-4558-a9aa-177cfbb395b6
Error Message:
- <ValidationError: "$.theme[0], '' does not match any of the acceptable formats: non-empty">
Type: ValidationError
Date Created: 2025-11-01 01:24:43.079927
ad165665-cc26-4a7c-a32d-73639fed308a
Identifier: C1214337770-ASF
Title: UAVSAR_POLSAR_ML_COMPLEX_GRD
Harvest Record ID: da1a7971-e278-43a9-aa80-d129622cbfc5
Error Message:
- <ValidationError: "$.theme[0], '' does not match any of the acceptable formats: non-empty">
Type: ValidationError
Date Created: 2025-11-01 01:21:57.573545
87d2e16b-6842-4ed8-b49a-5006d3c215a8
Identifier: C1214353593-ASF
Title: UAVSAR_POLSAR_DEM
Harvest Record ID: cf20ed37-96cd-499b-ac37-0ae5403afcfb
Error Message:
- <ValidationError: "$.theme[0], '' does not match any of the acceptable formats: non-empty">
Type: ValidationError
Date Created: 2025-11-01 01:23:14.897893
40dfaeed-e7a7-4457-a20e-31a4fa0366f4
Identifier: 10.5067/HLS/HLSL30.002
Title: HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0
Harvest Record ID: 1c59bb28-390f-49a2-bf26-9dd8bd6b75f9
Error Message:
- <ValidationError: "$.description, 'The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 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](https://doi.org/10.5067/HLS/HLSS30.002) and HLSL30 products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) 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](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)); 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.* 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 year 2016 in UTC, but the time was 10:15:52 of day 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 was acquired in the next orbit in eastern Australia. This issue also occurs for Landsat. For example, HLS.L30.T59HQS.2016117T221209.v2.0 was acquired on the same day as the first S30 example given above, but both on day 118 of 2016 locally. Adding to the confusion for L30, in the same region, Landsat 8 and 9 can each overpass once in one of the two adjacent WRS-2 Paths (91/92/93) over a two-day period on a local calendar, but based on UTC time, the two overpasses can appear to be on the same day. For example, in the following seemingly same-day pair, the second L30 is actually for day 168 locally: HLS.L30.T55GCN.2023167T000407.v2.0 HLS.L30.T55GCN.2023167T235747.v2.0 Bear in mind, the date peculiarity for the data occurs when the overpass time is during the late hours of a UTC day. * The atmospheric ancillary data from the wrong date was used for LaSRC: Related to the above, for eastern Australia and New Zealand, L30 and S30 surface reflectance on certain days was created using the atmospheric ancillary data from a date that was one day too early. The exact geographic extent of the affected HLS products and the impact on the surface reflectance quality are under investigation. Practice caution when using data with overpass times during the late hours of a UTC day.* Duplicates in L30: The Landsat 9 acquisitions from October 2021 to March 2023 in Landsat Collection 2 were reprocessed by USGS in March 2023. This reprocessing updated the overpass time by a fraction of a second for some scenes. Since HLS uses overpass time as part of the L30 filename, the older L30 granules were not automatically overwritten due to the different filenames. For example, the first L30 granule in the following pair originated from an older version of L1TP of Landsat 9 with the second granule originating from the reprocessed version. HLS.L30.T11SLC.2022166T182646.v2.0 HLS.L30.T11SLC.2022166T182645.v2.0 There are other causes of duplicate L30 granules, but the overall number of duplicates is very small.* Poor Geolocation: A large amount of granules that were processed for May through July 2023 were created with L1GT input scenes which were deemed undesirable due to a poor geolocation issue. These granules were removed from the archive. (see the full list of removed [granules](https://lpdaac.usgs.gov/documents/2161/L30_L1GT_granules_May_July_2023.csv))Improvements/Changes from Previous Versions* Aerosol QA bits from the USGS Land Surface Reflectance Code (LaSRC) model output have been added into the Function of Mask (Fmask) data layer. The added two bits indicate the aerosol levels: high, medium, low, and climatology aerosol.' does not match any of the acceptable formats: max string length requirement">
Type: ValidationError
Date Created: 2025-11-01 00:27:52.073308
93375e43-e111-441b-8d13-c309751a09a4
Identifier: C1214353986-ASF
Title: UAVSAR_POLSAR_METADATA
Harvest Record ID: 5225cfa5-33a0-44de-bf1e-713900aa23d8
Error Message:
- <ValidationError: "$.theme[0], '' does not match any of the acceptable formats: non-empty">
Type: ValidationError
Date Created: 2025-11-01 01:25:02.535676
e5ef1ca9-59c9-456c-b410-181ef37bc0cc
Identifier: C1214354031-ASF
Title: UAVSAR_POLSAR_PAULI
Harvest Record ID: 98bb502c-f215-4d1c-b0fe-cfd05da0d250
Error Message:
- <ValidationError: "$.theme[0], '' does not match any of the acceptable formats: non-empty">
Type: ValidationError
Date Created: 2025-11-01 01:25:58.939132
3ceeab09-c27a-4da1-948c-ea32398937d1
Identifier: ivo://nasa.heasarc/fermigbrst
Title: Fermi GBM Burst Catalog
Harvest Record ID: e4e4dbd3-aaf5-43fe-b89e-7ce6af6a6968
Error Message:
- <ValidationError: '$.description, \'When referencing results from this online catalog, please cite <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ab7a18">von Kienlin, A. et al. 2020</a>, <a href="http://iopscience.iop.org/0067-0049/211/1/12/">Gruber, D. et al. 2014</a>, <a href="http://iopscience.iop.org/0067-0049/211/1/13/">von Kienlin, A. et al. 2014</a>, and <a href="http://iopscience.iop.org/article/10.3847/0067-0049/223/2/28/">Bhat, P. et al. 2016</a>. This table lists all of the triggers observed by a subset of the 14 GBM detectors (12 NaI and 2 BGO) which have been classified as gamma-ray bursts (GRBs). Note that there are two Browse catalogs resulting from GBM triggers. All GBM triggers are entered in the <a href="/W3Browse/fermi/fermigtrig.html">Fermi GBM Trigger Catalog</a>, while only those triggers classified as bursts are entered in the Burst Catalog. Thus, a burst will be found in both the Trigger and Burst Catalogs. The Burst Catalog analysis requires human intervention; therefore, GRBs will be entered in the Trigger Catalog before the Burst Catalog. The latency requirements are 1 day for triggers and 3 days for bursts. There are four fewer bursts in the online catalog than in the Gruber et al. 2014 paper. The four missing events (081007224, 091013989, 091022752, and 091208623) have not been classified with certainty as GRBs and are not included in the general GRB catalog. This classification may be revised at a later stage. The GBM consists of an array of 12 sodium iodide (NaI) detectors which cover the lower end of the energy range up to 1 MeV. The GBM triggers off of the rates in the NaI detectors, with some Terrestrial Gamma-ray Flash (TGF)-specific algorithms using the bismuth germanate (BGO) detectors, sensitive to higher energies, up to 40 MeV. The NaI detectors are placed around the Fermi spacecraft with different orientations to provide the required sensitivity and FOV. The cosine-like angular response of the thin NaI detectors is used to localize burst sources by comparing rates from detectors with different viewing angles. The two BGO detectors are placed on opposite sides of the spacecraft so that all sky positions are visible to at least one BGO detector. The signals from all 14 GBM detectors are collected by a central Data Processing Unit (DPU). This unit digitizes and time-tags the detectors' pulse height signals, packages the resulting data into several different types for transmission to the ground (via the Fermi spacecraft), and performs various data processing tasks such as autonomous burst triggering. The GRB science products are transmitted to the FSSC in two types of files. The first file, called the "bcat" file, provides basic burst parameters such as duration, peak flux and fluence, calculated from 8-channel data using a spectral model which has a power-law in energy that falls exponentially above an energy EPeak, known as the Comptonized model. The crude 8-channel binning and the simple spectral model allow data fits in batch mode over numerous time bins in an efficient and robust fashion, including intervals with little or no flux, yielding both values for the burst duration, and deconvolved lightcurves for the detectors included in the fit. The bcat file includes two extensions. The first, containing detailed information about energy channels and detectors used in the calculations, is detector-specific, and includes the time history of the deconvolved flux over the time intervals of the burst. The second shows the evolution of the spectral parameters obtained in a joint fit of the included detectors for the model used, usually the Comptonized model described above. The bcat files and their time-varying quantities contained in these two extensions are available at the HEASARC FTP site. Quantities derived from these batch fits are given in the bcat primary header and presented in the Browse table, as described below. The main purpose of the analysis contained in the bcat file is to produce a measure of the duration of the burst after deconvolving the instrument response. The duration quantities are: <pre> * 't50' - the time taken to accumulate 50% of the burst fluence starting at the 25% fluence level. * 't90' - the time taken to accumulate 90% of the burst fluence starting at the 5% fluence level. </pre> By-products of this analysis include fluxes on various timescales and fluences, both obtained using the simple Comptonized model described above. These quantities are detailed in the Browse table using the following prefixes: <pre> * 'flux' - the peak flux over 3 different timescales obtained in the batch mode fit used to calculate t50/t90. * 'fluence' - the total fluence accumulated in the t50/t90 calculation. </pre> The fluxes and fluences derived from the 8-channel data for these bcat files should be considered less reliable than those in the spectral analysis files described below. Analysis methods used in obtaining these quantities are detailed in the first GBM GRB Catalog (Paciesas et al. 2011). Updates of bcat files will be sent (with new version numbers) as these parameters are refined. This "bcat" file is produced for triggers that are classified as GRBs (with exceptions as described below), and supplements the initial data in the trigger or "tcat" file that is produced for all triggers. The second type of file (the spectrum or "scat" file) provides parameter values and goodness-of-fit measures for different types of spectral fits and models. These fits are performed using 128-channel data, either CSPEC or, for short bursts, TTE data. The type and model are coded into the file name. There are currently two spectrum categories: <pre> * Peak flux ('pflx') - a single spectrum over the time range of the peak flux of the burst * Fluence ('flnc') - a single spectrum over the entire burst duration selected by the duty scientist. </pre> Like the bcat files, the scat files have two extensions. The first extension gives detector-specific information, including photon fluxes and fluences for each detector, which are provided for each energy channel. The second extension provides derived quantities such as flux, fluence and model parameters for the joint fit of all included detectors. The scat files and their energy-resolved quantities contained in these two extensions are available in the Fermi data archive at the HEASARC. Quantities derived from these spectral fits are available in the Browse table, as described below and in Goldstein et al. (2011). The spectra are fit with a number of models, with the signal-to-noise ratio of the spectrum often determining whether a more complex model is statistically favored. The current set is: <pre> * Power law ('plaw'), * Comptonized (exponentially attenuated power law; 'comp') * Band ('band') * Smoothly broken power law ('sbpl') </pre> <b>Warnings</b> The bcat and scat files result from two completely independent analyses, and consequently, it is possible that the same quantities might show differences. Indeed, 1) the fluxes and fluences in the "scat" files should be considered more reliable than those in the "bcat" files, with the official fluxes and fluences being those yielded by the statistically favored model ("Best_Fitting_Model" in the Browse table) and with the full energy resolution of the instrument; 2) in both the bcat and scat analyses, the set of detectors used for the fits ("Scat_Detector_Mask" in the Browse table) may not be the same as the set of detectors that triggered GBM ("Bcat_Detector_Mask" in the Browse table); 3) background definitions are different for the bcat and scat analysis (see References below). Finally, for weak events, it is not always possible to perform duration or spectral analyses, and some bursts occur too close in time to South Atlantic Anomaly entries or exits by Fermi with resultant data truncations that prevent background determinations for the duration analysis. There is not an exact one-to-one correspondence between those events for which the duration analysis fails and those which are too weak to have a useful spectral characterization. This means that in the HEASARC Browse table there are a handful of GRBs which have duration parameters but not spectral fit parameters, and vice versa. In these cases, blank entries in the table indicate missing values where an analysis was not possible. Values of 0.0 for the uncertainties on spectral parameters indicate those parameters have been fixed in the fit from which other parameters or quantities in the table were derived. Missing values for model fit parameters indicate that the fit failed to converge for this model. This is true mostly for the more complicated models (SBPL or BAND) when the fits fail to converge for weaker bursts. Bad spectral fits can often result in unphysical flux and fluence values with undefined errors. We include these bad fits but leave the error fields blank when they contain undefined values. The selection criteria used in the first catalog (Goldstein et al. 2011) for the determination of the best-fit spectral model are different from those in the second catalog (Gruber et al. 2014). The results using the two methods on the sample included in Goldstein et al. (2011) are compared in Gruber et al. (2014). The old catalog files can be retrieved using the HEASARC ftp archive tree, under "previous" directories. The values returned by Browse always come from the "current" directories. The chi-squared statistic was not used in the 2nd catalog, either for parameter optimization or model comparison. The chi-squared values are missing for a few GRBs. This is believed to be because of a known software issue and should not be considered indicative of a bad fit. The variable "scatalog" included in the Browse tables and in the FITS files indicates which catalog a file belongs to, with 2 being the current catalog, and 1 (or absent) the first catalog (preliminary values may appear with value 0). The information in this table is provided by the Fermi Gamma-ray Burst Monitor Instrument Operations Center (GIOC) and the Fermi Science Support Center (FSSC). The values come from burst and spectral catalog entry FITS files provided by the GIOC to the FSSC. These FITS files may contain additional data and are available for download. This table is updated automatically within a day or so of new data files being processed and made available. This is a service provided by NASA HEASARC .\' does not match any of the acceptable formats: max string length requirement'>
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Date Created: 2025-11-01 00:42:51.694461
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Date Created: 2025-11-01 01:26:13.192315
