This dataset provides daily fire ignition locations and timing for boreal fires in Alaska, U.S., and Canada between 2001 and 2019. The fire ignition locations and timing are extracted from the ABoVE Fire Emission Database; however, the temperate prairies of Canada, the Atlantic Highlands, and Mixed Wood Plains were not included. Fires were detected from Landsat differenced normalized burn ratio (dNBR) and the daily MODIS burned area and active fire products. Detections by dNBR were limited to fire perimeters from national fire databases. Fire ignition locations were retrieved using a local minimum within the fire perimeters. However, when fire locations were confounded due to simultaneous active fire detections, the fire ignition location was set as the centroid of these pixels. A spatial uncertainty equaling the standard deviation of the pixels' coordinates and the nominal nadir of 1000 m was applied to the fire ignition location. The temporal resolution of the ignition timing is within one day. Data is provided in comma separated values (CSV) and shapefile formats.

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument collected and developed by the Student Airborne Research Program (SARP). The spectral data were collected from flights flown on 2016-06-17 in a NASA ER-2 aircraft over Santa Barbara, California. SARP was an eight-week summer program for junior and senior undergraduate students to acquire hands-on research experience in all aspects of a scientific campaign using airborne science laboratories. The SARP 2016 deployment included one flight with 5 flight tracks. The L1B file format is HDF-4, and L2 products are provided in ENVI and KMZ formats. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.

The dataset provides simulated PM2.5 concentration estimates over Alaska, U.S. PM2.5 (particulate matter with diameter <= 2.5 microns) concentrations in air (micrograms m-3) are gridded at 0.1-degree resolution for May to September for the years 2001 through 2015. The data were created in a modeling process utilizing the Wildland Fire Emissions Inventory System (WFEIS), the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, and multiple models including the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The data are provided in GeoTIFF format.

The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.

The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.

The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.

The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.

The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.

The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the DIST-ALERT data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the DIST-ALERT scenes to provide information about more general disturbance trends. The DIST-ANN product tracks changes at the annual scale, aggregating changes identified in the DIST-ALERT product. Only confirmed disturbances from the associated year are reported together with the date of initial disturbance. As confirmed disturbances are determined using subsequent cloud-free observations to determine if the loss detections persist, the required number of HLS scenes depends on visibility of the target. Due to this dependency, summarizing the DIST-ALERT in the DIST-ANN product will have some latency contingent on the algorithmic calibration and is detailed in the Algorithm Theoretical Basis Document (ATBD).
The OPERA_L3_DIST-ANN-HLS (or DIST-ANN) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate COG. There are 21 layers contained within the DIST-ANN product: vegetation disturbance status, historical vegetation cover indicator, maximum vegetation cover indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, vegetation disturbance duration, date of last observation assessed for vegetation disturbance, and several generic disturbance layers. Each product layer is gridded to the same resolution and tiling system as HLS V2: 30 meter (m) and Military Grid Reference System (MGRS). See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ANN product.