AndreaBozzo/ceres-open-data-index huggingface dataset:
USGS LCMAP CONUS Collection 1.3

The [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. 

This [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, "nodata" values where appropriate, and an updated projection definition.
FWS National Wetlands Inventory

The Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.

**NOTE:** Due to the variation in use and analysis of this data by the end user, each  state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.

For more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).

## STAC Metadata

In addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) to select just the geoparquet assets. See the Example Notebook for more.
ESA Climate Change Initiative Land Cover Maps (NetCDF)

The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. 

The data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.
ESA Climate Change Initiative Land Cover Maps (Cloud Optimized GeoTIFF)

The ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. 

The data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).
Urban Innovation Eclipse Sensor Data

The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.

Project Eclipse currently includes over 100 locations in Chicago, Illinois, USA.

This network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.

## Storage resources

Data are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:

`https://ai4edataeuwest.blob.core.windows.net/eclipse`

Within that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.
Each snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.
Therefore, the data files for the first snapshot are at

`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_*.parquet

The Parquet file schema is as described below. 

## Additional Documentation

For details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).

## License and attribution
Please cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)

## Contact

For questions about this dataset, contact [`msrurbanops@microsoft.com`](mailto:msrurbanops@microsoft.com?subject=eclipse%20question) 


## Learn more

The [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.
Sea Surface Temperature - WHOI CDR

The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25° resolution grid over the global ice-free oceans from January 1988—present.

These Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).
For the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.
USDA Cropland Data Layers (CDLs)

The Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission "to provide timely, accurate and useful statistics in service to U.S. agriculture" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.

This collection includes Cropland, Confidence, Cultivated, and Frequency products.

- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.
- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.
- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.
- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland products.

For more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).
Monthly NOAA U.S. Climate Gridded Dataset (NClimGrid)

The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). 

NClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as "prelim" (preliminary) or "scaled". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. 

This Collection contains **Monthly** data. See the journal publication ["Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.

Users of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:
>"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research."

The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).

*Note*: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.*
NOAA MRMS QPE 1-Hour Pass 2

The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.

MRMS QPE is available as a "Pass 1" or "Pass 2" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.

This Collection contains the **1-Hour Pass 2** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).
NOAA MRMS QPE 1-Hour Pass 1

The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.

MRMS QPE is available as a "Pass 1" or "Pass 2" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.

This Collection contains the **1-Hour Pass 1** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).
Chesapeake Land Use

A high-resolution 1-meter [land use data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-use-data-project/) in raster format for the entire Chesapeake Bay watershed. The dataset was created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.

The dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.
Chesapeake Land Cover (13-class)

A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. 

The dataset is composed of 13 land cover classes, although not all classes are used in all areas. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf) and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.
Chesapeake Land Cover (7-class)

A high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/) in raster format for the entire Chesapeake Bay watershed based on 2013-2014 imagery from the National Agriculture Imagery Program (NAIP). The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. 

The dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.
Denver Regional Council of Governments Land Use Land Cover

The [Denver Regional Council of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project) datasets are developed in partnership with the [Babbit Center for Land and Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy) and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution covering 6,000 square miles of the Denver, Colorado region. The classification data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP) aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project) (6in resolution everywhere except the mountainous regions to the west, which are 1ft resolution).
Landsat Collection 2 Level-1

Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.

This dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.
Esri 10-Meter Land Cover (10-class)

__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2020 data available in the newer [9-class dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)._

Global estimates of 10-class land use/land cover (LULC) for 2020, derived from ESA Sentinel-2 imagery at 10m resolution.  This dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification.  The global map was produced by applying this model to the relevant yearly Sentinel-2 scenes on the Planetary Computer.

This dataset is also available on the [ArcGIS Living Atlas of the World](https://livingatlas.arcgis.com/landcover/).
NOAA MRMS QPE 24-Hour Pass 2

The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.

MRMS QPE is available as a "Pass 1" or "Pass 2" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.

This Collection contains the **24-Hour Pass 2** sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).
MODIS Snow Cover 8-day

This global Level-3 (L3) data set provides the maximum snow cover extent observed over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles are generated by compositing 500 m observations from the 'MODIS Snow Cover Daily L3 Global 500m Grid' data set. A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.