NOAA GEFS forecast, 35 day

Archive of NOAA Global Ensemble Forecast System (GEFS) weather forecasts produced by NOAA/NCEP, providing 31-member ensemble predictions initialized daily at 00 UTC from October 2020 to present. Forecasts extend up to 35 days (840 hours) ahead at global coverage with 0.25–0.5 degree spatial resolution, covering atmospheric variables including temperature, precipitation, wind, humidity, pressure, and radiation flux. Data are binary-rounded for improved compression and stored in cloud-optimized Zarr format by dynamical.org.

weather ensemble-forecast numerical-weather-prediction meteorology noaa global-coverage zarr precipitation

Dataset files (2 x .zarr)

Source Cooperative product page

## README.md
# NOAA GEFS forecast, 35 day - dynamical.org zarr

View the latest documentation at https://dynamical.org/catalog/noaa-gefs-forecast-35-day/.

| | |
| - | - |
| **Spatial domain**      | Global                                                                  |
| **Spatial resolution**  | 0-240 hours: 0.25 degrees (\~20km), 243-840 hours: 0.5 degrees (\~40km) |
| **Time domain**         | Forecasts initialized 2020-10-01 00:00:00 UTC to Present                |
| **Time resolution**     | Forecasts initialized every 24 hours.                                   |
| **Forecast domain**     | Forecast lead time 0-840 hours (0-35 days) ahead                        |
| **Forecast resolution** | Forecast step 0-240 hours: 3 hourly, 243-840 hours: 6 hourly            |

### Data Access

```
https://data.dynamical.org/noaa/gefs/forecast/latest.zarr?email=optional@email.com
```

*Email optional. Providing your email as a query param helps us understand usage and impact to keep dynamical.org supported for the long-term. For catalog updates follow [here](https://dynamical.org/updates).*

### Description

The Global Ensemble Forecast System (GEFS) is a National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) weather forecast model. GEFS creates 31 separate forecasts (ensemble members) to describe the range of forecast uncertainty.

This dataset is an archive of past and present GEFS forecasts. Forecasts are identified by an initialization time (`init_time`) denoting the start time of the model run as well as by the `ensemble_member`. Each forecast has a 3-hourly forecast step along the `lead_time` dimension. This dataset contains only the 00 hour UTC initialization times which produce the full-length, 35-day forecast.

The data values in this dataset have been rounded in their binary floating point representation to improve compression. See [Klöwer et al. 2021](https://www.nature.com/articles/s43588-021-00156-2) for more information on this approach.

Storage for this dataset is generously provided by [Source Cooperative](https://source.coop/), a [Radiant Earth](https://radiant.earth/) initiative.

## Dimensions

| Dimension            | Min                 | Max              | Units                    |
| -------------------- | ------------------- | ---------------- | ------------------------ |
| **init\_time**       | 2020-10-01T00:00:00 | Present          | seconds since 1970-01-01 |
| **ensemble\_member** | 0                   | 30               | realization              |
| **lead\_time**       | 0 days 00:00:00     | 35 days 00:00:00 | seconds                  |
| **latitude**         | -90                 | 90               | degrees\_north           |
| **longitude**        | -180                | 179.75           | degrees\_east            |

## Variables

| Variable                                         | Units               | Dimensions                                               |
|--------------------------------------------------|---------------------|---------------------------------------------------------|
| **categorical_freezing_rain_surface**            | 0=no; 1=yes         | init_time × ensemble_member × lead_time × latitude × longitude |
| **categorical_freezing_rain_surface_avg**        | 0=no; 1=yes         | init_time × lead_time × latitude × longitude           |
| **categorical_ice_pellets_surface**              | 0=no; 1=yes         | init_time × ensemble_member × lead_time × latitude × longitude |
| **categorical_ice_pellets_surface_avg**          | 0=no; 1=yes         | init_time × lead_time × latitude × longitude           |
| **categorical_rain_surface**                     | 0=no; 1=yes         | init_time × ensemble_member × lead_time × latitude × longitude |
| **categorical_rain_surface_avg**                 | 0=no; 1=yes         | init_time × lead_time × latitude × longitude           |
| **categorical_snow_surface**                     | 0=no; 1=yes         | init_time × ensemble_member × lead_time × latitude × longitude |
| **categorical_snow_surface_avg**                 | 0=no; 1=yes         | init_time × lead_time × latitude × longitude           |
| **downward_long_wave_radiation_flux_surface**    | W/(m^2)             | init_time × ensemble_member × lead_time × latitude × longitude |
| **downward_long_wave_radiation_flux_surface_avg**| W/(m^2)             | init_time × lead_time × latitude × longitude           |
| **downward_short_wave_radiation_flux_surface**   | W/(m^2)             | init_time × ensemble_member × lead_time × latitude × longitude |
| **downward_short_wave_radiation_flux_surface_avg**| W/(m^2)             | init_time × lead_time × latitude × longitude           |
| **expected_forecast_length**                     | seconds             | init_time                                              |
| **geopotential_height_cloud_ceiling**            | gpm                 | init_time × ensemble_member × lead_time × latitude × longitude |
| **ingested_forecast_length**                     | seconds             | init_time × ensemble_member                           |
| **maximum_temperature_2m**                       | C                   | init_time × ensemble_member × lead_time × latitude × longitude |
| **maximum_temperature_2m_avg**                   | C                   | init_time × lead_time × latitude × longitude           |
| **minimum_temperature_2m**                       | C                   | init_time × ensemble_member × lead_time × latitude × longitude |
| **minimum_temperature_2m_avg**                   | C                   | init_time × lead_time × latitude × longitude           |
| **percent_frozen_precipitation_surface**         | %                   | init_time × ensemble_member × lead_time × latitude × longitude |
| **precipitable_water_atmosphere**                | kg/(m^2)            | init_time × ensemble_member × lead_time × latitude × longitude |
| **precipitable_water_atmosphere_avg**            | kg/(m^2)            | init_time × lead_time × latitude × longitude           |
| **precipitation_surface**                        | kg/(m^2)            | init_time × ensemble_member × lead_time × latitude × longitude |
| **precipitation_surface_avg**                    | kg/(m^2)            | init_time × lead_time × latitude × longitude           |
| **pressure_reduced_to_mean_sea_level**           | Pa                  | init_time × ensemble_member × lead_time × latitude × longitude |
| **pressure_reduced_to_mean_sea_level_avg**       | Pa                  | init_time × lead_time × latitude × longitude           |
| **pressure_surface**                             | Pa                  | init_time × ensemble_member × lead_time × latitude × longitude |
| **pressure_surface_avg**                         | Pa                  | init_time × lead_time × latitude × longitude           |
| **relative_humidity_2m**                         | %                   | init_time × ensemble_member × lead_time × latitude × longitude |
| **relative_humidity_2m_avg**                     | %                   | init_time × lead_time × latitude × longitude           |
| **temperature_2m**                               | C                   | init_time × ensemble_member × lead_time × latitude × longitude |
| **temperature_2m_avg**                           | C                   | init_time × lead_time × latitude × longitude           |
| **total_cloud_cover_atmosphere**                 | %                   | init_time × ensemble_member × lead_time × latitude × longitude |
| **total_cloud_cover_atmosphere_avg**             | %                   | init_time × lead_time × latitude × longitude           |
| **valid_time**                                   | seconds since 1970-01-01 | init_time × lead_time                              |
| **wind_u_100m**                                  | m/s                 | init_time × ensemble_member × lead_time × latitude × longitude |
| **wind_u_10m**                                   | m/s                 | init_time × ensemble_member × lead_time × latitude × longitude |
| **wind_u_10m_avg**                               | m/s                 | init_time × lead_time × latitude × longitude           |
| **wind_v_100m**                                  | m/s                 | init_time × ensemble_member × lead_time × latitude × longitude |
| **wind_v_10m**                                   | m/s                 | init_time × ensemble_member × lead_time × latitude × longitude |
| **wind_v_10m_avg**                               | m/s                 | init_time × lead_time × latitude × longitude           |

Don't see what you're looking for? Let us know at [feedback@dynamical.org](mailto\:feedback@dynamical.org).

## Examples

[Open notebook in GitHub](https://github.com/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-35-day.ipynb)

[Open notebook in Colab](https://colab.research.google.com/github/dynamical-org/notebooks/blob/main/noaa-gefs-forecast-35-day.ipynb)

```python
import xarray as xr

ds = xr.open_zarr("https://data.dynamical.org/noaa/gefs/forecast-35-day/latest.zarr?email=optional@email.com")
ds['temperature_2m'].sel(init_time="2025-01-01T00", latitude=0, longitude=0).max().compute()
```


NOAA GFS analysis, hourly

Historical weather data from the Global Forecast System (GFS) model operated by NOAA NCEP, transformed into Zarr format by dynamical.org.


NOAA GFS analysis

Global atmospheric analysis output from NOAA's Global Forecast System (GFS) model, operated by NOAA National Weather Service NCEP. Covers the full globe (180°W–179.75°E, 90°S–90°N) from May 2021 to present at sub-daily temporal resolution. Distributed in cloud-optimized Zarr format for efficient cloud-native access.

weather numerical-weather-prediction meteorology atmospheric-data noaa global-coverage zarr time-series

Dataset files (2 x .zarr)

Source Cooperative product page

## README.md
# NOAA GFS analysis - dynamical.org Zarr

See the latest dataset documentation and examples here: https://dynamical.org/catalog/noaa-gfs-analysis/

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.


NOAA GFS forecast

Global weather forecast data from NOAA's Global Forecast System (GFS), a numerical weather prediction model producing atmospheric forecasts with full global coverage (180°W–179.75°E, 90°S–90°N). The native GFS output has been reformatted by dynamical.org into cloud-native Zarr format for efficient cloud-based access and analysis.

weather numerical-weather-prediction meteorology zarr global-coverage weather-forecasting atmospheric-modeling noaa

Dataset files (1 x .zarr)

Source Cooperative product page

## README.md
# NOAA GFS forecast - dynamical.org Zarr

See the latest dataset documentation and examples here: https://dynamical.org/catalog/noaa-gfs-forecast/


NOAA HRRR forecast, 48 hour

48-hour weather forecasts from NOAA's High Resolution Rapid Refresh (HRRR) numerical weather prediction model, covering the contiguous United States and surrounding regions. The dataset spans July 2018 to present and is derived from NOAA NWS NCEP operational model output, reformatted into cloud-optimized Zarr format by dynamical.org for efficient cloud-native access.

weather numerical-weather-prediction meteorology atmospheric-data noaa zarr weather-forecasting

Dataset files (1 x .zarr)

Source Cooperative product page

## README.md
# NOAA HRRR Forecast, 48 hour - dynamical.org Zarr

See the latest dataset documentation and examples here: https://dynamical.org/catalog/noaa-hrrr-forecast-48-hour/


NOAA MRMS CONUS analysis, hourly

Hourly precipitation analysis from NOAA's Multi-Radar Multi-Sensor (MRMS) system, operated by NWS NCEP, covering the contiguous United States (CONUS). The dataset spans from November 2014 to near-present, combining data from multiple weather radars and sensors. It has been reformatted into cloud-optimized Zarr by dynamical.org for efficient cloud access.

precipitation radar earth-observation zarr noaa meteorology weather time-series

Dataset files (2 x .zarr)

Source Cooperative product page

## README.md
# NOAA MRMS CONUS analysis, hourly - dynamical.org Zarr

See the latest dataset documentation and examples here: https://dynamical.org/catalog/noaa-mrms-conus-analysis-hourly/


East Africa Hydromet Forecasts

Pan and Pencil chunked hydromet observations datasets


East Africa Hydromet Observations


Climate Change Impact on Water Resources (CCIWR)

Projected future changes in water resources across all global land areas at 0.5° × 0.5° resolution, built on top of the ISIMIP3b protocol. It covers three hydrological variables — total runoff, diffuse groundwater recharge, and actual evapotranspiration — derived from a multi-model ensemble of global hydrological and CMIP6 climate models. Relative changes are provided for three future 30-year horizons under three emissions scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5), at annual and seasonal timescales. The ensemble spread captures projection uncertainty, supporting risk-aware climate change adaptation planning.


Amazon Mining Watch

Amazon Mining Watch is a dataset of automated machine-learning detections of artisanal gold mine scars derived from Sentinel-2 satellite imagery, covering all nine Amazonian countries. Detections are stored as cumulative GeoJSON polygons at yearly granularity from 2018–2024 and quarterly from Q2 2025. The dataset enables tracking of legal and illegal gold mining expansion across the Amazon rainforest.

gold-mining amazon illegal-mining remote-sensing machine-learning environmental-monitoring deforestation conservation

Dataset files (10 x .geojson)

Source Cooperative product page

## README.md

![EarthGenome](https://data.source.coop/earthgenome/earthindexembeddings/logo.png)
# Amazon Mining Watch

[![CC-4 license](https://img.shields.io/badge/License-CC--4-blue.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Sensor](https://img.shields.io/badge/Sensor%F0%9F%9B%B0%EF%B8%8F-Sentinel2-blue)](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2)
[![Format](https://img.shields.io/badge/Format-GeoJSON%20%F0%9F%8C%8E%EF%B8%8F-blue)](https://geojson.org/)

This repository contains automated detections of artisanal gold mine
scars in Sentinel-2 satellite imagery.

The data is licensed under the [creative commons 4.0](https://creativecommons.org/licenses/by/4.0/) international license which, in summary, only requires attribution.

Code, description of the machine-learning models that generated the
detections, and additional versions of this data are available at the
[Amazon Mining Watch Github repository](https://github.com/earthrise-media/mining-detector).

### Data

Data are presented yearly from 2018-2024 and quarterly starting from
the second quarter of 2025. Each file represents cumulative detections of mine scars
begining in 2018. Detections are made on overlapping 480m x 480m
patches of the Earth's surface, merged into polygons where they overlap, and saved as
GeoJSON.

### Models

- Beginning with the 2024 data year, the model is an ensemble of
  classification heads trained atop embeddings from the [SSL4EO
  ViT-DINO/S16 geospatial foundation model](github.com/zhu-xlab/SSL4EO-S12),
  with ex-post-facto data cleaning by human review of satellite imagery.

- Data years 2018-2023 use a legacy ensemble of end-to-end trained
  convolutional neural networks.

### Data notes

- The break in models means that trends are not reliable from year
  2023 to 2024. In particular, the newer models are significantly more
  sensitive the the older, and the additional mining detected in
  2024 is partly an artefact of the model shift.

- The models producing this data are under active development. Data updates and
pixelwise segmentations of mining scars are expected in early 2026.

### Contact
Using this? Want work together? Ping us @ info@earthgenome.org or info@earthindex.ai

providers: role [processor] for 'Earthrise Media' is an AI guess — Assumed processor: hosts the upstream mining-detector GitHub repo where the ML model code lives. Verify before flipping to _state: reviewed.


Earth Index Embeddings

Global-coverage machine learning embeddings derived from Sentinel-2 satellite temporal mosaics using the SoftCon Model. Each embedding is a 384-dimensional vector capturing Earth surface characteristics at specific UTM grid locations and time periods.

remote-sensing sentinel-2 embeddings machine-learning earth-observation

Dataset files (17722 x .parquet)

Source Cooperative product page

## README.md

![EarthGenome](https://data.source.coop/earthgenome/earthindexembeddings/logo.png)
# EarthIndex Embeddings from Sentinel2 Temporal Mosaics

[![CC-4 license](https://img.shields.io/badge/License-CC--4-blue.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Sensor](https://img.shields.io/badge/Sensor%F0%9F%9B%B0%EF%B8%8F-Sentinel2-blue)](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2)
[![Format](https://img.shields.io/badge/Format-GeoParquet%20%F0%9F%8C%8E%EF%B8%8F-blue)](https://geoparquet.org/)

This repository holds embeddings generated the entire globe for the given date ranges.

This data is licensed under the [creative commons 4.0](https://creativecommons.org/licenses/by/4.0/) international license which, in summary, only requires attribution.

### Model
These embeddings are generated from the [SoftCon Model](https://github.com/zhu-xlab/softcon/tree/main) published by [Zhu Labs](https://www.asg.ed.tum.de/sipeo/home/)

### Naming
Artifacts are name according to their original UTM grid number, concatenated with the start and end dates.  For example `31TDF_2024-01-01_2025-01-01.parquet` will give you embeddings for Barcelona (31TDF) over the course of 2024.

### Artifacts and Metadata
All assets are Geoparquet in EPSG:4626 projection with point geometries. The fields are as follows:
- id: uint64
- embedding: float[384]
- geometry: Point in WKB

### Contact
Using this? Want work together? Ping us @ info@earthgenome.org or info@earthindex.ai


Earth Index Imagery

Global collection of cloudless Sentinel-2 yearly imagery mosaics, covering all spectral bands except B10 at their native resolution. Assets are Cloud-Optimized GeoTIFFs (COGs) in Web Mercator (EPSG:3857) projection, organized by UTM grid tile and annual date range. Each asset includes quality metadata (good pixel percentage) and provenance references to the contributing Sentinel-2 L2A scenes.

remote-sensing satellite-imagery sentinel-2 multispectral mosaics

Dataset files (1045277 x .tif)

Source Cooperative product page

## README.md

![EarthGenome](https://data.source.coop/earthgenome/earthindexembeddings/logo.png)
# Earth Index yearly imagery mosaics

[![CC-4 license](https://img.shields.io/badge/License-CC--4-blue.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Sensor](https://img.shields.io/badge/Sensor%F0%9F%9B%B0%EF%B8%8F-Sentinel2-blue)](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2)
[![Format](https://img.shields.io/badge/Format-COG%20%E2%9A%99%EF%B8%8F-purple)](https://www.cogeo.org/)

This repository holds a large (and growing!) collection of cloudless Senitnel 2 data. All bands (except B10 as this was generated from L2A) are available at their original native resolution.

This data is licensed under the creative commons 4.0 international license which, in summary, only requires attribution.

You can read more about how this was created [here](https://medium.com/earthrisemedia/earth-index-imagery-v2-40674f18b4d2).

### Resources
- [Earth Genome STAC](https://stac.earthgenome.org/collections/sentinel2-yearly-mosaics) instance for searching this collection
- [STAC Browser](https://browser.stac.earthgenome.org/collections/sentinel2-yearly-mosaics), human friendly interface

### Naming
Artifacts are name according to their original UTM grid number, concatenated with the start and end dates.  For example the `31TDF_2024-01-01_2025-01-01/` directory will give you bands over for Barcelona (31TDF) over the course of 2024.

### Artifacts and Metadata
All assets are COG encoded GeoTiffs in Web Mercator (EPSG:3857) projection. Besides the item id itself, the STAC catalog has the original UTM infornmation so you can calculate and then reproject to the original EPSG code, if needed.

The STAC assets also have 2 properties to help better understand the provience and quality of the images:

- `good_pxl_pct` is a the estimated percentage of "good" (non masked) pixels
- `generated_from` is an array of references to the Sentinel L2A assets which contributed to this asset

### Contact
Using this? Want work together? Ping us @ info@earthgenome.org or info@earthindex.ai

providers: role [producer, processor] for 'Earth Genome' is an AI guess — Credited as dataset owner/publisher; README contact and STAC catalog are earthgenome.org, but the processing pipeline is described under the Earth Index brand. Verify before flipping to _state: reviewed.

providers: role [producer] for 'European Space Agency (ESA)' is an AI guess — Sentinel-2 L2A imagery is the upstream source; ESA is not explicitly named as provider in the README but is the originating satellite operator. Verify before flipping to _state: reviewed.


Global Food Twin

The Global Food Twin is a model that estimates the most likely multi-modal transportation routes for food flows between producing and consuming subnational regions worldwide. It integrates food balance sheets, production maps, bilateral trade data, and transportation network optimization across 82 food groups and 3,787 subnational administrative regions. The dataset includes model inputs (downscaled trade allocations, infrastructure nodes, transport costs, production and demand rasters) and outputs (routed land, sea, and within-country food flow paths with mode and segment detail).

agriculture food-systems food-security trade-flows digital-twin

BEL CHN CZE DEU DNK ESP FRA HUN LVA NLD POL SVK SVN

ARG AUT BEL BGD BGR BLR BOL BRA CAF CAN CHE CHN COD COG COL CZE DEU FIN FRA HRV HUN IND ITA KHM LTU LUX MDA NLD PER POL PRT PRY ROU RUS SVK THA UKR USA VEN VNM

Alcoholic Beverages
Animal Fats
Aquatic Products, Other
Cereals - Excluding Beer
Eggs
Fish, Seafood
Fruits - Excluding Wine
Meat
Milk - Excluding Butter
Offals
Oilcrops
Pulses
Spices
Starchy Roots
Stimulants
Sugar Crops
Sweetners
Treenuts
Vegetables
Vegetable Oils

Dairy and Eggs
Fruits
Grains
Meat and Fish
Oils and Oilseed
Other
Pulses
Starchy Roots
Treenuts
Vegetables

[admin_code] ISO3 country code identifying the subnational demand region's country

[admin_code] ISO3 code of the reporting (exporting) country

[admin_code] ISO3 code of the partner (importing) country

Sugar beet.csv

IWWPorts_infrastructure.csv

lon

[latlon] Longitude of infrastructure node

lat

[latlon] Latitude of infrastructure node (port, rail junction, etc.)

fg1_fg2_fg3_lookup.csv

global_IWW_transport_cost.csv

[admin_code] ISO3 country code of the route origin node

[admin_code] ISO3 country code of the route destination node

fg1_totals_production_exports_calories_protein_iron_vita.csv

Flows_Yams.csv

[admin_code] Subnational admin region ID of flow origin (e.g. 'FRA.10_1')

[admin_code] Subnational admin region ID of flow destination

Flows_Sugar beet.csv

Flows_Sugar beet.csv

Flows_Sugar beet.csv

Flows_Fish__Body_Oil.csv

Source Cooperative product page

## README.md
# Global Food Twin
The Global Food Twin is a model that estimates the most likely transportation routes for food flows between producing and consuming regions worldwide. By combining food balance sheets, production maps, trade data, transportation networks, and optimization algorithms, we've created the best guess of how food moves across the globe—from producing to consuming regions—accounting for the complex multi-modal transportation systems. Visit a proof of concept [application visualizing this model and global food flows here](https://foodtwin.theplotline.org/).

A few facts about the Global Food Twin:
- Comprehensive coverage: 82 food groups across 3,787 subnational administrative regions globally
- Multi-modal transport: Maritime, land-based (rail, road, inland waterway), and combined transportation routes
- Detailed routing: Actual geospatial routes including specific ports, rail lines, and road networks

## Acknowledgements
This project was led by [Cameron Kruse](https://www.linkedin.com/in/krusecameron/) at [Earth Genome](https://www.earthgenome.org/) working with [Zia Mehrabi](https://www.colorado.edu/envs/zia-mehrabi), [Ginni Braich](https://www.linkedin.com/in/ginni-braich-41672337/), and [Kushank Bajaj](https://www.linkedin.com/in/kushank-bajaj-ph-d-536a8350/) [Better Planet Laboratory](https://betterplanetlab.com/) on the model; [Development Seed](https://developmentseed.org/) on the front end application; the model built on prior work by [Jasper Verschuur](https://www.linkedin.com/in/jasper-verschuur-076092104/) Assistant Professor at Delft University.

## Methods
This repository contains the data produced by the model and used by the Global Food Twin Application. More details about how this data was developed including the code used can be found in this repository [GitHub repository](https://github.com/Better-Planet-Laboratory/globalfoodtwin).

## the Data
This repository is organized into inputs and outputs. Below is a diagram of the folder structure. the [all_files.md](https://source.coop/earthgenome/foodtwin/all_files.md) document contains a full file structure.

```
├── 📁 inputs/
│   ├── 📁 downscaled_exports_imports/
│   │   ├── 📁 demand_allocated/
│   │   └── 📁 supply_allocated/
│   │       └── 📁 foodgroup1/
│   ├── 📁 infrastructure/
│   ├── 📁 production_demand/
│   │   ├── 📁 demand_rasters/
│   │   └── 📁 production_rasters/
│   └── 📁 transport_cost/
│       ├── 📁 land_transport/
│       └── 📁 maritime_transport/
└── 📁 outputs/
    ├── 📁 additional_files/
    ├── 📁 land/
    │   ├── 📁 land_domestic/
    │   └── 📁 land_reexports/
    ├── 📁 sea/
    │   ├── 📁 sea_domestic/
    │   └── 📁 sea_reexports/
    └── 📁 within_country/
```

## Using this data
This data is licensed under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).

If you use this data in accordance with the [CC 4.0 license](http://creativecommons.org/licenses/by/4.0/), please cite:
`Better Planet Laboratory and Earth Genome. (2025). Global Food Twin. Harvard Dataverse. https://doi.org/10.7910/DVN/BLVPFU`

Reach out to [Cameron Kruse](https://www.linkedin.com/in/krusecameron/) with questions.

extent.spatial.bbox: omitted; spatial reference is categorical via [inputs/infrastructure.lat (latlon), inputs/infrastructure.lon (latlon), inputs/downscaled_exports_imports/demand_allocated.iso3 (admin_code), inputs/downscaled_exports_imports/demand_allocated.reporter_ISO3 (admin_code), inputs/downscaled_exports_imports/demand_allocated.partner_iso3 (admin_code), inputs/transport_cost/land_transport.from_iso3 (admin_code), inputs/transport_cost/land_transport.to_iso3 (admin_code), outputs/land/land_domestic.from_id_admin (admin_code), outputs/land/land_domestic.to_id_admin (admin_code)]. Add bbox once the placename → bbox gazetteer covers it.


Sentinel 2 Embeddings

No Description Provided


Sentinel 2 Cloud Free Temporal Mosaics

A large and growing collection of cloud-free Sentinel-2 temporal mosaics covering all spectral bands except B10, generated from L2A data at native resolution. Assets are Cloud-Optimized GeoTIFFs in Web Mercator projection, named by UTM grid tile and composite date range. Each asset includes provenance metadata tracking contributing source scenes and estimated good-pixel percentage.

remote-sensing sentinel-2 satellite-imagery temporal-mosaic multispectral cog

Dataset files (576453 x .tif)

Source Cooperative product page

## README.md

![EarthGenome](https://assets-global.website-files.com/642c1e823b4d441963f3fe5e/6604748ad5ef1e8e7c26dde2_Earth%20Genome%20Logo%20(Original).svg)
# Sentinel 2 Cloud Free Temporal Mosaics

[![CC-4 license](https://img.shields.io/badge/License-CC--4-blue.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Sensor](https://img.shields.io/badge/Sensor%F0%9F%9B%B0%EF%B8%8F-Sentinel2-blue)](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2)
[![Format](https://img.shields.io/badge/Format-COG%20%E2%9A%99%EF%B8%8F-purple)](https://www.cogeo.org/)

This repository holds a large (and growing!) collection of cloudless Senitnel 2 data.  All bands (except B10 as this was generated from L2A) are available at their original native resolution.

This data is licensed under the [creative commons 4.0](https://creativecommons.org/licenses/by/4.0/) international license which, in summary, only requires attribution.

### Resources
- Earth Genome [STAC](https://stac.earthgenome.org) instance for searching this collection
- [STAC Browser](https://browser.stac.earthgenome.org/collections/sentinel2-temporal-mosaics), human friendly interface
- [Tile Cache, levels 0-8 (2.5gb)](https://data.source.coop/earthgenome/sentinel2-temporal-mosaics/cache.tar.gz) - if you wanted to set up a tile server, this archive has tile levels 0-8 pre-tiled.

### Naming
Artifacts are name accoring to their original UTM grid number, concatonated with the start and end dates.  For example `31TDF_2023-01-01_2024-01-01` will give you Barcelona (31TDF) over the course of 2023.

### Artifacts and Metadata
All assets are COG encoded GeoTiffs in Web Mercator (EPSG:3857) projection. Besides the item id itself, the STAC catalog has the original UTM infornmation so you can calculate and then reproject to the original EPSG code, if needed.

The STAC assets also have 2 properties to help better understand the provience and quality of the images:
- `good_pxl_pct` is a the estimated percentage of "good" (non masked) pixels
- `generated_from` is an array of references to the Sentinel L2A assets which contributed to this asset

### Contact
Using this? Want work together? Ping us @ info@earthgenome.org

providers: role [producer] for 'ESA / Copernicus' is an AI guess — ESA/Copernicus produces the underlying Sentinel-2 L2A source imagery; role inferred from data lineage, not stated in this README.. Verify before flipping to _state: reviewed.


Storm Events Database

The Storm Events Database documents storms and other weather phenomena having sufficient intensity to cause loss of life, injuries, property damage, and/or disruption to commerce across the United States. Records date from 1950 up to the current year. Created by the National Weather Service and the National Centers for Environmental Information, the dataset is presented here by The Internet of Water Coalition, The Commons, and Earth Genome.


Repository of all forecasts submitted to the Ecological Forecasting Initiative

A partitioned Parquet database of all forecasts submitted to the Ecological Forecasting Initiative's NEON Forecasting Challenge. It contains probabilistic ecological predictions across multiple themes (aquatics, terrestrial, phenology, etc.) from numerous research groups, partitioned by theme and model. The canonical source is data.ecoforecast.org; this copy is in development.

ecology biodiversity ecological-forecasting neon environmental-monitoring aquatics hydrology time-series environment

part-0.parquet

[admin_code] NEON site identifier (4-letter code) indicating the ecological monitoring station the forecast targets

Source Cooperative product page

## README.md
# Forecasts submitted to the Ecological Forecasting Initiative's NEON Challenge

***In development, experimental***

*At this time, canonical sources are still hosted on* `data.ecoforecast.org`.

## Resources

- [STAC Catalog](https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/eco4cast/neon4cast-catalog/main/stac/catalog.json)

## Quickstart

Arrow provides an easy way to access remote parquet files from most languages widely used in data science.  Here we access all forecasts submitted to a particular theme. (Users looking to load only a single model should specify that on the path for faster access.  The STAC catalog can be used to explore available models).

The examples below show 'cloud-native' connections to the data -- 'lazy' connections that do not download the entire asset, but allow us to filter, subset, and operate directly on the remote data product.

### R Access

```{r}
library(arrow)
base = "s3://anonymous@us-west-2.opendata.source.coop"
repo = "eco4cast/neon4cast-forecasts"
theme = "aquatics"
uri = glue::glue("{base}/{repo}/parquet/{theme}?region=us-west-2")

open_dataset(uri)

```

### Python Access

```{python}
import pyarrow.dataset as ds

base = "s3://anonymous@us-west-2.opendata.source.coop"
repo = "eco4cast/neon4cast-forecasts"
theme = "aquatics"
uri = f"{base}/{repo}/parquet/{theme}?region=us-west-2"

ds.dataset(uri, format="parquet")
```

### duckdb

At this time, `duckdb` access substantially faster than `arrow`.

### R + duckdb

R users can get a dplyr-compatible lazy remote tibble as follows:

```{r}
# remotes::install_github("cboettig/duckdbfs")
library(duckdbfs)

base = "s3://anonymous@us-west-2.opendata.source.coop"
repo = "eco4cast/neon4cast-forecasts"
theme = "aquatics"
uri = glue::glue("{base}/{repo}/parquet/{theme}?region=us-west-2")

df = open_dataset(uri)
```

### Python + duckdb

[ibis](https://ibis-project.org/) provides a more Pythonic interface to SQL:

```{python}
import ibis
con = ibis.duckdb.connect()

base = "s3://us-west-2.opendata.source.coop"
repo = "eco4cast/neon4cast-forecasts"
theme = "aquatics"
uri = f"{base}/{repo}/parquet/{theme}/**"

con.raw_sql(f"""
INSTALL httpfs;
LOAD httpfs;
SET s3_region='us-west-2';
""")

db = con.read_parquet(uri)
```

providers: role [producer, host] for 'Ecological Forecasting Initiative' is an AI guess — EFI runs the challenge and aggregates submitted forecasts at data.ecoforecast.org; treating them as producer and original host. Verify before flipping to _state: reviewed.

providers: role [producer] for 'NEON (National Ecological Observatory Network)' is an AI guess — NEON provides the observation sites and targets that forecasts are issued against; role inferred from challenge structure. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [part-0.site_id (admin_code)]. Add bbox once a placename → bbox gazetteer is available.


Ecological Forecasting Initiative Scored Forecast Database

A partitioned Parquet database of ecological forecasts, observations, and probabilistic skill scores submitted to the Ecological Forecasting Initiative (EFI). Covers multiple ecological domains including aquatics, terrestrial ecosystems, beetles, and ticks, with data collected at NEON sites. Each record includes forecast distributions (mean, median, quantiles) and scoring metrics (CRPS, log score) for evaluating forecast accuracy.

ecology biodiversity timeseries neon environmental-monitoring aquatics hydrology terrestrial

part-0.parquet

[placename] NEON site identifier indicating the ecological monitoring location

Source Cooperative product page

providers: role [producer, licensor] for 'Ecological Forecasting Initiative' is an AI guess — Assumed producer and licensor as the named initiative behind the forecast database; not explicitly stated in README.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [part-0.site_id (placename)]. Add bbox once a placename → bbox gazetteer is available.


Canada's Energy Future 2023 (EF2023) is the Canada Energy Regulator's long-term outlook of national energy supply and demand projections from 2000 to 2050, covering electricity, crude oil, natural gas, hydrogen, and other energy carriers. It models three scenarios—Current Measures, Canada Net-zero, and Global Net-zero—with the latter two representing pathways to net-zero GHG emissions by 2050. Data are disaggregated by province/territory and energy type across 20 CSV files.

energy energy-projections electricity-generation crude-oil greenhouse-gas-emissions net-zero

Canada Net-zero
Current Measures
Global Net-zero

Canadian Light Sweet (CLS) - 2022 US$/bbl

Nova Inventory Transfer (NIT) - 2022 US$/MMBtu

West Texas Intermediate (WTI) - 2022 US$/bbl

Western Canadian Select (WCS) - 2022 US$/bbl

Exports from westcoast BC to International

(Upgraded Bitumen)
C5+
Conventional Heavy
Conventional Light
Field Condensate
In Situ Bitumen
Mined Bitumen
Total

Biomass / Geothermal
Coal & Coke
Hydro / Wave / Tidal
Natural Gas
Oil
Solar
Uranium
Wind

Battery Storage
Bioenergy
Bioenergy with CCUS
Coal and Coke
Coal with CCUS
Hydro
Hydrogen
Natural Gas
Natural Gas with CCUS
Offshore Wind
Oil
Onshore Wind
Solar (Distributed)
Solar (Utility scale)
Total
Uranium
Uranium SMR

Exports
Imports
Interprovincial In-Flows
Interprovincial Out-Flows
Net Exports
Net Interprovincial Out-Flows

Aviation Fuel
Biofuels
Biofuels & Emerging Energy
Diesel
Electricity
Heavy Fuel Oil
Hydrogen
LPG
Lubricants
Motor Gasoline
Natural Gas
Other
RPP
Total End-Use

Diesel
Electricity
Gasoline
Natural Gas
Oil

AB Petrochemical Demand
Ethane Not Recovered
Exports
Imports to AB from U.S.
Imports to ON from U.S.
ON Petrochemical Demand
Oil Sands Solvent Demand
Production from Gas Processing
Production from Oil Sands off-gas
Total Demand
Total Disposition
Total Production
Total Supply
Total Supply - Total Disposition

Agriculture
Buildings
Direct Air Capture
Electricity
Heavy Industry
Land Use, Land Use Change and Forestry
Low or Non-emitting Hydrogen Production
Oil and Gas
Total
Transportation
Waste and Others

Biomass
Electrolysis
Natural gas with CCS

Gross Domestic Product Deflator (2012=100)

Real Gross Domestic Product ($2012 Millions)

Coalbed Methane
Conventional
Conventional (Tight Portion)
Shale
Total

Coalbed Methane
Non Associated
Shale
Solution
Tight
Total

Production from Gas Processing - Pentanes Plus

Coal, Coke and Coke Oven Gas
Hydro
Natural Gas
Nuclear
Other Renewables and Landfill Gas
Refined Petroleum Products
Total

Thousand Barrels per day
Thousand Cubic Metres per day

Billion Cubic Feet per day
Million Cubic Metres per day

Alberta
British Columbia
Canada
Manitoba
Newfoundland and Labrador
Northwest Territories
Nova Scotia
Ontario
Saskatchewan

Alberta
British Columbia
Canada
Manitoba
New Brunswick
Newfoundland and Labrador
Northwest Territories
Nova Scotia
Nunavut
Ontario
Prince Edward Island
Quebec
Saskatchewan
Yukon

Alberta
British Columbia
Canada
Manitoba
New Brunswick
Newfoundland and Labrador
Nova Scotia
Ontario
Prince Edward Island
Quebec
Saskatchewan

Alberta
British Columbia
Manitoba
New Brunswick
Newfoundland and Labrador
Northwest Territories
Nova Scotia
Nunavut
Ontario
Prince Edward Island
Quebec
Saskatchewan
Yukon

Alberta
British Columbia
Saskatchewan
Western Canada

Alberta
British Columbia
Canada
New Brunswick
Northwest Territories
Nova Scotia
Ontario
Saskatchewan
Western Canadian Sedimentary Basin
Yukon

Commercial
Industrial
Residential
Total End-Use
Transportation

Commercial Industrial Residential Transportation

benchmark-prices-2023.csv

butane-2023.csv

crude-oil-production-2023.csv

Region

[placename] Canadian province or national aggregate

ef2023-data-dictionary.csv

Description of Data Value or Column Name

electricity-capacity-2023.csv

[placename] Canadian province/territory or national aggregate

electricity-capacity-technology-2023.csv

electricity-generation-2023.csv

electricity-generation-technology-2023.csv

electricity-interchange-2023.csv

end-use-demand-2023.csv

end-use-prices-2023.csv

ethane-2023.csv

greenhouse-gas-emissions-2023.csv

hydrogen-production-2023.csv

macro-indicators-2023.csv

[placename] National aggregate (Canada only)

natural-gas-drilling-2023.csv

[placename] Western Canadian province or regional aggregate

natural-gas-production-2023.csv

pentanes-plus-2023.csv

primary-demand-2023.csv

propane-2023.csv

Source Cooperative product page

## README.md
# canada_energy_supply_and_demand
Canada’s Energy Future 2023: Energy Supply and Demand Projections to 2050 (EF2023) is the latest long-term energy outlook from the Canada Energy Regulator (CER). The Canada’s Energy Future series explores how possible energy futures might unfold for Canadians over the long term. We use economic and energy models to explore how supply and demand for energy could evolve. EF2023 is the CER’s first long-term outlook that models scenarios where Canada reaches net-zero greenhouse gas (GHG) emissions by 2050. To model net-zero we begin with the end goal in mind: net-zero GHG emissions in 2050, and use our models to identify pathways to that point. This is a different approach compared to past versions of the report where we ran our models without restrictions, giving us insights into what a given premise meant for the future. EF2023 contains three scenarios. Two of these scenarios explore pathways where Canada achieves net-zero emissions by 2050. In the Global Net-zero Scenario, we assume Canada achieves net-zero emissions by 2050. We also assume the rest of the world reduces emissions enough to limit global warming to 1.5 Celsius (°C). In the Canada Net-zero Scenario, Canada also achieves net-zero emissions by 2050 but the rest of the world moves more slowly to reduce GHG emissions. The third scenario, the Current Measures Scenario, assumes limited action to reduce GHG emissions beyond measures in place today. In this scenario, we do not require our modeling results achieve net-zero GHG emissions in Canada by 2050. We also assume limited future global climate action.

extent.spatial.bbox: omitted; spatial reference is categorical via [crude-oil-production-2023.Region (placename), electricity-capacity-2023.Region (placename), electricity-capacity-technology-2023.Region (placename), electricity-generation-2023.Region (placename), electricity-generation-technology-2023.Region (placename), electricity-interchange-2023.Region (placename), end-use-demand-2023.Region (placename), end-use-prices-2023.Region (placename), hydrogen-production-2023.Region (placename), macro-indicators-2023.Region (placename), natural-gas-drilling-2023.Region (placename), natural-gas-production-2023.Region (placename), primary-demand-2023.Region (placename)]. Add bbox once a placename → bbox gazetteer is available.


Canada Natural Gas - Imports and Exports

Monthly and annual records of Canada's natural gas imports and exports by border crossing port, collected by the Canada Energy Regulator (CER) from holders of export and import authorizations. The dataset covers approximately 40 ports connecting Canada to the United States from 1985 to present, reporting volumes, values, and prices in both Canadian and US units.

energy-trade imports-exports energy-regulation commodity-trade

Aden
Brunswick
Cardston
Cardston/Emerson/Niagara Falls
Chippawa
Cornwall
Corunna
Courtright
Coutts
East Hereford
Elmore
Emerson
Emerson/Monchy
Fort Frances
Highwater
Huntingdon
Huntingdon/Kingsgate
Iroquois
Kingsgate
Kitimat
Loomis
Monchy
Napierville
Niagara Falls
North Portal
Ojibway (Windsor)
Philipsburg
Rainy River
Reagan Field
Sarnia
Sarnia/Blue Water
Sarnia/Sault Ste. Marie
Sault Ste Marie
Sierra
Sprague
St Clair
St Stephen
Total
Willow Creek
Windsor (Ojibway)

April August December February January July June March May November October September

natural-gas-exports-and-imports-annual.csv

Port

[placename] Named border crossing port through which natural gas imports or exports flow between Canada and the US

Region

[placename] Broad Canadian geographic region (East, MidWest, West) aggregating multiple ports

natural-gas-exports-and-imports-data-dictionary.csv

natural-gas-exports-and-imports-monthly.csv

Source Cooperative product page

## README.md
# canada_natural_gas_imports_exports
Canada is the world’s fifth largest producer of natural gas and accounts for around five per cent of global production. Natural gas production in Canada is predominantly from the Western Canadian Sedimentary Basin in British Columbia, Alberta, and Saskatchewan. Canadian natural gas supply currently exceeds domestic consumption. Canada’s natural gas markets are heavily integrated with those of the United States and Canada exports its surplus natural gas to the U.S., while importing smaller amounts from the U.S. into Central Canada. The Canada Energy Regulator regulates the export and import of natural gas. Orders or licenses are required to export or import natural gas to and from Canada. Holders of these authorizations report monthly on their activities to the CER. Import and export activities are available by Port from 1985 to present. Data is delayed by approximately 2 months.

providers: role [producer, licensor] for 'Canada Energy Regulator (CER)' is an AI guess — CER is explicitly stated as the regulatory body that collects and publishes this data; licensor role assumed as the authoritative government publishing body, but no explicit license is stated in the README.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [natural-gas-exports-and-imports-annual.Port (placename), natural-gas-exports-and-imports-annual.Region (placename), natural-gas-exports-and-imports-monthly.Port (placename), natural-gas-exports-and-imports-monthly.Region (placename)]. Add bbox once the placename → bbox gazetteer covers it.


Canada Solar Maps

Canada Solar Maps provides monthly and annual estimates of electricity generation potential (kWh/kWp) for grid-connected photovoltaic systems and mean daily global insolation (MJ/m² and kWh/m²) across Canada at a ~2 km grid resolution. Data cover six PV array orientations (sun-tracking and five fixed South-facing tilts) and are also aggregated for over 3,500 Canadian municipalities. The dataset was produced by the Canadian Forest Service in collaboration with CanmetENERGY and the Federal Geospatial Platform, using insolation data from Environment and Climate Change Canada.

solar-energy insolation renewable-energy solar-resource climate

Northwest Territories/Territoires du Nord-Ouest

Prince Edward Island/île du Prince-Édouard

Annual April August December February January July June March May November October September

Canada_municipality_daily_insolation.csv

Province

[placename] Canadian province name in English and French

Municipality

[placename] Canadian municipality name

Mean daily global insolation (kWh/m2)
South-facing with vertical (90 degrees) tilt

Mean daily global insolation (kWh/m2)
South-facing with latitude tilt

Mean daily global insolation (kWh/m2)
South-facing with tilt=latitude+15 degrees

Mean daily global insolation (kWh/m2)
South-facing with tilt=latitude-15 degrees

Mean daily global insolation (kWh/m2)
2-axis tracking

Mean daily global insolation (kWh/m2)
Horizontal (0 degree)

Canada_municipality_photovoltaic-potential.csv

[placename] Canadian municipality name

Photovoltaic potential (kWh/kWp)
South-facing with vertical (90 degrees) tilt

Photovoltaic potential (kWh/kWp)
South-facing with latitude tilt

Photovoltaic potential (kWh/kWp)
South-facing with tilt=latitude+15 degrees

Photovoltaic potential (kWh/kWp)
South-facing with tilt=latitude-15 degrees

Photovoltaic potential (kWh/kWp)
2-axis tracking

Photovoltaic potential (kWh/kWp)
Horizontal (0 degree)

Source Cooperative product page

## README.md
# canada_solar_maps
This repository gives estimates of the electricity that can be generated by grid-connected photovoltaic systems without batteries (in kWh/kWp) and of the mean daily global insolation (in MJ/m2 and in kWh/m2) for any location in Canada on a 60 arc seconds ~2 km grid. They are presented for each month and for the entire year, for six different PV array orientations: a sun-tracking orientation and five fixed South-facing orientations with latitude, vertical (90°), horizontal (0°) and latitude ± 15° tilts. Data can also be obtained directly for individual municipalities from a list of over 3500 municipalities or downloaded for all municipalities at once.

These maps and datasets were developed by the Canadian Forest Service (Great Lakes Forestry Centre) in collaboration with the CanmetENERGY Photovoltaic systems group and the Federal Geospatial Platform. Insolation data were provided by Environment and Climate Change Canada. Web map application developed by Federal Geospatial Platform, 2020.

# References:

Pelland S., McKenney D. W., Poissant Y., Morris R., Lawrence K., Campbell K. and Papadopol P., 2006. The Development of Photovoltaic Resource Maps for Canada, In Proceedings of the Annual Conference of the Solar Energy Society of Canada (SESCI) 2006.

McKenney D. W., Pelland S., Poissant Y., Morris R., Hutchinson M, Papadopol P., Lawrence K. and Campbell K., 2008. Spatial insolation models for photovoltaic energy in Canada, Solar Energy 82, pp. 1049–1061.

Publisher - Current Organization Name: Natural Resources Canada
Licence: Open Government Licence - Canada - https://open.canada.ca/en/open-government-licence-canada

license: Open Government Licence - Canada; not a standard SPDX identifier. See https://open.canada.ca/en/open-government-licence-canada

providers: role [processor] for 'Federal Geospatial Platform' is an AI guess — Described as developer of the web map application; assumed processor role for geospatial delivery.. Verify before flipping to _state: reviewed.

providers: role [licensor] for 'Natural Resources Canada' is an AI guess — Listed as 'Publisher - Current Organization Name'; assumed licensor given Open Government Licence attribution.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: derived national extent for Canada from a categorical placename column; verify before flipping to _state: reviewed.


cefi icechunks

Demo of creating Icechunk stores for CEFI data


chlaz-test

For demo-ing how to create Icechunk stores


Bedmap

Bedmap is a comprehensive Antarctic Ice Sheet topographic dataset providing ice thickness, bedrock elevation, and surface elevation measurements compiled from geophysical surveys and satellite observations.

ice-thickness glaciology cryosphere bedrock-elevation surface-elevation

British Antarctic Survey / BEDMAP Consortium

Dataset files (160 x .parquet)

Source Cooperative product page


DEMOGORGN

A collection of Digital Elevation Model (DEM) realizations covering Antarctica, stored in IceChunk format. The dataset spans approximately 93 GB across roughly 2,200 files and includes NetCDF assets.

digital-elevation-model topography ice-sheet cryosphere elevation

Dataset files (2 x .icechunk)

Source Cooperative product page

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.


Internal Depth-averaged Ice Temperature

Depth-temperature profiles from ice boreholes drilled at multiple sites across Antarctica, covering both East and West Antarctic ice sheets. Includes ice temperature versus depth measurements, with supplemental electrical conductivity, ice chemistry, and grain-size data at select sites. A companion index records site-level metadata including coordinates, ice thickness, drilled depth, and original source publications for each borehole.

glaciology cryosphere borehole ice-temperature ice-cores paleoclimate polar

BIS9845_temp.csv

BoreholeLocations.csv

Location

[placename] Named Antarctic ice feature or station where borehole was drilled

Region

[placename] Broad Antarctic region designation (East or West Antarctica)

Lat

[latlon] Latitude of borehole site

Lon

[latlon] Longitude of borehole site

byrd_temp.csv

CIRD_temp.csv

AntarcticaBoreholeData/DomeC

3 files sampled for this table

domef_temp.csv

ER9612_temp.csv

AntarcticaBoreholeData/KambIceStream

4 files sampled for this table

kohnen_temp.csv

lawdome_temp.csv

AntarcticaBoreholeData/SipleDome

3 files sampled for this table

AntarcticaBoreholeData/TaylorDome

2 files sampled for this table

AntarcticaBoreholeData/Unicorn

4 files sampled for this table

AntarcticaBoreholeData/Vostok

2 files sampled for this table

AntarcticaBoreholeData/WAISDivide

2 files sampled for this table

AntarcticaBoreholeData/WhilliansIceStream

5 files sampled for this table

Source Cooperative product page

providers: role [producer] for 'Various Antarctic research programs and institutions' is an AI guess — Multiple original publications cited per site; no README or explicit provider statement available to identify a single authoritative producer. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [AntarcticaBoreholeData.Lat (latlon), AntarcticaBoreholeData.Lon (latlon), AntarcticaBoreholeData.Location (placename), AntarcticaBoreholeData.Region (placename)]. Add bbox once the placename → bbox gazetteer covers it.


ISMIP6 Combined


ISMIP6

ISMIP6 (Ice Sheet Model Intercomparison Project 6) provides coordinated ice sheet model simulations forced by CMIP6 climate projections. The dataset covers Antarctica (south of ~52°S) with a temporal range of 2001–2101, stored as ~10,000 NetCDF files (~1.3 TB total). It supports multi-model comparison of ice sheet responses to standardized climate scenarios for sea-level rise projections.

ice-sheets climate-modeling climate model-intercomparison cmip6 cryosphere sea-level-rise polar

Dataset files (10334 x .nc)

Source Cooperative product page

providers: role [producer] for 'ISMIP6 Steering Committee / Contributing Ice Sheet Modeling Groups' is an AI guess — ISMIP6 is a community-wide project with dozens of contributing modeling centers; no specific institutional provider is stated in the available metadata.. Verify before flipping to _state: reviewed.


xOPR STAC

xOPR (Open Polar Radar) is a STAC catalog of airborne ice-penetrating radar data collected over polar regions, covering both Arctic and Antarctic hemispheres from 1993 to 2025. Each item references radar flight segments with associated CSARP-processed products (standard, quicklook, layer, MVDR, MUSIC algorithms) and flight path geometry.

remote-sensing radar earth-observation ice-sheet cryosphere polar airborne stac

stac.parquet

assets

STRUCT(CSARP_layer STRUCT(href VARCHAR, "type" VARCHAR), CSARP_qlook STRUCT(href VARCHAR, "type" VARCHAR), CSARP_standard STRUCT(href VARCHAR, "type" VARCHAR), "data" STRUCT(href VARCHAR, "type" VARCHAR), flight_path STRUCT(href VARCHAR, "type" VARCHAR), thumbnail STRUCT(href VARCHAR, "type" VARCHAR))

bbox

STRUCT(xmin DOUBLE, ymin DOUBLE, xmax DOUBLE, ymax DOUBLE)

[geometry] Bounding box struct (xmin/ymin/xmax/ymax) in EPSG:4326

geometry

[geometry] Flight segment footprint in EPSG:4326

links

STRUCT(href VARCHAR, rel VARCHAR, "type" VARCHAR)[]

[placename] Polar hemisphere (north/south) of the collection

Source Cooperative product page

providers: role [producer, processor] for 'CReSIS / University of Kansas' is an AI guess — Inferred from CSARP processing product names and opr:provider field; no explicit provider named in README. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [stac.geometry (geometry), stac.bbox (geometry), stac.opr:hemisphere (placename)]. Add bbox once a placename → bbox gazetteer is available.


Zarr-aggregator

Lambda builds for zagg


WRI/Google DeepMind Global Drivers of Forest Loss (1km)

This dataset maps the dominant driver of tree cover loss globally from 2001 to 2025 globally. Produced by the World Resources Institute (WRI) and Google Deepmind. Using satellite imagery and a neural network to classify seven categories: permanent agriculture, hard commodities, shifting cultivation, logging, wildfires, settlements and infrastructure, and other natural disturbances.

Source & citation: Zenodo record 14163025; also on the WRI Data Explorer.

License: CC-BY 4.0 — please cite Sims et al. 2025, Environmental Research Letters (https://doi.org/10.1088/1748-9326/add606) and the Zenodo concept DOI.


Global Natural and Planted Forests (30m)

30 m global map distinguishing natural forest, planted forest, and non-forest, from Xiao Yuelong (2024), "Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30m." Forest class is encoded as RGB colour: green = natural forest, yellow = planted forest, other = non-forest. This is an unmodified mirror of the Zenodo release (record 10701417, CC-BY 4.0, DOI 10.5281/zenodo.10701417), republished as cloud-optimized GeoTIFF tiles with a geoparquet STAC index and a virtualizarr/icechunk store for cloud-native access. Cite Xiao Yuelong (2024); modifications (if any) are flagged in the repository README.


JRC Tropical Moist Forests

Developed by the European Commissions Joint Research Centre (JRC), this dataset maps forest cover change in tropical moist forests (TMF) using 43 years of Landsate time series. At 30m resolution, depicting TMF extent, disturbances (deforestation and degradation), and post-deforestation recovery (regrowth).


Spring-2026


A large-scale dataset of 439,001 field polygons across Vietnam and Cambodia, digitized from satellite imagery and organized into 62 tiles of approximately 5×5 km. Field boundaries were produced through a rigorous multi-step quality control process and distributed as fiboa GeoParquet. Originally published as the AI4SmallFarms dataset for crop field delineation research in Southeast Asian smallholder farming contexts.

field-boundaries smallholder-farming agriculture crop-classification remote-sensing southeast-asia

Field Boundaries

Source Cooperative product page

## README.md
# Field boundaries for Cambodia and Vietnam (AI4SmallFarms)

The large-scale data set consists of 439,001 field polygons divided into 62 tiles of approximately 5×5 km distributed across Vietnam and Cambodia, covering a range of fields and diverse landscape types.
The field polygons have been meticulously digitized from satellite images, following a rigorous multi-step quality control process and topological consistency checks.
It has been converted to a fiboa GeoParquet file.

- **Source Data Provider:** [Persello, C., Grift, J., Fan, X., Paris, C., Hansch, R., Koeva, M., & Nelson, A. (2023). AI4SmallFarms: A Dataset for Crop Field Delineation in Southeast Asian Smallholder Farms. IEEE Geoscience and Remote Sensing Letters, 20, 1-5. Article 2505705](https://doi.org/10.1109/LGRS.2023.3323095)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/ai4sf/ai4sf.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/ai4sf/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/ai4sf/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/ai4sf/ai4sf.pmtiles)

## Lineage

- Data downloaded on 2024-08-13 from the source.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.6.0.


Crop Fields for Austria

Crop field boundaries for Austria from the INVEKOS system, recording all field uses submitted for Common Agricultural Policy funding. Each field is a contiguous area with a single crop type and uniform management requirements, digitized as polygons or points.

field-boundaries crop-classification agriculture land-parcels lpis

Dataset files (5 x .parquet)

Source Cooperative product page


Field boundaries for Austria

Provides ca. 1,300,000 official field boundaries for Austria.
Converted from the INVEKOS Referenzen Österreich 2021 GeoPackage to a fiboa GeoParquet file.

The layer includes all reference parcels ("Referenzparzellen") defined by the paying agency Agrarmarkt Austria and recorded landscape elements.

austria
fiboa
field boundaries
invekos
geoparquet
vector
pmtiles


Field boundaries for Flanders, Belgium (2021)

Official field boundaries for approximately 588,000 agricultural parcels in Flanders, Belgium, including parcel specialization typology. Converted to fiboa GeoParquet format from data provided by the Department of Agriculture and Fisheries, with specialization categories including arable farming, horticulture, livestock, and mixed farms.

field-boundaries agriculture land-parcels land-use arable-land

Agentschap Landbouw & Zeevisserij (Government)

Field Boundaries

Source Cooperative product page

## README.md
# Field boundaries for Flanders, Belgium

Provides ca. 588.000 official field boundaries from Flanders in Belgium.
It has been converted to a fiboa GeoParquet file from a file that was provided by the Agentschap Landbouw & Zeevisserij.

- **Source Data Provider:** [Agentschap Landbouw & Zeevisserij](https://landbouwcijfers.vlaanderen.be/open-geodata-landbouwgebruikspercelen)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** [Licentie modellicentie-gratis-hergebruik/v1.0](https://data.vlaanderen.be/id/licentie/modellicentie-gratis-hergebruik/v1.0)

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/be-vlg/be_vlg.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/be-vlg/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/be-vlg/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/be-vlg/be_vlg.pmtiles)

## Lineage

- Data downloaded on 2024-08-13 from https://www.landbouwvlaanderen.be/bestanden/gis/Landbouwgebruikspercelen_2023_-_Definitief_(extractie_28-03-2024)_GPKG.zip.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.6.0.


Belgium Wallonia: Parcellaire Agricole Anonyme

Official field boundaries for the Wallonia region of Belgium, comprising 341,968 agricultural parcels from the Parcellaire Agricole Anonyme (PAA) dataset. Represents anonymous agricultural plots managed under the EU Common Agricultural Policy by the Wallonia Paying Agency, including crop name and code attributes. Sourced from the INSPIRE Geoportal and converted to fiboa GeoParquet format in EPSG:3035 projection.

field-boundaries agriculture crop-classification lpis land-parcels land-use wallonia

Inspire Geoportal of the European Commission

Field Boundaries

Source Cooperative product page

## README.md
# Field boundaries for Belgium, Wallonia

Provides 341968 official field boundaries from Belgium, Wallonia.
It has been converted to a fiboa GeoParquet file from data obtained from INSPIRE Geoportal by the European Commission.

- **Source Data Provider:** [INSPIRE Geoportal by the European Commission](https://inspire-geoportal.ec.europa.eu/)
- **Converted by:** Ivor Bosloper
- **License:** No conditions apply to access and use. Distributed through Inspire guidelines
- **Projection:** EPSG:3035

---

- [Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/be-wa/be_wa.parquet)
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/be-wa/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/be-wa/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/be-wa/be_wa.pmtiles)

## Columns

| Property               | **Data Type** | Description |
|------------------------|---------------|-------------|
| crop_name              | string        |             |
| crop_code              | string        |             |
| id                     | string        |             |
| determination_datetime | timestamp     |             |

## Lineage

- Data downloaded on 2024-11-10 from https://geoservices.wallonie.be/geotraitement/spwdatadownload/get/2a0d9be0-ac3d-443e-9db0-a7cfb0f128e2/LU_ExistingLandUse_SIGEC2022.gml.zip?blocksize=0 .
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.7.0

providers: role [producer, licensor] for 'Service Public de Wallonie (Wallonia Paying Agency)' is an AI guess — Inferred as producer/licensor from the source download URL (geoservices.wallonie.be) and PAA dataset description; not explicitly named as such in the README.. Verify before flipping to _state: reviewed.

providers: role [host] for 'INSPIRE Geoportal, European Commission' is an AI guess — Listed as 'Source Data Provider' in the README but functions as a distribution/discovery portal rather than the original data producer.. Verify before flipping to _state: reviewed.


Field Boundaries for Luís Eduardo Magalhães in the west of Bahia state, Brazil

Monthly field boundaries and land use information about 1854 fields from Luís Eduardo Magalhães (LEM) and other municipalities in the west of Bahia state, Brazil. It has been converted to a fiboa GeoParquet file.

brazil
geoparquet
fiboa
field boundaries


Croatian Agricultural Spatial Data

Official agricultural field boundaries for the Republic of Croatia from the ARKOD land-use registry, comprising 1,346,655 parcels maintained by the Agency for Payments in Agriculture, Fisheries and Rural Development. Each parcel represents an uninterrupted area of agricultural land cultivated by a single farmer, classified by land-use type under Croatian agricultural ordinance. Data has been converted from a government GeoPackage to fiboa GeoParquet format.

agriculture field-boundaries land-use land-parcels crop-classification lpis

Agencija za plaćanja u poljoprivredi, ribarstvu i ruralnom razvoju (APPRRR)

Field Boundaries

Source Cooperative product page

Prostorni podaci i servisi

## README.md
# Field boundaries for Republic of Croatia

Provides 1346655 official field boundaries from Republic of Croatia.
It has been converted to a fiboa GeoParquet file from data obtained from
Agencija za plaćanja u poljoprivredi, ribarstvu i ruralnom razvoju objavila.

Data published by Republic of Croatia, in the ARKOD system. ARKOD is a record of the use of agricultural
land in the territory of the Republic of Croatia, which is maintained in digital graphic form by the Agency for Payments.

ARKOD plots represent uninterrupted areas of agricultural land cultivated by one farmer, classified according to
the type of use of agricultural land from gievn Annex I of the Ordinance on records of use of agricultural land.

Process for registration of agricultural land - vectorization of ARKOD polygons is carried out by the method of
photointerpretation of agricultural land on DOF bases on a scale of 1:5000 in branches of the Agency for Payments,
statements of farmers on the use of agricultural land with proof of ownership or possession.

- **Source Data Provider:** [Agencija za plaćanja u poljoprivredi, ribarstvu i ruralnom razvoju objavila](https://www.apprrr.hr/prostorni-podaci-servisi/)
- **Converted by:** Ivor Bosloper
- **License:** Not stated . Data is downloadable.
- **Projection:** EPSG:9122

---

- [Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/croatia/croatia.parquet)
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/croatia/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/croatia/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/croatia/croatia.pmtiles)

## Columns

| Property     | **Data Type** | Description          |
|--------------|---------------|----------------------|
| id           | string        |                      |
| crop_code    | int           | Crop code            |
| crop_name    | string        | Crop name (Croatian) |
| crop_name_en | string        | Crop name (English)  |
| area         | Real          | Area                 |

## Lineage

- Data downloaded on 2024-11-14 from https://www.apprrr.hr/wp-content/uploads/nipp/land_parcels.gpkg .
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.7.0


Field boundaries for Czech

Official field boundary dataset for the Czech Republic containing 426,584 agricultural parcels sourced from the Czech Ministry of Agriculture (Ministr Zemědělství) LPIS system. Each record includes crop code, crop name, field area, and determination date. Released under the EU INSPIRE directive and converted to fiboa GeoParquet format.

field-boundaries agriculture crop-classification lpis land-parcels arable-land land-use fiboa

Field Boundaries

Source Cooperative product page

## README.md
# Field boundaries for Czech

Provides 426584 official field boundaries from Czech.
It has been converted to a fiboa GeoParquet file from data obtained from Czech Ministry of Agriculture (Ministr Zemědělství).

- **Source Data Provider:** [Czech Ministry of Agriculture (Ministr Zemědělství)](https://mze.gov.cz/public/portal/mze/farmar/LPIS)
- **Converted by:** Ivor Bosloper
- **License:** CC-0 (Publicly distributed as part of the Inspire directive)
- **Projection:** EPSG:5514 or EPSG:4258

---

- [Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/czech/czech.parquet)
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/czech/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/czech/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/czech/czech.pmtiles)

## Columns

| Property               | **Data Type** | Description |
|------------------------|---------------|-------------|
| id                     | string        |             |
| block_id               | string        |             |
| crop_code              | string        |             |
| crop_name              | string        |             |
| area                   | float         |             |
| determination_datetime | timestamp     |             |

## Lineage

- Data downloaded on 2024-11-15 from https://mze.gov.cz/public/app/eagriapp/Files/geoprostor_zadosti23_2024-08-01_202409261243_epsg4258.zip.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.8.0


Field boundaries for Austria (2025)

Agricultural field boundary polygons for Austria for the 2025 reference year, derived from official LPIS (Land Parcel Information System) data and conforming to the fiboa 0.3.0+ specification. Each parcel record includes polygon geometry, harmonized crop type codes (HCAT), native and English crop names, and area metrics.

National agricultural agencies (various)

at-2025.parquet

geometry

[geometry] Field boundary polygon in EPSG:31287 (MGI / Austria Lambert)

bbox

[geometry] Bounding box struct (xmin/ymin/xmax/ymax) derived from the field polygon

be_vlg-2023.parquet

be_vlg-2024.parquet

be_vlg-2025.parquet

be_wal.parquet

bg.parquet

br_ba_lem.parquet

br_conab.parquet

ch.parquet

ch_2023.parquet

cz.parquet

de_bb.parquet

de_by.parquet

de_mv.parquet

de_nds-2025.parquet

de_nrw.parquet

dk-2024.parquet

ec_ro.parquet

ee-2024.parquet

es-2025.parquet

es-2025_11.parquet

fi.parquet

fr-2021.parquet

fr-2022.parquet

fr-2023.parquet

fr-2024.parquet

hr.parquet

ie-2024.parquet

jecam.parquet

lt.parquet

lv.parquet

nl-2025.parquet

pt-2023.parquet

se-2024.parquet

se.parquet

si-2024.parquet

si.parquet

sk.parquet

ukfields.parquet

us_ca_scm-2023.parquet

us_usda_cropland-2024.parquet

Source Cooperative product page

extent.spatial.bbox: omitted; spatial reference is categorical via [at-2025.geometry (geometry), at-2025.bbox (geometry)]. Add bbox once a placename → bbox gazetteer is available.


Official field block boundaries for the German federal states of Berlin and Brandenburg, comprising approximately 90,000 polygons derived from state-provided Shapefiles. Published in fiboa GeoParquet and PMTiles formats, each record includes area measurements, land-use category (hbn), validity dates, and the FLIK area identifier used in Germany's land parcel identification system.

field-boundaries agriculture land-parcels lpis arable-land land-use

Field Boundaries

Source Cooperative product page

Data licence Germany - attribution - Version 2.0

## README.md
# Field boundaries for Berlin / Brandenburg, Germany

Provides ca. 90.000 official field boundaries from the federal states of Berlin and Brandenburg in Germany.
It has been converted to a fiboa GeoParquet file from a Shapefile that was provided by the state.

- **Source Data Provider:** [Land Brandenburg](https://geobroker.geobasis-bb.de/gbss.php?MODE=GetProductInformation&PRODUCTID=9e95f21f-4ecf-4682-9a44-e5f7609f6fa0)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** [Data licence Germany – attribution – Version 2.0](https://www.govdata.de/dl-de/by-2-0)
- **Projection:** EPSG:25833 (UTM 33N)

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/de-bb/dfbk.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/de-bb/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/de-bb/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/de-bb/dfbk.pmtiles)

## Columns

| Property               | Data Type | Description                                                  |
| ---------------------- | --------- | ------------------------------------------------------------ |
| id                     | string    | Numerical internal identifier, same as `flik`.               |
| flik                   | string    | Area identifier for field blocks. Example: `DEBBLI0552020444` |
| area                   | float     | (Gross) Area in ha                                           |
| net_area               | float     | Net area in ha ("exkl. zugeordnetem Landschaftselement")     |
| perimeter              | float     | Length of the boundary in meters                             |
| determination_datetime | datetime  | Valid since the given date.                                  |
| expiry_datetime        | datetime  | Valid until the given date.                                  |
| fgue_jahr              | string    | Validity year                                                |
| hbn                    | string    | Category of main land use                                    |
| kreis_nr               | int32     | Identifier for the regional office for agriculture           |
| tk10_blatt             | string    | Paper of the 1:10,000 topographic map in which the center of where the center of gravity of the field block lies. Example: `4044SO` |

The following values are valid for `hbn`:

- 34A = non-agricultural area on former arable land, eligible under Art. 32 (2b (i)) of Regulation 1307/2013
- 34G = non-agricultural area on former grassland, eligible under Art. 32 (2b (i)) of Regulation 1307/2013
- AL = arable land
- AL-AF = Agroforestry on arable land
- DK = Permanent crops
- GL-ELP = Grassland management under "established local practices"
- GL = Grassland 40545 307026.8188
- GL-MO = Grassland created from arable land on moorland
- HE = Heath, no agricultural land

## Lineage

- Data downloaded on 2024-04-12 from https://data.geobasis-bb.de/geofachdaten/Landwirtschaft/dfbk.zip.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.3.2-dev.


Field boundaries for Mecklenburg-Western Pomerania, Germany

Official field block boundaries for the German federal state of Mecklenburg-Western Pomerania (MV), comprising approximately 70,000 polygons from the state's Ministry of Agriculture and Environment. The data is derived from the regional LPIS (Feldblock) register and converted to fiboa-compliant GeoParquet format.

field-boundaries lpis land-parcels agriculture

Field Boundaries

Source Cooperative product page

No restrictions apply

## README.md
# Field boundaries for Mecklenburg-Western Pomerania (MV), Germany

Provides ca. 70.000 official field boundaries from the federal state of Mecklenburg-Western Pomerania (MV) in Germany.
It has been converted to a fiboa GeoParquet file from a file that was provided by the state.

- **Source Data Provider:** [Ministerium für Landwirtschaft und Umwelt M-V](https://www.geodaten-mv.de/dienste/feldblock_atom?type=dataset&id=f18122c4-2585-4c22-9c48-9e960e8dhd34)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** No restrictions apply

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/de-mv/de-mv.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/de-mv/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/de-mv/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/de-mv/de-mv.pmtiles)

## Lineage

- Data downloaded on 2024-11-05 from https://www.geodaten-mv.de/dienste/gdimv_feldblock_wfs?SERVICE=WFS&VERSION=1.1.0&REQUEST=GetFeature&TYPENAME=mv:feldbloecke&OUTPUTFORMAT=shape-zip.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.7.0.


Official field boundary dataset for the German federal states of Lower Saxony, Hamburg, and Bremen, comprising approximately 550,000 field blocks. Data originates from ML/SLA Niedersachsen and has been converted from Shapefile to fiboa-compliant GeoParquet format. Each boundary includes land-use category codes (BNK), area, perimeter, and FLIK field-block identifiers.

field-boundaries agriculture lpis land-parcels land-use arable-land geoparquet fiboa

Field Boundaries

Source Cooperative product page

## README.md
# Field boundaries for Lower Saxony, Germany

Provides ca. 550.000 official field boundaries from the federal state of Lower Saxony in Germany.
It has been converted to a fiboa GeoParquet file from a Shapefile that was provided by the state.

- **Source Data Provider:** [ML/SLA Niedersachsen](https://sla.niedersachsen.de/landentwicklung/LEA/)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** [Data licence Germany – attribution – Version 2.0](https://www.govdata.de/dl-de/by-2-0)
- **Projection:** EPSG:25832 (UTM 32N)

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/de-nds/FB_NDS.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/de-nds/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/de-nds/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/de-nds/FB_NDS.pmtiles)

## Columns

| Property               | Data Type | Description                                                  |
| ---------------------- | --------- | ------------------------------------------------------------ |
| id                     | string    | Numerical internal identifier, same as `flik`.               |
| flik                   | string    | Area identifier for field blocks. Example: `DENILI0552020444` |
| determination_datetime | datetime  | Valid since the given date.                                  |
| area                   | float     | Area in ha                                                   |
| perimeter              | float     | Length of boundary in m                                      |
| ant_jahr               | string    | Year of claim, e.g. `2024`                                   |
| bnk                    | string    | Category of land use, short code. One of: AFF*, AL, AL*, DK, DK*, GL, GL*, MB, SO, SO* |
| bnk_txt                | string    | Textual representation of BNK in German                      |

## Lineage

- Data downloaded on 2024-04-12 from https://sla.niedersachsen.de/mapbender_sla/download/FB_NDS.zip.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.3.2-dev.
- Data: © ML/SLA Niedersachsen (2024), dl-de/by-2-0 (www.govdata.de/dl-de/by-2-0), Daten bearbeitet


Approximately 400,000 official agricultural field boundaries for the German federal state of North Rhine-Westphalia (NRW), sourced from Land Nordrhein-Westfalen and converted to fiboa GeoParquet format. Each record includes an INSPIRE-compliant identifier, FLIK field-block code, land use category (arable, grassland, permanent crops, etc.), and area in hectares. Data reflects the 2023 state survey.

field-boundaries agriculture land-use land-parcels lpis arable-land

Field Boundaries

Source Cooperative product page

## README.md
# Field boundaries for North Rhine-Westphalia (NRW), Germany

Provides ca. 400.000 official field boundaries from the federal state of North Rhine-Westphalia (NRW) in Germany.
It has been converted to a fiboa GeoParquet file from a shapefile that was provided by the state.

- **Source Data Provider:** [Land Nordrhein-Westfalen / Open.NRW](https://www.opengeodata.nrw.de/produkte/umwelt_klima/bodennutzung/landwirtschaft/)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** [Data licence Germany – attribution – Version 2.0](https://www.govdata.de/dl-de/by-2-0)
- **Projection:** EPSG:25832 (UTM 32N)

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/de-nrw/LFK-AKTI_EPSG25832.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/de-nrw/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/de-nrw/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/de-nrw/LFK-AKTI.pmtiles)

## Columns

| Property               | Data Type | Description                                                  |
| ---------------------- | --------- | ------------------------------------------------------------ |
| id                     | string    | Numerical internal identifier                                |
| inspire:id             | string    | INSPIRE-compliant identifier. Example: `https://geodaten.nrw.de/id/inspire-lc-dgl/landcoverunit/6467974` |
| flik                   | string    | Area identifier for field blocks. Example: `DENWLI0552020444` |
| determination_datetime | datetime  | Valid since the given date.                                  |
| nutz_code              | string    | Category of land use. A = Arable land, G = Permanent grassland, S = Other land, F = Eligible in the 2nd pillar, K = Permanent Crops |
| nutz_txt               | string    | Textual representation of nutz_code in German (`Ackerland`, `Dauergrünland`, `Sonstige Flächen`, `Förderfähig 2. Säule`,  `Dauerkultur`) |
| area                   | number    | Area in ha                                                   |

## Lineage

- Data downloaded on 2024-04-12 from https://www.opengeodata.nrw.de/produkte/umwelt_klima/bodennutzung/landwirtschaft/LFK-AKTI_EPSG25832_Shape.zip.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.3.2-dev.


Field boundaries for Saxony, Germany

Official field boundaries for the German federal state of Saxony (Sachsen), comprising approximately 95,500 agricultural parcels. The dataset is derived from a source file provided by the Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie and converted to the fiboa GeoParquet format.

field-boundaries agriculture lpis land-parcels arable-land land-use fiboa

Field Boundaries

Source Cooperative product page

## README.md
# Field boundaries for Saxony, Germany

Provides ca. 100.000 official field boundaries from the federal state of Saxony in Germany.
It has been converted to a fiboa GeoParquet file from a file that was provided by the state.

- **Source Data Provider:** [Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie](https://geoportal.sachsen.de/)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** [Germany - attribution - Version 2.0](https://www.govdata.de/dl-de/by-2-0)

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/de-sax/de-sax.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/de-sax/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/de-sax/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/de-sax/de-sax.pmtiles)

## Lineage

- Data downloaded on 2024-11-05 from https://www.smul.sachsen.de/gis-online/download/FBZ_ISS_Bereiche/gesamt_2024_RE.zip.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.7.0.


Approximately 200,000 official agricultural field block boundaries for the German federal state of Schleswig-Holstein, sourced from the state's open geodata catalog. Each record includes a FLIK identifier, area in hectares, determination date, and a main land-use category (HBN) covering arable land, permanent crops, grassland, and non-agricultural areas. Distributed as fiboa-compliant GeoParquet and PMTiles.

field-boundaries agriculture land-use lpis land-parcels arable-land

Field Boundaries

Source Cooperative product page

Data licence Germany - Zero - Version 2.0

## README.md
# Field boundaries for Schleswig-Holstein (SH), Germany

Provides ca. 200.000 official field boundaries from the federal state of Schleswig-Holstein (SH) in Germany.
It has been converted to a fiboa GeoParquet file from a GeoPackage that was provided by the state.

- **Source Data Provider:** [Land Schleswig-Holstein](https://sh-mis.gdi-sh.de/catalog/?lang=en#/datasets/iso/21f67269-780f-4f3c-8f66-03dde27acfe7)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** [Deutschland – Zero – Version 2.0](https://www.govdata.de/dl-de/zero-2-0)
- **Projection:** EPSG:4647 - ETRS89 / UTM zone 32N

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/de-sh/Feldbloecke_2024.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/de-sh/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/de-sh/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/de-sh/Feldbloecke_2024.pmtiles)

## Columns

| Property               | Data Type | Description                                                  |
| ---------------------- | --------- | ------------------------------------------------------------ |
| id                     | string    | Area identifier for field blocks, same as `flik`. Example: `DENWLI0552020444` |
| flik                   | string    | Area identifier for field blocks, same as `id`.              |
| area                   | float     | Area in ha                                                   |
| determination_datetime | datetime  | Valid since the given date.                                  |
| hbn                    | string    | Category of main land use (see below)                        |

Values for `HBN`:

- `Ackerland` (arable land)
- `Dauerkultur` (permanent crops)
- `Dauergrünland` (permanent grassland)
- `Nicht landwirtschaftliche förderfähige Flächen` (Areas not eligible for agricultural subsidies)
- `Sonstige nicht ldw. genutzte Flächen` (Other non-agricultural land)

## Lineage

- Data downloaded on 2024-04-12 from https://service.gdi-sh.de/SH_OpenGBD/feeds/Atom_SH_Feldblockfinder_OpenGBD/data/Feldbloecke_2024_GPKG.zip.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.3.2-dev.


Field boundaries for Saarland, Germany

Approximately 50,000 official agricultural field boundaries for the German federal state of Saarland (SL), sourced from the state's WFS instance and transformed to conform to the INSPIRE "Land Use" data model for IACS subsidy-application areas. Published as a fiboa-compliant GeoParquet file.

field-boundaries agriculture land-parcels lpis land-use fiboa

Field Boundaries

Source Cooperative product page

## README.md
# Field boundaries for Saarland (SL), Germany

Provides ca. 50.000 official field boundaries from the federal state of Saarland (SL) in Germany.
It has been converted to a fiboa GeoParquet file from multiple files that were provided by the state.

- **Source Data Provider:** [Ministerium für Umwelt, Klima, Mobilität, Agrar und Verbraucherschutz](https://geoportal.saarland.de)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** CC-BY-4.0

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/de-sl/de-sl.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/de-sl/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/de-sl/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/de-th/de-sl.pmtiles)

## Lineage

- Data downloaded on 2024-11-05 from 15 files received from the WFS instance of the state.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.7.0.


Field boundaries for Thuringia, Germany

Approximately 100,000 official agricultural field boundaries for the German federal state of Thuringia (TH), sourced from state authorities and published as fiboa-compliant GeoParquet. Each boundary includes FLIK parcel identifiers, area measurements, and administrative attributes.

field-boundaries agriculture land-parcels lpis arable-land land-use fiboa

Field Boundaries

Source Cooperative product page

## README.md
# Field boundaries for Thuringia (TH), Germany

Provides ca. 100.000 official field boundaries from the federal state of Thuringia (TH) in Germany.
It has been converted to a fiboa GeoParquet file from a file that was provided by the state.

- **Source Data Provider:** [Thüringer Landesamt für Landwirtschaft und Ländlichen Raum](https://geomis.geoportal-th.de/geonetwork/srv/ger/catalog.search#/metadata/D872F2D6-60BC-11D6-B67D-00E0290F5BA0)
- **Converted by:** [Matthias Mohr](https://mohr.ws)
- **License:** [Germany - attribution - Version 2.0](https://www.govdata.de/dl-de/by-2-0)

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/de-th/de-th.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/de-th/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/de-th/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/de-th/de-th.pmtiles)

## Lineage

- Data downloaded on 2024-08-13 from https://www.geoproxy.geoportal-th.de/download-service/opendata/agrar/DGK_Thue.zip.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.6.0.


Denmark Crop Fields (Marker)

Crop field (Marker) polygons for Denmark published by the Danish Ministry of Food, Agriculture and Fisheries, with records available from 2005 onwards. Each feature represents a single field boundary with an associated crop code and Danish-language crop name. Distributed as fiboa-compliant GeoParquet and PMTiles.

agriculture field-boundaries crop-classification lpis land-parcels arable-land land-use

Ministry of Food, Agriculture and Fisheries of Denmark

Field Boundaries

Source Cooperative product page

## README.md
# Denmark Crop Fields (Marker)

- **Source Data Provider:** The Danish Ministry of Food, Agriculture and Fisheries
- **Converted by:** Ivor Bosloper
- **Homepage:** https://landbrugsgeodata.fvm.dk/

The Danish Ministry of Food, Agriculture and Fisheries publishes
the open data "Marker" containing Crop Fields.

Crop field data is available from 2005 onwards and is typically published as
"preliminary data" from the start of a year (updated monthly). Field blocks are available
before the year starts, starting from 2008.

---

- [Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/denmark/denmark.parquet)
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/denmark/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/denmark/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/denmark/denmark.pmtiles)

### Columns

The Crop Field dataset has the following properties:

| Property   | **Data Type** | Description               |
|------------|---------------|---------------------------|
| id         | string        | Identifier                |
| area       | float         | Surface Area              |
| crop_code  | string        | Crop code                 |
| crop_name  | string        | Crop description (Danish) |

## Lineage

- Data downloaded on 2024-08-22 from https://landbrugsgeodata.fvm.dk/Download/Marker/Marker_2023.zip
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.6.0


Field boundaries for Latvia from EuroCrops (2021)

Official field boundaries for Latvia (2021), covering approximately 430,000 parcels. Derived from Latvian government data via the EuroCrops project, converted to fiboa GeoParquet with harmonized crop classification values using the HCAT extension.

field-boundaries agriculture crop-classification lpis land-parcels remote-sensing

Field Boundaries

Source Cooperative product page

## README.md
# Field Boundaries for Latvia

Provides around 430,000 official field boundaries of Lativa for the year 2021. It has been converted to a fiboa GeoParquet file from the Eurocrops Shapefile that provides crop classification values that are harmonized across Europe. The data was provided to Eurocrops under an open license from the Latvian government.
- **Source Data Provider**: [Eurocrops](https://github.com/maja601/EuroCrops/wiki/Latvia)
- **Converted by**: Eddie Choi
- **License**: CC-BY-4.0
- **Projection**: EPSG:3059

---

- **[Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/ec-lv/ec_lv.parquet)**
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/ec-lv/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/ec-lv/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/ec-lv/ec_lv.pmtiles)

## Columns

| Property               | Data Type | Description                                                  |
| ---------------------- | --------- | ------------------------------------------------------------ |
| id                     | string    | Identifier |
| determination_datetime | datetime  | Valid since the given date. |
| area                   | float     | Area in ha |
| year                   | uint16    | Year of when the data was collected. |
| parcel_id              | uint64    | Identifcation number of each parcel |
| crop_id                | uint16    | Crop type identifier |
| subsidy_type           | string    | Subsidy type |
| EC_NUTS3               | string    | The Nomenclature of Territorial Units for Statistics 3 (NUTS3) region |
| ec:translated_name     | string    | Part of the [HCAT extension](https://github.com/fiboa/hcat-extension0); the original crop name translated to English. |
| ec:hcat_name           | string    | Part of the [HCAT extension](https://github.com/fiboa/hcat-extension0); the machine-readable HCAT name of the crop |
| ec:hcat_code           | uint32    | Part of the [HCAT extension](https://github.com/fiboa/hcat-extension0); the 10-digit HCAT code indicating the hierarchy of the crop |

## Lineage

- Data downloaded on 2024-06-16 from  https://zenodo.org/records/8229128/files/LV_2021.zip?download=1.
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.5.0-dev.

providers: role [processor] for 'EuroCrops' is an AI guess — EuroCrops harmonized and redistributed the data; assumed processor role as the README credits them as source data provider. Verify before flipping to _state: reviewed.

providers: role [processor] for 'Eddie Choi' is an AI guess — Listed as converter to fiboa GeoParquet format; assumed processor role. Verify before flipping to _state: reviewed.


Spain Andalusia Crop fields

Official field boundaries for the Andalusia region of Spain, derived from SIGPAC (Geographic Information System for the Identification of Agricultural Plots), a public administrative register of agricultural parcels and their subdivisions. Contains 4,381,464 enclosures with crop classifications and administrative codes, converted to fiboa GeoParquet format.

field-boundaries agriculture crop-classification lpis land-parcels land-use

Field Boundaries

Source Cooperative product page

Pursuant to Law 37/2007 of 16 November on the reuse of public sector information and Law 3/2013 of 24 July approving the Statistical and Cartographic Plan of Andalusia 2013-2017, the geographic information of SIGPAC is made available to the public.

## README.md
# Field boundaries for Spain Andalusia

Provides 4381464 official field boundaries from Spain Andalusia.
It has been converted to a fiboa GeoParquet file from data obtained from Junta de Andalucia - La consejería de Agricultura, Pesca, Agua y Desarollo Rural.

- **Source Data Provider:** [Junta de Andalucia - La consejería de Agricultura, Pesca, Agua y Desarollo Rural](https://www.juntadeandalucia.es/organismos/agriculturapescaaguaydesarrollorural.html)
- **Converted by:** Ivor Bosloper
- **License:** https://www.juntadeandalucia.es/organismos/agriculturapescaaguaydesarrollorural/servicios/sigpac/visor/paginas/sigpac-descarga-informacion-geografica-shapes-provincias.html#toc-condiciones-de-uso-para-la-licencia-de-uso-comercial
- **Projection:** EPSG:4258

---

- [Download the data as fiboa GeoParquet](https://data.source.coop/fiboa/es-an/es_an.parquet)
- [STAC Browser](https://radiantearth.github.io/stac-browser/#/external/data.source.coop/fiboa/es-an/stac/collection.json)
- [STAC Collection](https://data.source.coop/fiboa/es-an/stac/collection.json)
- [PMTiles](https://data.source.coop/fiboa/es-an/es_an.pmtiles)

## Columns

| Property                | **Data Type** | Description |
|-------------------------|---------------|-------------|
| id                      | string        |             |
| admin_province_code     | string        |             |
| admin_municipality_code | string        |             |
| area                    | float         |             |
| crop:code               | string        |             |
| crop:name               | string        |             |
| crop:name_en            | string        |             |
| crop:code_list          | string        |             |
| determination_datetime  | timestamp     |             |
| admin:country_code      | string        |             |
| admin:subdivision_code  | string        |             |

## Lineage

- Data downloaded on 2024-12-24 from https://www.juntadeandalucia.es/ssdigitales/festa/agriculturapescaaguaydesarrollorural/2024/SP24_REC_04.zip .
- Converted to GeoParquet using [fiboa-cli](https://github.com/fiboa/cli), version 0.8.0