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EuroCropsV2 - GSA Parquet Repository

Overview

This repository provides access to harmonized GeoSpatial Application (GSA) datasets across multiple European regions (NUTS levels), distributed in Apache Parquet format.

The data originates from national or regional GSAs and has been standardized to enable cross-country and temporal analysis of agricultural crop fields.

Files are hosted via FTP:

https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/DRLL/EuroCropsV2/gpqt/

Coverage

Spatial Coverage (NUTS regions)

The dataset includes the following NUTS regions:

be2, be3, bg, cz, de4, dea, dk, ee, es, fi, fr, ie, iti1, nl, si, sk

Temporal Coverage

File Structure

1. Yearly GSA Files

Each file represents one NUTS region and one year.

Naming convention:

<nuts>_<year>.parquet

Example:

fr_2018.parquet
de4_2020.parquet

2. Stack Files

Each stack file aggregates crop field information across multiple years for a given NUTS region.

Naming convention:

<nuts>_stack.parquet

Example:

fr_stack.parquet
de4_stack.parquet

Data Schema

Yearly GSA Tables (<nuts>_<year>.parquet)

Column Type Description
cropfield int4 Unique identifier of the crop field within the table
original_code varchar Crop code as defined in the original GSA source
area_ha float Area of the polygon in hectares
geom geometry Polygon geometry (CRS: EPSG:3035)

Notes

EuroCrops mapping table: https://github.com/Martincccc/EuroCropsV2/blob/main/data/cropcodemapping/eurocrops.csv

Stack Tables (<nuts>_stack.parquet)

These tables track crop field evolution over time.

Column Type Description
cropfield int4 Unique identifier of the stacked crop field
cf<YEAR> int4 Crop field ID in <nuts>_<YEAR> table
c<YEAR> varchar Crop code (original_code) in <nuts>_<YEAR> table
area_ha float Area of the polygon in hectares
geom geometry Polygon geometry (CRS: EPSG:3035)

Example columns:

cf2018, c2018, cf2019, c2019, ...

Data Access

Data can be accessed directly via FTP or programmatically.

Example (Python)

import pandas as pd

url = "https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/DRLL/EuroCropsV2/gpqt/fr_2018.parquet"
df = pd.read_parquet(url)

print(df.head())

Example: join yearly GSA data with the EuroCrops crop-code mapping

The EuroCrops ecosystem uses an HCAT-based harmonization of crop classes, including machine-readable crop names and codes. ([GitHub][1])

The example below reads one yearly Parquet layer as a GeoDataFrame, links it to the EuroCrops crop-code mapping, and adds hcat4_name to the output.

Python example

import pandas as pd
import geopandas as gpd

# Example input
nuts = "fr"
year = 2018

# Yearly GSA parquet
gsa_url = f"https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/DRLL/EuroCropsV2/gpqt/{nuts}_{year}.parquet"

# EuroCrops mapping table (raw GitHub URL)
mapping_url = (
    "https://github.com/Martincccc/EuroCropsV2/main/"
    "data/cropcodemapping/eurocrops.csv"
)

# Read geospatial parquet as GeoDataFrame
gdf = gpd.read_parquet(gsa_url)

# Read crop-code mapping table
mapping = pd.read_csv(mapping_url, dtype={"original_code": str, "nuts": str})

# Ensure join keys use the same type/casing
gdf["original_code"] = gdf["original_code"].astype(str)
gdf["nuts"] = nuts

mapping["original_code"] = mapping["original_code"].astype(str)
mapping["nuts"] = mapping["nuts"].astype(str).str.lower()

# Keep only the fields needed for the join
mapping_sel = mapping[["nuts", "original_code", "hcat4_name"]].drop_duplicates()

# Join mapping information to the yearly geodata
gdf_ec = gdf.merge(
    mapping_sel,
    on=["nuts", "original_code"],
    how="left"
)

# Example result
print(gdf_ec[["cropfield", "original_code", "hcat4_name", "area_ha", "geom"]].head())

Coordinate Reference System

All geometries are provided in:

EPSG:3035 (ETRS89 / LAEA Europe)

versions history

Contact / Attribution

This dataset is part of the EuroCropsV2 initiative under JRC open data.

Please cite appropriately when using the data. https://doi.org/10.2905/JRC.FX0BVKR https://essd.copernicus.org/preprints/essd-2025-752/