jonghanko/Germany_Barley_dataset_n_DL_Framework

Germany Barley (Yield, GPP, & ET) Dataset and Deep Learning Framework (2017–2021)

This repository contains the processed dataset and analysis scripts for spring-barley modeling across 13 German states (2017–2021), including yield, gross primary production (GPP), evapotranspiration (ET), and climate-change scenario outputs.

Overview

We combine MODIS-based remote sensing, AgERA5 meteorology, and CORDEX-Europe climate projections with the process-based Remote Sensing-integrated Crop Model (RSCM) and deep-learning / machine-learning regressors to simulate spatiotemporal barley yield, GPP, and ET at 500-m resolution.

The repository provides:

1. Processed dataset archives (~26 GB compressed):
   • Barley_DEU_dataset.tar.gz (~17 GB) — state-level MODIS, weather, LAI, RSCM growth, yield, GPP, ET, and climate-change outputs for 13 German states.
   • RSCM_Barley.tar.gz (~9 GB) — RSCM barley model parameter files, observation inputs, and simulation outputs used to build the hybrid modeling workflow.
2. Analysis scripts (unzipped): training, inference, and visualization code for LAI estimation, yield prediction, GPP simulation, ET simulation, and climate-change analyses.

Repository structure

<repo-root>/
├── README.md
├── Barley_DEU_dataset.tar.gz
├── RSCM_Barley.tar.gz
├── Scripts_DL_Climate_to_LAI/
├── Scripts_DL_Climate_to_LAI_CC/
├── Scripts_DL_Climate_n_LAI_to_Yield/
├── Scripts_DL_RSCM_sim_growth_n_climate_to_Yield/
├── Scripts_ML_GPP/
└── Scripts_ML_ET/

Barley_DEU_dataset.tar.gz

Top-level folder inside the archive: Barley_DEU_dataset/, with one subdirectory per state:

BadenW, Bayern, Brandenburg, Hessen, MecklenburgV, Niedersachsen, NordrheinW, RheinlandP, Saarland, Sachsen, SachsenA, SchleswigH, Thuringen

Typical contents per state include:

• weather/ — AgERA5-based daily meteorology resampled to the 500-m grid
• LAI_VIs/ — MODIS-derived vegetation indices and LAI-related inputs
• processed_RSCM_data/ — RSCM growth variables prepared for ML workflows
• data_LAI_geo_wx_2017_to_21/ — 120-day LAI + weather + geolocation .npy sequences (historical 2017–2021)
• data_LAI_geo_wx_CC2050_RCP26/, ..._CC2050_RCP85/, ..._CC2070_RCP26/, ..._CC2070_RCP85/, ..._CC2090_RCP26/, ..._CC2090_RCP85/ — climate-change perturbed LAI + weather inputs
• coord_n_DNN_sim_yield/ — reference and simulated yield arrays at the pixel level
• out_pixGro1_yr2017/ … out_pixGro1_yr2021/ — pixel-level RSCM growth outputs by year
• data_GPP_2017_to_21/, data_ET_2017_to_21/ — historical GPP and ET simulation outputs
• data_GPP_CC2050_RCP26_ML/, data_ET_CC2050_RCP26_ML/, … — ML-simulated GPP and ET under climate-change scenarios
• {State}_map/ — state boundary shapefiles for mapping
• vis/ — visualization outputs

Historical LAI/weather .npy files use shape (P, 120, 8) with channels:

[DOY1, LAI, Easting, Northing, DOY2, solar radiation, Tmax, Tmin]

RSCM_Barley.tar.gz

Top-level folder inside the archive: RSCM_Barley/, containing RSCM spring-barley model setup files, parameter tables, LAI observation files, and state/year simulation outputs used in the assimilation and hybrid yield workflows.

Script folders

• Scripts_DL_Climate_to_LAI/ — deep-learning LAI estimation from daily weather drivers (FFNN, LSTM, BiLSTM, GRU, Transformer) for each German state
• Scripts_DL_Climate_to_LAI_CC/ — climate-change LAI projection and seasonal / delta visualization
• Scripts_DL_Climate_n_LAI_to_Yield/ — yield prediction from climate + LAI inputs (walk-forward CV, CC summary plots)
• Scripts_DL_RSCM_sim_growth_n_climate_to_Yield/ — hybrid RSCM growth + climate yield modeling (temporal and spatiotemporal configurations)
• Scripts_ML_GPP/ — machine-learning GPP simulation from RSCM growth and weather variables, including CC application scripts
• Scripts_ML_ET/ — machine-learning ET simulation from RSCM growth and weather variables, including CC application scripts

Dataset details

• Spatial coverage: 13 German states — Baden-Württemberg, Bayern, Brandenburg, Hessen, Mecklenburg-Vorpommern, Niedersachsen, Nordrhein-Westfalen, Rheinland-Pfalz, Saarland, Sachsen, Sachsen-Anhalt, Schleswig-Holstein, and Thüringen
• Spatial resolution: 500 m
• Temporal range: 2017–2021 (historical); climate-change scenarios for the 2050s, 2070s, and 2090s under RCP 2.6 and RCP 8.5
• Key variables: leaf area index (LAI), above-ground biomass / growth, barley yield, GPP, ET, solar radiation, maximum and minimum air temperature, vegetation indices
• Reference yields: state-level official statistics disaggregated to the 500-m cropland grid following the RSCM-based downscaling workflow used in the associated study

Intended use

This dataset is suitable for research on regional-scale spring-barley yield prediction, GPP and ET modeling, remote-sensing-based agroecosystem monitoring, hybrid process-based + machine-learning modeling, and climate-change impact assessment for barley systems in Germany. It is intended for research and educational purposes.

Out-of-scope use

The 500-m reference yield maps were produced by disaggregating state-level totals in proportion to RSCM-simulated pixel yield and are not independent pixel-level observations. Quantitative accuracy statements should therefore be interpreted at the state-year aggregation level unless independently validated.

Climate-change outputs based on climate-only or ML-transfer configurations should be read as scenario analyses rather than calibrated forecasts. CO₂ fertilization effects and irrigation management are not fully represented in all workflow branches.

Installation and usage

Download the repository with the Hugging Face CLI:

huggingface-cli download jonghanko/Germany_Barley_dataset_n_DL_Framework --repo-type dataset --local-dir ./Germany_Barley_dataset

Extract the archives:

cd Germany_Barley_dataset
tar -xzf Barley_DEU_dataset.tar.gz
tar -xzf RSCM_Barley.tar.gz

Run the scripts in the relevant workflow directory. Deep-learning training scripts were developed with Python 3.10+ and PyTorch and require a CUDA-capable GPU for model training. Machine-learning GPP/ET scripts use scikit-learn, XGBoost, and LightGBM.

Example:

cd Scripts_DL_Climate_n_LAI_to_Yield
python main.py

License

Dataset archives (Barley_DEU_dataset.tar.gz, RSCM_Barley.tar.gz): CC BY 4.0.

Scripts (Scripts_*/ directories): MIT License (see LICENSE in each script directory, where provided).

Citation

If you use this dataset or the accompanying scripts, please cite the associated manuscript (in preparation) and the underlying data providers below.

@article{Ko2026GermanyBarley,
  author  = {Ko, Jonghan and Shawon, Ashifur Rahman and Jeong, Seungtaek and Shin, Taewhan},
  title   = {Germany spring barley dataset and DL framework (2017--2021)},
  journal = {Nature Communications},
  year    = {2026},
  note    = {In preparation}
}

Please also cite the underlying data providers:

• MODIS MOD09A1 / MOD11A1: NASA LP DAAC
• AgERA5: Boogaard et al. (2020), ECMWF Copernicus Climate Change Service
• CORDEX-Europe: Copernicus Climate Change Service, doi:10.24381/cds.bc91edc3

Contact

Jonghan Ko (corresponding author)
Applied Plant Science, Chonnam National University, Gwangju, South Korea
Email: jonghan.ko@jnu.ac.kr

Acknowledgements

See the acknowledgements section of the associated manuscript.