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

Associated manuscript (2026-09). This repository supports the manuscript “Cross scale validation and error chain auditing of a hybrid crop model and deep learning framework for German winter barley,” prepared for submission to Ecological Informatics. The study treats the coupled crop-model–machine-learning workflow as an ecological information pipeline and evaluates it through independent district-scale validation, error-chain diagnosis, and an external atmospheric-aridity check. State-scale Nash–Sutcliffe efficiency (0.92; n = 64) declines to 0.41 across 1,259 district-years, demonstrating that aggregation can overstate the skill of spatially detailed products. All six projected national yield-change signals are smaller than the illustrative 1.65 t ha⁻¹ error-chain sensitivity bound. The 500-m reference yields remain model-derived products and are district-tested, not field-validated.

Reproducibility and access. The full processed dataset and deep-learning framework retain the Hugging Face DOI 10.57967/hf/9266. A machine-readable analysis-artifact bundle containing validation tables, error-chain diagnostics, run manifests, and figure-reproduction scripts is deposited in Zenodo under 10.5281/zenodo.22077671. The Zenodo record and metadata are public; its files are embargoed until August 24, 2027, or article acceptance, whichever is earlier. Private reviewer access is supplied through the journal submission system.

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:

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.
Analysis scripts (unzipped): training, inference, and visualization code for LAI estimation, yield prediction, GPP simulation, ET simulation, and climate-change analyses.
Revision_CEEA_2026/: cross-scale validation and error-chain audit materials — documentation, results tables, figures, and code (no large data; derived products only).

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/
└── Revision_CEEA_2026/          # audit docs, results, figures, analysis code

`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 (solar radiation, Tmax, Tmin)
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 (the LAI channel is the FFNN climate→LAI projection; see Revision_CEEA_2026/)
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 winter-barley model setup files, parameter tables, LAI observation files, and state/year simulation outputs used in the assimilation and hybrid yield workflows. The nationwide application directory RSCM_Barley/2017_to_2021_MODIS_Germany_Barley/{State}/class_map/ holds the per-year Thünen/Schwieder crop masks (DEU.winter.barley.*.bin, DEU.spring.barley.*.bin, DEU.winter.wheat.*.bin); the modeled pixels correspond to DEU.winter.barley. Calibration draws on winter-barley (Sites 5, 6) and, in smaller part, spring-barley (Site 4) eddy-covariance/field data from Baden-Württemberg (Kraichgau, Swabian Alb).

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

Crop: winter barley (Germany)
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 winter-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. Independent validation against harmonized statistics shows that Nash–Sutcliffe efficiency falls from 0.92 at state scale (n = 64) to 0.41 at district scale (n = 1,259 district-years). The product is therefore district-tested, not field-validated, and should not be presented as direct evidence of field-level accuracy.

Climate-change outputs should be read as scenario analyses rather than calibrated forecasts. The projection is a two-stage climate→LAI→yield emulator; its combined seed, cascade, and reference-product discrepancies exceed all six national-mean change signals. Precipitation and CO₂ changes are not propagated through the emulator, and irrigation management is not represented.

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) and Revision_CEEA_2026/ code: MIT License (see LICENSE in each script directory, where provided).

Third-party data used only as external references in Revision_CEEA_2026/ (not redistributed here): the German 397-district yield dataset (Düden, Nacke & Offermann 2024, Sci Data 11:95, OpenAgrar doi:10.3220/DATA20231117103252-0) and AgERA5 vapor pressure (Copernicus CDS). Please obtain these from their original providers under their own licenses.

Citation

If you use this dataset or the accompanying scripts, please cite both the dataset DOI and the associated manuscript.

@misc{Ko2026GermanyWinterBarleyDataset,
  author    = {Ko, Jonghan and Jeong, Seungtaek and Shawon, Ashifur Rahman and Shin, Taewhan and Feike, Til},
  title     = {Germany winter barley dataset and deep-learning framework (2017--2021)},
  year      = {2026},
  publisher = {Hugging Face},
  doi       = {10.57967/hf/9266},
  url       = {https://huggingface.co/datasets/jonghanko/Germany_Barley_dataset_n_DL_Framework}
}

@unpublished{Ko2026CrossScaleBarleyAudit,
  author = {Ko, Jonghan and Jeong, Seungtaek and Shawon, Ashifur Rahman and Shin, Taewhan and Feike, Til},
  title  = {Cross scale validation and error chain auditing of a hybrid crop model and deep learning framework for German winter barley},
  year   = {2026},
  note   = {Manuscript prepared for submission to Ecological Informatics}
}

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
German 397-district crop yields (validation): Düden, Nacke & Offermann (2024), Scientific Data 11:95, doi:10.1038/s41597-024-02951-8

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.

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