| --- |
| language: |
| - en |
| license: other |
| license_name: upstream-product-terms |
| license_link: https://huggingface.co/datasets/PHENOBIID/CropDynamicsBench/blob/main/TERMS.md |
| tags: |
| - agriculture |
| - crop-yield |
| - earth-observation |
| - time-series |
| - weather |
| pretty_name: CropDynamicsBench |
| --- |
| |
| # CropDynamicsBench |
|
|
| Processed global data for weather-conditioned vegetation forecasting and |
| in-season yield assessment of maize, rice, soybean, and wheat. |
| NDVI and GPP span 1982--2016; yield history, ERA5-Land, and legacy LAI include |
| 1981--2016. This is the numeric data release, not a collection of model outputs. |
|
|
| ## Download |
|
|
| Files are losslessly compressed tar shards. Their members preserve the |
| `Data/` layout used by the processing code. `manifest.json` lists every member's |
| shape, dtype, size and SHA-256, as well as the checksum of each archive. |
| The release contains **2,691 files in 52 shards**: **5.48 GiB compressed** and |
| **44.40 GiB extracted**, including the optional SEAS5 supplement. Reserve about |
| 60 GiB for the download cache and extracted data together. |
| Select product prefixes in `shards/` or use the companion code repository's |
| `scripts/download_processed_data.py`. All shards can also be obtained with: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| snapshot_download(repo_id="PHENOBIID/CropDynamicsBench", repo_type="dataset", |
| local_dir="CropDynamicsBench_download") |
| ``` |
|
|
| The download contains archives, not automatically extracted NumPy arrays. |
| Use the verified extraction script from the code release to restore `Data/`. |
| For exact reproduction, pin a dataset commit rather than the moving `main` branch. |
|
|
| ## Contents and shapes |
|
|
| | Shard prefix | Contents | Typical shape | |
| |---|---|---| |
| | `crop_active` | Four-crop yield, aligned ERA5-Land, MIRCA-OS month mappings and areas | weather `[13,12,360,720]`; yield `[360,720]` | |
| | `era5_land` | Monthly 0.5-degree weather before crop-active packing | `[12,360,720]` per variable/year | |
| | `gdhy` | Four-crop annual yield with reordered longitudes | `[360,720]` | |
| | `ndvi` | Monthly and crop-active NDVI, availability and quality fractions | `[12,360,720]` | |
| | `gpp` | Monthly and crop-active GPP totals, daily rates and valid-area fractions | `[12,360,720]` | |
| | `lai` | Legacy monthly and crop-active LAI used for cohort support and comparisons | `[12,360,720]` | |
| | `seas5` | Optional ensemble-mean seasonal weather on its native 1-degree grid | `[6,180,360,3]` per initialization | |
|
|
| The 0.5-degree latitude and longitude arrays are shipped with the products: |
| latitude increases from -89.75 to 89.75, longitude from -179.75 to 179.75. |
| Do not replace crop-active slots by calendar-month indices. `src_rel.npy`, |
| `month_rel.npy`, `valid_rel.npy`, and `weight_rel.npy` define the packed support; |
| invalid slots and missing values must be masked before model training. |
| The mapping arrays have shape `[12,360,720]`: `month_rel` stores calendar |
| months 1--12 (0 for padding), `src_rel` stores zero-based source indices |
| 0--11 (255 for padding), and `valid_rel` is 1 for active slots. Active months |
| are packed in ascending calendar-month order. `weight_rel` is monthly growing |
| area divided by the sum of growing areas over valid months at that grid cell. |
| NDVI is dimensionless, yield is t/ha, and the selected GPP state is the daily |
| mean rate in gC m-2 day-1, not the monthly total. |
|
|
| ERA5-Land channel order is also supplied as `variable_order.txt`: |
| `d2m, t2m, stl1, stl2, swvl1, swvl2, swvl3, ssrd, pev, u10, v10, sp, tp`. |
| These are physical processed arrays; model normalization must be fitted on |
| the corresponding training partition, not all years. |
|
|
| ### Optional seasonal forecasts |
|
|
| SEAS5 contains 432 monthly initializations (1981--2016), six lead months and |
| 25-member ensemble means. Channels are `t2m, tp, ssrd`, in K, m/day, and |
| J/m2/day. Latitude descends from 89.5 to -89.5; longitude increases from |
| 0.5 to 359.5. These files are **native-grid forecasts**, not already |
| crop-aligned or bias-corrected model inputs. Use the separate SEAS5 crop-weather |
| interface and training-only calibration before comparisons. The main benchmark |
| uses realized ERA5-Land weather and is a retrospective conditional assessment. |
|
|
| ## Evaluation protocol |
|
|
| The companion `protocol.json` defines the temporal splits. Model fitting through |
| 2001, 2005 and 2009 is paired with evaluation in 2002--2004, 2006--2008 and |
| 2010--2016, respectively. The three blocks comprise 13 evaluation years. |
| Seeds are 42, 45 and 48; primary nominal hidden fractions are 10%, 30%, 50% |
| and 70%. Future hidden vegetation is a target, not a permitted model input. |
|
|
| ## Scope |
|
|
| All selected numeric arrays are preserved byte-for-byte. Duplicate smoke-test |
| directories, intermediate caches, private machine-specific audit logs, model |
| weights, papers and raw-provider archives are excluded. Generated release |
| checksums replace private audit paths, not the original processing history. |
| The full numeric release does not by itself supply every legacy training cache |
| or establish a fresh full-benchmark retraining reproduction. The companion |
| repository documents the verified sample inference and reconstruction stages. |
|
|
| ## Sources and terms |
|
|
| See `sources.json` for original provider records, versions, and attribution, |
| and [TERMS.md](TERMS.md) before reuse. This release does not place all upstream |
| products under a new blanket license. In particular, rEC-LUE GPP is a |
| model-derived productivity product, not an independent direct measurement. |
|
|
