PaddockTimeSeries (PaddockTS)

Paddock-scale time-series analysis of Australian agricultural land, end-to-end from a single bounding box.

Built at the Borevitz Lab, Australian National University for ecologists, agronomists, and remote-sensing researchers who want a reproducible pipeline for Sentinel-2 imagery to paddock (or field) boundaries and paddock-level summaries of greenness, ground cover, and phenology.

📚 Documentation: https://johnburley3000.github.io/paddocktimeseries/

Preprint: Burley et al. (2026), EarthArXiv

Code-free web tool: paddocktimeseries.net by Yasar Adeel Ansari

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What it does

Give PaddockTS a time and region of interest (troi). It produces:

• Reusable Sentinel-2 raster time series — multispectral observations are stored in a spatial, time-indexed cache managed by pysentinel2. The raster stack can be reopened for other analyses, with cloud masking and spectral indices applied on read.
• Paddock boundaries — automatic field-boundary detection using Segment Anything through segment-geospatial (samgeo), applied to an NDWI Fourier-feature image. The resulting GeoPackage contains paddock geometries, identifiers, areas, and shape compactness.
• Compact paddock-level time series — median surface reflectance, spectral indices, and fractional cover for each paddock and observation date, stored as Zarr datasets on (paddock, time). PaddockTS also produces resampled, gap-filled, and smoothed time series.
• Spectral indices and fractional cover — PaddockTS computes NDVI, CFI, NIRv, NDTI, and CAI. It also estimates bare ground (bg), green vegetation (pv), and non-green vegetation (npv) using a TFLite model adapted from fractionalcover3.
• Phenology metrics — start, peak, and end of season, together with seasonal amplitudes and integrals, calculated for each paddock and year using a vendored version of phenolopy.
• Environmental context — Copernicus 30 m elevation and derived terrain variables; OzWALD and SILO daily climate data; and SLGA 90 m soil properties, all matched to the same area of interest.
• Plots, videos, and reports — true-colour and fractional-cover time-lapse videos, paddock calendar plots, phenology curves, climate and terrain panels, and a combined PDF report.

Alternatively, supply your own paddock boundaries, optionally labelled with management categories or outcomes. PaddockTS can analyse these instead of, or alongside, automatically segmented paddocks.

Shared data stores reuse previously downloaded observations across overlapping areas and dates. Derived PaddockTS intermediates are reused when the area and date range match. Incomplete cached writes are detected and rebuilt on the next run.

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Install

Conda + pip (recommended)

The conda environment provides the native stack (GDAL/PROJ/GEOS, PyTorch, TensorFlow, Segment Anything); pip install . then pulls the lab packages — the troi core and the five data stores — from PyPI, pinned to the releases tested with this version:

git clone https://github.com/johnburley3000/paddocktimeseries.git
cd paddocktimeseries
conda env update -n paddockts -f environment.yml
conda activate paddockts
pip install .

PaddockTS targets Python ≥ 3.11.

Hardened-kernel note (Fedora / recent glibc). If import tensorflow fails with "cannot enable executable stack as shared object requires", your kernel enforces non-executable stacks and conda-forge's TensorFlow libraries request one. Clear the flag once (the libraries are in your env, so no root needed):

conda install -n paddockts -c conda-forge patchelf   # or use execstack
find "$CONDA_PREFIX" -name '*.so*' -exec sh -c \
  'execstack -c "$1" 2>/dev/null || true' _ {} \;

Most Linux systems don't hit this.

From source (development)

git clone https://github.com/thestochasticman/troi.git
git clone https://github.com/thestochasticman/pysentinel2.git
git clone https://github.com/thestochasticman/pysilo.git
git clone https://github.com/thestochasticman/pyozwald.git
git clone https://github.com/thestochasticman/pycopdem.git
git clone https://github.com/thestochasticman/pyslga.git
git clone https://github.com/johnburley3000/paddocktimeseries.git
cd paddocktimeseries
conda env update -n paddockts -f environment.yml   # native + scientific stack
conda activate paddockts
pip install --no-deps -e ../troi -e ../pysentinel2 -e ../pysilo \
            -e ../pyozwald -e ../pycopdem -e ../pyslga -e .

Configure (optional)

Default output and cache directories are ~/Documents/Troi-Outputs and ~/Downloads/Troi-Tmp. Override and add credentials by creating ~/.config/Troi.json:

{
  "out_dir": "/data/paddockts/outputs",
  "tmp_dir": "/data/paddockts/tmp",
  "email": "you@example.org",
  "tern_api_key": "<your-tern-key>"
}

Settings can also come from environment variables (TROI_OUTDIR, TROI_TMPDIR, TROI_EMAIL, TROI_TERN_KEY).

Credentials:

• email is required only by the SILO climate stage
• tern_api_key is required only by the SLGA soils stage — generate one at https://account.tern.org.au/

The Sentinel-2 → PaddockTS chain itself works without any credentials.

You can also pass configuration directly to Troi via a Config object — see the Getting started page.

---

Runnable demos

Three Jupyter notebooks under demo/ walk through the most common workflows:

• demo/01_quickstart.ipynb — bbox + dates → get_outputs(troi) → review the calendar / phenology / PDF.
• demo/02_pipeline_stages.ipynb — call each Sentinel-2 stage individually, inspect intermediate outputs.
• demo/03_custom_paddocks.ipynb — bring your own paddock boundaries and skip SAM.

jupyter lab demo/

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Tests

The offline unit suite under tests/ verifies the index math, fractional-cover unmixing, per-paddock median aggregation, smoothing, phenology metrics, and the caching contract — all against synthetic inputs, with no network access or credentials. It runs on every push via GitHub Actions:

pip install -e '.[tests]'
pytest

End-to-end acceptance scripts that exercise the full pipeline against the live data services live in test/ — see test/about_testing.md.

---

Quick example

from datetime import date
from troi.troi import Troi
from PaddockTS.get_outputs import get_outputs

troi = Troi(
    bbox=[148.36265, -33.52606, 148.38265, -33.50606],  # [W, S, E, N]
    start=date(2020, 1, 1),
    end=date(2021, 12, 31),
    stub="my_first_run",
)

get_outputs(troi)

This kicks off both pipelines (Sentinel-2 → PaddockTS and Environmental) in parallel and renders a live dashboard. The next get_outputs(troi) on the same Troi skips every cached step.

Outputs land under ~/Documents/Troi-Outputs/<stub>/:

File	What's in it
<stub>_paddocks.gpkg	Segmented paddock polygons + area_ha + compactness
<stub>_paddockTS.zarr	Per-paddock medians for every band + index, on (paddock, time)
<stub>_paddockTS_<year>.zarr	Yearly slices with a DOY coordinate
<stub>_sentinel2.mp4	True-colour Sentinel-2 timeline
<stub>_fractional_cover.mp4	Bare/green/non-green RGB timeline
<stub>_calendar_<year>_p01.png	Per-paddock thumbnail calendar
<stub>_phenology_p01.png	SoS/PoS/EoS curves per paddock per year
<stub>_topography.png	Elevation, slope, aspect, flow accumulation
<stub>.pdf	Stitched report combining every plot

---

Bring your own paddocks

If you already have field boundaries (QGIS export, cadastral layer, previous run), skip SAM segmentation and use them directly.

Importantly, tell build_from_paddocks() which column of your .gpkg specifies paddock name using label_col="".

from datetime import date
from troi.troi import Troi
from PaddockTS.get_outputs import get_outputs

paddocks_fp = "/path/to/paddocks.gpkg"  # .gpkg, .shp, or .geojson

troi = Troi.build_from_paddocks(
    paddocks_filepath=paddocks_fp,
    start=date(2024, 1, 1),
    end=date(2024, 12, 31),
    stub="my_farm",
    label_col="paddock_name",
)

get_outputs(
    troi,
    paddocks_filepath=paddocks_fp,
    skip_sam=True,
    label_col="paddock_name",
)

---

Pipeline at a glance

get_outputs(troi) runs two pipelines on parallel threads. The Sentinel-2 chain segments paddocks and builds the per-paddock time series, phenology, and plots; the Environmental chain pulls terrain, climate, and soils. Both feed the final stitched PDF.

flowchart TD
    Q["<b>Troi</b><br/>bbox · dates · stub"] --> GO(["get_outputs(troi)"])

    GO -->|"thread 1 · Sentinel-2 → PaddockTS"| D["Download + clean<br/>Sentinel-2"]
    D --> IDX["Spectral indices<br/>NDVI · CFI · NIRv · NDTI · CAI"]
    D --> FC["Fractional cover<br/>bg · pv · npv"]
    D --> SAM["Segment paddocks<br/>(SAM)"]
    IDX --> TS["Per-paddock<br/>time series"]
    SAM --> TS
    TS --> YR["Yearly split<br/>+ DOY"]
    YR --> PH["Phenology<br/>SoS · PoS · EoS"]
    FC --> PLOT["Videos · calendar ·<br/>phenology plots"]
    SAM --> PLOT
    PH --> PLOT

    GO -->|"thread 2 · Environmental"| TER["Terrain DEM<br/>slope · aspect · TWI"]
    GO --> OZ["OzWALD<br/>daily climate"]
    GO --> SILO["SILO<br/>climate"]
    GO --> SLGA["SLGA<br/>soils"]
    TER --> EPLOT["Climate +<br/>terrain plots"]
    OZ --> EPLOT
    SILO --> EPLOT
    SLGA --> EPLOT

    D -.->|"clean cube<br/>(terrain plot waits)"| EPLOT
    PLOT --> PDF[["Stitched PDF report"]]
    EPLOT --> PDF

    classDef default color:#000;

The table below is the same chain, stage by stage:

Sentinel-2 → PaddockTS	Environmental
Download Sentinel-2 + clean	Download terrain (Copernicus DEM)
Compute spectral indices	Download OzWALD daily climate
Compute fractional cover	Download SILO climate
Sentinel-2 video	Download SLGA soils
Segment paddocks (SAM)	OzWALD plot
Sentinel-2 + paddocks video	SILO plot
Fractional cover video	Terrain plot
Fractional cover + paddocks video
Make paddock time series
Make yearly paddock time series
Estimate phenology
Calendar plot
Phenology plot
PDF report

Every stage is a standalone function — pick any subset, swap in your own segmentation, plug in your own phenology library. See the pipeline page for the full call-graph and per-stage caching behaviour.

---

Calling individual stages

from pysentinel2.cube import Cube
from PaddockTS.FractionalCover import compute_fractional_cover
from PaddockTS.PaddockSegmentation.get_paddocks import get_paddocks

cube = Cube(config=troi.config)
ds = cube.get_ds_troi(troi, clean=True,                # cloud-masked window
                      indices=("NDVI", "CFI", "NIRv", "NDTI", "CAI"))
fc = compute_fractional_cover(troi, ds_sentinel2=ds)   # bg / pv / npv
paddocks = get_paddocks(troi, ds_sentinel2=ds)         # GeoDataFrame

Every function loads its own inputs from the cache if you don't pass them, so you can call them out of order or in isolation.

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Data sources and acknowledgments

PaddockTS does not redistribute upstream data; it queries them on demand:

• Sentinel-2 ARD — Geoscience Australia Digital Earth Australia STAC.
• Copernicus DEM 30 m — AWS Open Data.
• OzWALD — Australian Water and Landscape Dynamics, hosted by ANU.
• SILO — Queensland Government's gridded climate dataset.
• SLGA — TERN / CSIRO Soil and Landscape Grid of Australia (TERN API key required).

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Citation

Please cite the associated EarthArXiv preprint when using PaddockTS in research:

Burley, J. T., Ansari, Y. A., Bradley, C., Norton, A., & Borevitz, J. (2026). PaddockTS: paddock-level satellite time series analysis of agroecosystem dynamics. EarthArXiv. https://doi.org/10.31223/X5821Z

Machine-readable citation metadata is provided in CITATION.cff.

Please also cite the upstream datasets and methods relevant to your analysis.

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License and attribution

PaddockTS is MIT-licensed — see LICENSE.

Vendored code

Third-party code shipped inside the package; see PaddockTS/LICENSES/ for full license texts:

• fractionalcover3 by Robert Denham — MIT. The TFLite unmixing models and the unmixing routine in PaddockTS.FractionalCover._unmix are adapted from this work.
• phenolopy by Lewis Trotter — Apache 2.0. Vendored verbatim under PaddockTS.Phenology._phenolopy (with minor NumPy 2.0 compatibility fixes documented in the file header) and used through PaddockTS.Phenology.estimate_phenology.
• DAESIM_preprocess by Christopher Bradley — MIT. Environmental data harvesting functions adapted in the pyozwald / pysilo / pyslga / pycopdem stores for downloading and processing climate, vegetation, soil, and topographic datasets.

Key runtime dependencies

Installed as regular dependencies, not vendored — please cite if relevant to your work:

• segment-geospatial (samgeo) by Qiusheng Wu — MIT. Wraps Segment Anything for geospatial use; drives the paddock segmentation stage. Cite: Wu & Osco (2023), J. Open Source Software.
• Segment Anything Model (SAM) by Meta AI Research — Apache 2.0. The underlying segmentation model.

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Contributing & support

• Bug reports / feature requests: GitHub Issues
• Documentation: https://johnburley3000.github.io/paddocktimeseries/
• Known failure modes: diagnostics.md (DEA STAC cold-start, GDAL HTTP auth)
• Maintainers: John T. Burley and Yasar Adeel Ansari