PaddockTS: paddock-level satellite time series analysis of agroecosystem dynamics
Abstract
PaddockTimeSeries (PaddockTS) is an open-source data analysis pipeline that aligns Earth observation data with agricultural fields, or paddocks—the core land management units where agricultural decisions are made and outcomes such as yield, phenology, productivity, and soil condition are recorded. PaddockTS enables scalable agricultural monitoring and research on how land management and environmental variation shape agroecosystem dynamics. For a user-defined location and date range in Australia, PaddockTS delineates paddock boundaries from Sentinel-2 time-series imagery using geospatial segmentation or accepts user-provided boundaries, aggregates Sentinel- 2 satellite time series within them, and returns analysis-ready datasets and graphics describing vegetation dynamics and derived seasonal features. It also retrieves environmental covariates, including terrain, soil, weather, and water-balance variables. By summarising vegetation dynamics at the paddock level, PaddockTS makes Earth observation data easier to integrate with management records, environmental conditions, and agricultural and ecological outcomes. Its outputs enable comparisons across paddocks, years, and environmental gradients and provide analysis-ready inputs for agroecosystem modelling and machine-learning prediction tasks. PaddockTS can be used as a programmable Python workflow for reproducible research or through a web interface for exploratory analysis without coding.
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BibTeX
@misc{burley2026paddockts,
title = {{PaddockTS: paddock-level satellite time series analysis of agroecosystem dynamics}},
author = {John T Burley and Yasar Adeel Ansari and Christopher Gregory Bradley and Alex Norton and Justin Borevitz},
howpublished = {EarthArXiv},
year = {2026},
month = sep,
doi = {10.31223/x5821z},
url = {https://eartharxiv.org/repository/view/14760/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - PaddockTS: paddock-level satellite time series analysis of agroecosystem dynamics
AU - John T Burley
AU - Yasar Adeel Ansari
AU - Christopher Gregory Bradley
AU - Alex Norton
AU - Justin Borevitz
PY - 2026
DA - 2026/09/02
PB - EarthArXiv
DO - 10.31223/x5821z
UR - https://eartharxiv.org/repository/view/14760/
AB - PaddockTimeSeries (PaddockTS) is an open-source data analysis pipeline that aligns Earth observation data with agricultural fields, or paddocks—the core land management units where agricultural decisions are made and outcomes such as yield, phenology, productivity, and soil condition are recorded. PaddockTS enables scalable agricultural monitoring and research on how land management and environmental variation shape agroecosystem dynamics. For a user-defined location and date range in Australia, PaddockTS delineates paddock boundaries from Sentinel-2 time-series imagery using geospatial segmentation or accepts user-provided boundaries, aggregates Sentinel- 2 satellite time series within them, and returns analysis-ready datasets and graphics describing vegetation dynamics and derived seasonal features. It also retrieves environmental covariates, including terrain, soil, weather, and water-balance variables. By summarising vegetation dynamics at the paddock level, PaddockTS makes Earth observation data easier to integrate with management records, environmental conditions, and agricultural and ecological outcomes. Its outputs enable comparisons across paddocks, years, and environmental gradients and provide analysis-ready inputs for agroecosystem modelling and machine-learning prediction tasks. PaddockTS can be used as a programmable Python workflow for reproducible research or through a web interface for exploratory analysis without coding.
ER -