PlotToSat : A tool for generating time-series signatures from Sentinel-1 and Sentinel-2 at field-based plots for machine learning applications
Miltiadou, Milto ; Grieve, Stuart ; Ruiz-Benito, Paloma ; Astigarraga, Julen LU
; Cruz-Alonso, Verónica
; Triviño, Julián Tijerín
and Lines, Emily R.
(2025)
In Environmental Modelling and Software
188.
- Abstract
PlotToSat offers a practical and time efficient way to the challenge of extracting time-series from multiple Earth Observation (EO) datasets at numerous plots spread across a landscape. This opens up new opportunities to understand and model various ecosystems. Regarding forest ecology, plot networks play a vital role in monitoring and understanding the dynamics of forest ecosystems. These networks often contain thousands of plots arranged systematically to represent an ecosystem. Combining field data collected at plots with EO time-series will allow us to better understand phenology and ecosystem composition, structure and distribution. Linking plot networks with EO data without PlotToSat is time consuming and computational expensive... (More)
PlotToSat offers a practical and time efficient way to the challenge of extracting time-series from multiple Earth Observation (EO) datasets at numerous plots spread across a landscape. This opens up new opportunities to understand and model various ecosystems. Regarding forest ecology, plot networks play a vital role in monitoring and understanding the dynamics of forest ecosystems. These networks often contain thousands of plots arranged systematically to represent an ecosystem. Combining field data collected at plots with EO time-series will allow us to better understand phenology and ecosystem composition, structure and distribution. Linking plot networks with EO data without PlotToSat is time consuming and computational expensive because plots are small and spread out, requiring data from multiple satellite tiles. PlotToSat processed a full year of multi-tile Sentinel-1 and Sentinel-2 data (estimated 18.3TB) at 15,962 plots from the fourth Spanish Forest Inventory in less than 24 h. PlotToSat, implemented using the Python API of Google Earth Engine, offers a new and unique workflow that is innovative due to its efficient, scalable and adaptable implementation. It supports Sentinel-1 and Sentinel-2 data, but its flexible design eases integration of additional EO datasets. New environmental modelling is expected to emerge facilitating EO time-series analyses and investigating interactive effects of environmental drivers.
(Less)
- author
- Miltiadou, Milto
; Grieve, Stuart
; Ruiz-Benito, Paloma
; Astigarraga, Julen
LU
; Cruz-Alonso, Verónica
; Triviño, Julián Tijerín
and Lines, Emily R.
- publishing date
- 2025-04
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Forest ecology, Plots networks, Scalability, Sentinel-1, Sentinel-2, Time-series
- in
- Environmental Modelling and Software
- volume
- 188
- article number
- 106395
- publisher
- Elsevier
- external identifiers
-
- scopus:86000594583
- ISSN
- 1364-8152
- DOI
- 10.1016/j.envsoft.2025.106395
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2025
- id
- 429538b4-af3a-48e1-955a-f47442f70e20
- date added to LUP
- 2025-03-25 13:20:15
- date last changed
- 2025-04-22 13:08:49
@article{429538b4-af3a-48e1-955a-f47442f70e20,
abstract = {{<p>PlotToSat offers a practical and time efficient way to the challenge of extracting time-series from multiple Earth Observation (EO) datasets at numerous plots spread across a landscape. This opens up new opportunities to understand and model various ecosystems. Regarding forest ecology, plot networks play a vital role in monitoring and understanding the dynamics of forest ecosystems. These networks often contain thousands of plots arranged systematically to represent an ecosystem. Combining field data collected at plots with EO time-series will allow us to better understand phenology and ecosystem composition, structure and distribution. Linking plot networks with EO data without PlotToSat is time consuming and computational expensive because plots are small and spread out, requiring data from multiple satellite tiles. PlotToSat processed a full year of multi-tile Sentinel-1 and Sentinel-2 data (estimated 18.3TB) at 15,962 plots from the fourth Spanish Forest Inventory in less than 24 h. PlotToSat, implemented using the Python API of Google Earth Engine, offers a new and unique workflow that is innovative due to its efficient, scalable and adaptable implementation. It supports Sentinel-1 and Sentinel-2 data, but its flexible design eases integration of additional EO datasets. New environmental modelling is expected to emerge facilitating EO time-series analyses and investigating interactive effects of environmental drivers.</p>}},
author = {{Miltiadou, Milto and Grieve, Stuart and Ruiz-Benito, Paloma and Astigarraga, Julen and Cruz-Alonso, Verónica and Triviño, Julián Tijerín and Lines, Emily R.}},
issn = {{1364-8152}},
keywords = {{Forest ecology; Plots networks; Scalability; Sentinel-1; Sentinel-2; Time-series}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Environmental Modelling and Software}},
title = {{PlotToSat : A tool for generating time-series signatures from Sentinel-1 and Sentinel-2 at field-based plots for machine learning applications}},
url = {{http://dx.doi.org/10.1016/j.envsoft.2025.106395}},
doi = {{10.1016/j.envsoft.2025.106395}},
volume = {{188}},
year = {{2025}},
}