A site-specific survey for EO-based phenological monitoring in regenerative agriculture within LULUCF framework

A site-specific survey for EO-based phenological monitoring in regenerative agriculture within LULUCF framework

In environmental management, monitoring transitions toward regenerative agriculture (RA) supports carbon offset initiatives aligned with Regulation (EU) 2018/841. Current Land Use, Land Use Change, and Forestry (LULUCF) platforms primarily analyze macro-scale Earth Observation (EO) vegetation trends...

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Bibliographic Details
Main Authors: Marzia Gabriele, Raffaella Brumana, Nicola Genzano
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:European Journal of Remote Sensing
Subjects:
Lulucf
regenerative agriculture
gee
phenology
land monitoring
Online Access:https://www.tandfonline.com/doi/10.1080/22797254.2025.2515491
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Summary:In environmental management, monitoring transitions toward regenerative agriculture (RA) supports carbon offset initiatives aligned with Regulation (EU) 2018/841. Current Land Use, Land Use Change, and Forestry (LULUCF) platforms primarily analyze macro-scale Earth Observation (EO) vegetation trends, yet are increasingly enhancing ground-based data collection. This study integrates these approaches through a methodological workflow comprising: (1) a survey segment with a 30 × 30 m pixel sampling grid for landscape-scale trend assessment and sub-hectare Survey Validation Areas delineating specific RA management practices; and (2) an EO monitoring segment using Landsat 5, 7, and 8 time series, processed in R and Google Earth Engine (GEE) to model 30 m phenological dynamics, alongside 10 m Sentinel-2 NDVI 15-day Maximum Value Composites published via a GEE application (RegenAPP). Applied to an experimental RA site, La Junquera – Camp Altiplano (Murcia, Spain), the workflow enabled fine-scale analyses, identifying greening trends in no-till RA plots in contrast to browning in adjacent tilled organic fields. Sub-hectare analyses further detailed phenological patterns linked to specific RA practices. This integrated EO–Survey approach complements LULUCF assessments by coupling EO-derived vegetation analytics with targeted field validation, capturing spatial and temporal RA transition dynamics.
ISSN:2279-7254

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