Evaluation and improvement of Copernicus HR-VPP product for crop phenology monitoring

Monitoring agricultural land with optical remote sensing offers a valuable tool for estimating crop yield and supporting decision-making for food security. Cropland phenology indicators, such as the start of season (SOS), the end of season (EOS), and the number of growing seasons per year, provide essential information for land managers. While established toolboxes like TIMESAT have been extracting phenological metrics from coarse remote sensing data for two decades, agricultural monitoring applications demand continuous time series of high-resolution data, made possible by the European Union’s Copernicus Sentinel-2 since 2015. Recently, the Copernicus Land Monitoring Service (CLMS) released the pan-European High-Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. We conducted the first comprehensive validation of the analysis-ready SOS and EOS metrics from the VPP dataset of the HR-VPP product over a large set of agricultural fields spanning 10 countries, 14 crop types and 164 growing seasons. Our results demonstrate that the VPP product of the HR-VPP dataset correlates well with the sowing ( r 2 = 0.75) and harvesting ( r 2 = 0.56) dates observed in situ . The biases differ between spring (SOS bias: 59 days, EOS bias: 3 days) and winter (SOS bias: 136 days, EOS bias: –44 days) crops, likely due to the suppression of the autumn vegetation signal in the plant phenology index (PPI) by a solar zenith angle-dependent gain factor. We show that other indicators from the HR-VPP Vegetation Indices (VIs) product and re-parameterization of TIMESAT or DATimeS toolboxes are more suitable for winter crop phenology monitoring. This study calls for researchers and practitioners to carefully evaluate the performance of analysis-ready products to ensure their suitability for specific applications, ultimately promoting informed decision-making in agricultural management and food security endeavours. • Unique georeferenced dataset of observed sowing and harvesting dates was collected. • HR-VPP VPP product showed good performance for most of the European crops. • HR-VPP VPP product had an unacceptably high SOS bias for winter crops. • NDVI and FAPAR are best for SOS retrieval. • Physically-based indices (PPI, LAI) are best for EOS retrieval.

Authors

Institutions

Publication Details

Journal
Computers and Electronics in Agriculture
Published
2025-02-28
DOI
https://doi.org/10.1016/j.compag.2025.110136
Citations
7
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
12.01

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation and improvement of Copernicus HR-VPP product for crop phenology monitoring

Egor Prikaziuk, Santiago Belda, Zhanzhang Cai, Joel Segarra et al.
7 citations
Computers and Electronics in Agriculture
Remote Sensing in Agriculture
12.01
article

Evaluation and improvement of Copernicus HR-VPP product for crop phenology monitoring

Egor Prikaziuk, Santiago Belda, Zhanzhang Cai, Joel Segarra, Katja Berger, Cláudio Silva Figueira, Lukas Valentin Graf, Jochem Verrelst, Dessislava Ganeva, Gerbrand Koren, Enrico Tomelleri
article en
7 citations

Abstract

Monitoring agricultural land with optical remote sensing offers a valuable tool for estimating crop yield and supporting decision-making for food security. Cropland phenology indicators, such as the start of season (SOS), the end of season (EOS), and the number of growing seasons per year, provide essential information for land managers. While established toolboxes like TIMESAT have been extracting phenological metrics from coarse remote sensing data for two decades, agricultural monitoring applications demand continuous time series of high-resolution data, made possible by the European Union’s Copernicus Sentinel-2 since 2015. Recently, the Copernicus Land Monitoring Service (CLMS) released the pan-European High-Resolution Vegetation Phenology and Productivity (HR-VPP) product suite. We conducted the first comprehensive validation of the analysis-ready SOS and EOS metrics from the VPP dataset of the HR-VPP product over a large set of agricultural fields spanning 10 countries, 14 crop types and 164 growing seasons. Our results demonstrate that the VPP product of the HR-VPP dataset correlates well with the sowing ( r 2 = 0.75) and harvesting ( r 2 = 0.56) dates observed in situ . The biases differ between spring (SOS bias: 59 days, EOS bias: 3 days) and winter (SOS bias: 136 days, EOS bias: –44 days) crops, likely due to the suppression of the autumn vegetation signal in the plant phenology index (PPI) by a solar zenith angle-dependent gain factor. We show that other indicators from the HR-VPP Vegetation Indices (VIs) product and re-parameterization of TIMESAT or DATimeS toolboxes are more suitable for winter crop phenology monitoring. This study calls for researchers and practitioners to carefully evaluate the performance of analysis-ready products to ensure their suitability for specific applications, ultimately promoting informed decision-making in agricultural management and food security endeavours. • Unique georeferenced dataset of observed sowing and harvesting dates was collected. • HR-VPP VPP product showed good performance for most of the European crops. • HR-VPP VPP product had an unacceptably high SOS bias for winter crops. • NDVI and FAPAR are best for SOS retrieval. • Physically-based indices (PPI, LAI) are best for EOS retrieval.

Computers and Electronics in AgricultureVol. 233
University of Alicante (ES), University of Lisbon (PT), Free University of Bozen-Bolzano (IT), Lund University (SE), Utrecht University (NL), Bulgarian Academy of Sciences (BG), Parc Científic de la Universitat de València (ES), Space Research and Technology Institute (BG), ETH Zurich (CH), Institut de Biologia Evolutiva (ES), GFZ Helmholtz Centre for Geosciences (DE), Institute of Crop Science (JP), Universitat de Barcelona (ES), University of Twente (NL)
Institut de Recherche pour le Développement, European Commission, Centre National d’Etudes Spatiales, Generalitat Valenciana, Centre National de la Recherche Scientifique, Bulgarian Academy of Sciences, Institut national des sciences de l'Univers
Zero hunger
Openalex Percentile: Top 2%
Remote Sensing in Agriculture
12.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.