Validation and high-resolution downscaling of Sentinel-2 biophysical variables using PlanetScope in vineyard and annual cropping systems
Abstract
Accurate estimation of vegetation biophysical variables, including the fraction of absorbed photosynthetically active radiation (fAPAR), fraction of vegetation cover (FVC), and leaf area index (LAI), is essential for crop monitoring and management. Although Sentinel-2 provides operational biophysical variable products at 20 m resolution, uncertainties remain across crop types, particularly in woody systems such as vineyards, and this resolution is often insufficient to capture within-field variability. This study aimed to (i) validate the Sentinel-2 Simplified Level-2 Prototype (SL2P) product over Mediterranean herbaceous and vineyard crops; and (ii) develop and assess a PlanetScope–Sentinel-2 fusion framework to generate 3-m, high-frequency maps of fAPAR, FVC, and LAI. SL2P showed strong agreement with in-situ reference data for all variables (fAPAR: R2 = 0.88, |bias| = 0.02, uncertainty = 0.07; FVC: R2 = 0.80, |bias| = 0.08, uncertainty = 0.09; LAI: R2 = 0.86, |bias| = 0.48, uncertainty = 0.62), with fAPAR showing the lowest absolute bias. To increase spatial and temporal detail while preserving physical consistency, four downscaling approaches were compared. DMS-DT provided a favorable compromise between predictive performance and computational efficiency (fAPAR: R2 = 0.73, |bias| = 0.03, uncertainty = 0.10; FVC: R2 = 0.67, |bias| = 0.11, uncertainty = 0.13; LAI: R2 = 0.75, |bias| = 0.52, uncertainty = 0.75), with residual correction improving performance. Overall, the proposed fusion framework enables 3-m, high-frequency monitoring of biophysical variables while maintaining consistency with Sentinel-2 SL2P products. The resulting products could provide a potentially useful data source for applications requiring fine-scale and frequent vegetation information, including irrigation management, yield prediction, and other precision-agriculture applications.
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BibTeX
@article{minuesa2026validation,
title = {{Validation and high-resolution downscaling of Sentinel-2 biophysical variables using PlanetScope in vineyard and annual cropping systems}},
author = {César Minuesa and Jordi Gené-Mola and Èlia Capdevila and Joaquim Bellvert},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1974500},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1974500},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Validation and high-resolution downscaling of Sentinel-2 biophysical variables using PlanetScope in vineyard and annual cropping systems
AU - César Minuesa
AU - Jordi Gené-Mola
AU - Èlia Capdevila
AU - Joaquim Bellvert
PY - 2026
DA - 2026/10/30
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1974500
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1974500
AB - Accurate estimation of vegetation biophysical variables, including the fraction of absorbed photosynthetically active radiation (fAPAR), fraction of vegetation cover (FVC), and leaf area index (LAI), is essential for crop monitoring and management. Although Sentinel-2 provides operational biophysical variable products at 20 m resolution, uncertainties remain across crop types, particularly in woody systems such as vineyards, and this resolution is often insufficient to capture within-field variability. This study aimed to (i) validate the Sentinel-2 Simplified Level-2 Prototype (SL2P) product over Mediterranean herbaceous and vineyard crops; and (ii) develop and assess a PlanetScope–Sentinel-2 fusion framework to generate 3-m, high-frequency maps of fAPAR, FVC, and LAI. SL2P showed strong agreement with in-situ reference data for all variables (fAPAR: R2 = 0.88, |bias| = 0.02, uncertainty = 0.07; FVC: R2 = 0.80, |bias| = 0.08, uncertainty = 0.09; LAI: R2 = 0.86, |bias| = 0.48, uncertainty = 0.62), with fAPAR showing the lowest absolute bias. To increase spatial and temporal detail while preserving physical consistency, four downscaling approaches were compared. DMS-DT provided a favorable compromise between predictive performance and computational efficiency (fAPAR: R2 = 0.73, |bias| = 0.03, uncertainty = 0.10; FVC: R2 = 0.67, |bias| = 0.11, uncertainty = 0.13; LAI: R2 = 0.75, |bias| = 0.52, uncertainty = 0.75), with residual correction improving performance. Overall, the proposed fusion framework enables 3-m, high-frequency monitoring of biophysical variables while maintaining consistency with Sentinel-2 SL2P products. The resulting products could provide a potentially useful data source for applications requiring fine-scale and frequent vegetation information, including irrigation management, yield prediction, and other precision-agriculture applications.
SN - 1664-462X
ER -