A Semi-Empirical Method for Estimating All-Sky Photosynthetically Active Radiation from Sentinel-2 for High-Resolution Land Surface Analysis

Photosynthetically active radiation (PAR) is a fundamental driver of terrestrial photosynthesis and a key input for light use efficiency-based estimates of gross primary productivity (GPP). However, existing PAR products are typically designed for regional to global applications and often remain spatially mismatched with the finer-resolution land surface variables now commonly derived from optical satellite observations. In this study, we present a semi-empirical framework for deriving daily clear-sky and all-sky PAR from Sentinel-2 Level-2A imagery. The approach combines solar geometry, daily extraterrestrial radiation, and simplified atmospheric transmittance parameterizations using Sentinel-2 aerosol, water vapor, and scene classification information to estimate clear-sky PAR, and further extends this formulation to all-sky conditions through a cloud-transmission factor derived from cloud probability to generate a spatially explicit PAR product aligned with Sentinel-2 observations. The resulting estimates are evaluated against flux tower observations from 172 AmeriFlux sites across North and South America for the period 2017–2024 and compared with MODIS MCD18, VIIRS VNP18, and CERES SYN1deg PAR products. The clear-sky Sentinel-2 formulation showed a moderate positive bias of 6.38 W m−2, while the all-sky cloud adjustment reduced the mean bias to −1.44 W m−2 with an RMSE of 23.53 W m−2 and correlation of r = 0.87. The largest improvements occurred in spring and summer seasons, when atmospheric attenuation has the strongest influence on the clear-sky estimates. MODIS and CERES all-sky PAR products achieved lower overall errors with RMSE of 17.60 W m−2 and 15.56 W m−2, respectively, but at substantially coarser spatial resolution. The proposed framework therefore provides a practical high-resolution approximation of daily PAR that is spatially consistent with Sentinel-2 observations. Rather than replacing dedicated radiative transfer-based products, the method is intended to support analyses in which PAR needs to be evaluated together with Sentinel-2 bands, vegetation indices, and other Sentinel-2-derived variables within a common observational framework.

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Publication Details

Journal
Remote Sensing
Published
2026-08-14
DOI
https://doi.org/10.3390/rs18162745
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

A Semi-Empirical Method for Estimating All-Sky Photosynthetically Active Radiation from Sentinel-2 for High-Resolution Land Surface Analysis

Josip Križan, Lindsey Sloat, Leandro Parente, Mustafa Serkan Ișık et al.
Remote Sensing
Remote Sensing in Agriculture
article

A Semi-Empirical Method for Estimating All-Sky Photosynthetically Active Radiation from Sentinel-2 for High-Resolution Land Surface Analysis

Josip Križan, Lindsey Sloat, Leandro Parente, Mustafa Serkan Ișık, Laerte Guimarães Ferreira, Karla Čmelar
article en

Abstract

Photosynthetically active radiation (PAR) is a fundamental driver of terrestrial photosynthesis and a key input for light use efficiency-based estimates of gross primary productivity (GPP). However, existing PAR products are typically designed for regional to global applications and often remain spatially mismatched with the finer-resolution land surface variables now commonly derived from optical satellite observations. In this study, we present a semi-empirical framework for deriving daily clear-sky and all-sky PAR from Sentinel-2 Level-2A imagery. The approach combines solar geometry, daily extraterrestrial radiation, and simplified atmospheric transmittance parameterizations using Sentinel-2 aerosol, water vapor, and scene classification information to estimate clear-sky PAR, and further extends this formulation to all-sky conditions through a cloud-transmission factor derived from cloud probability to generate a spatially explicit PAR product aligned with Sentinel-2 observations. The resulting estimates are evaluated against flux tower observations from 172 AmeriFlux sites across North and South America for the period 2017–2024 and compared with MODIS MCD18, VIIRS VNP18, and CERES SYN1deg PAR products. The clear-sky Sentinel-2 formulation showed a moderate positive bias of 6.38 W m−2, while the all-sky cloud adjustment reduced the mean bias to −1.44 W m−2 with an RMSE of 23.53 W m−2 and correlation of r = 0.87. The largest improvements occurred in spring and summer seasons, when atmospheric attenuation has the strongest influence on the clear-sky estimates. MODIS and CERES all-sky PAR products achieved lower overall errors with RMSE of 17.60 W m−2 and 15.56 W m−2, respectively, but at substantially coarser spatial resolution. The proposed framework therefore provides a practical high-resolution approximation of daily PAR that is spatially consistent with Sentinel-2 observations. Rather than replacing dedicated radiative transfer-based products, the method is intended to support analyses in which PAR needs to be evaluated together with Sentinel-2 bands, vegetation indices, and other Sentinel-2-derived variables within a common observational framework.

Remote SensingVol. 18(16)
World Resources Institute (US), Pioneer (United States) (US), Universidade Federal de Goiás (BR)
Openalex Percentile: Top 10%
Remote Sensing in Agriculture
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