Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models

Canopy chlorophyll content per ground area (CCC, g·m −2 ) is tightly related to vegetation photosynthesis and is a promising indicator of photosynthetic capacity. However, a global operational CCC product is not yet available. To fill this gap, we developed a two-step upscaling method to estimate global CCC from Sentinel-3 OLCI top-of-atmosphere (TOA) reflectance. In the first step, a physically-based PROSAIL-D inversion model produced accurate CCC maps from over 20,000 high-spatial resolution (1 m) airborne hyperspectral images collected across 50 sites within the National Ecological Observatory Network (NEON) between 2019 and 2021. The validation against ground CCC measurements showed an R 2 of 0.89 and an RMSE of 0.30 g·m −2 . In the second step, these high-resolution CCC maps were resampled or upscaled to a spatial resolution of 300 m, and combined with Sentinel-3 OLCI TOA reflectance images to train random forest (RF) models. The RF model demonstrated robust performance with leave-one-site-out cross-validation, yielding an R 2 of 0.92 and RMSE of 0.14 g·m −2 . The two-step method also showed minimal sensitivity to angular effects and land cover variations, underscoring its robustness. In comparison, the traditional direct inversion method (the one-step method) led to underestimation of CCC by 0.16 g·m −2 and a moderate estimation accuracy (R 2 = 0.65, RMSE = 0.30 g·m −2 ). We generated a long-term global OLCI CCC product using Sentinel-3 OLCI TOA reflectance data from 2016 to 2024, which can also be continuously updated using current data. This global CCC product can provide important plant physiological information, for parameterizing terrestrial biosphere models and capturing spatiotemporal photosynthetic patterns, thereby advancing research on vegetation carbon dynamics cycles at the global scale. • A two-step method is developed to estimate canopy chlorophyll content (CCC) • Sentinel-3 OLCI top-of-atmosphere (TOA) reflectance is used to estimate CCC • CCC is accurately estimated from TOA reflectance using the two-step method • The two-step method outperforms the traditional one-step method in estimating CCC • A global CCC product (2016–2024) is generated at a 300 m spatial resolution

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Publication Details

Journal
Remote Sensing of Environment
Published
2025-06-03
DOI
https://doi.org/10.1016/j.rse.2025.114845
Citations
4
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
6.87

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article

Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models

Holly Croft, Kang Yu, Weixing Cao, Anirudh Belwalkar et al.
4 citations
Remote Sensing of Environment
Remote Sensing in Agriculture
6.87
article

Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models

Holly Croft, Kang Yu, Weixing Cao, Anirudh Belwalkar, Adam P. Schreiner‐McGraw, Grégory Duveiller, Dong Li, Tao Cheng, Yan Zhu
article en
4 citations

Abstract

Canopy chlorophyll content per ground area (CCC, g·m −2 ) is tightly related to vegetation photosynthesis and is a promising indicator of photosynthetic capacity. However, a global operational CCC product is not yet available. To fill this gap, we developed a two-step upscaling method to estimate global CCC from Sentinel-3 OLCI top-of-atmosphere (TOA) reflectance. In the first step, a physically-based PROSAIL-D inversion model produced accurate CCC maps from over 20,000 high-spatial resolution (1 m) airborne hyperspectral images collected across 50 sites within the National Ecological Observatory Network (NEON) between 2019 and 2021. The validation against ground CCC measurements showed an R 2 of 0.89 and an RMSE of 0.30 g·m −2 . In the second step, these high-resolution CCC maps were resampled or upscaled to a spatial resolution of 300 m, and combined with Sentinel-3 OLCI TOA reflectance images to train random forest (RF) models. The RF model demonstrated robust performance with leave-one-site-out cross-validation, yielding an R 2 of 0.92 and RMSE of 0.14 g·m −2 . The two-step method also showed minimal sensitivity to angular effects and land cover variations, underscoring its robustness. In comparison, the traditional direct inversion method (the one-step method) led to underestimation of CCC by 0.16 g·m −2 and a moderate estimation accuracy (R 2 = 0.65, RMSE = 0.30 g·m −2 ). We generated a long-term global OLCI CCC product using Sentinel-3 OLCI TOA reflectance data from 2016 to 2024, which can also be continuously updated using current data. This global CCC product can provide important plant physiological information, for parameterizing terrestrial biosphere models and capturing spatiotemporal photosynthetic patterns, thereby advancing research on vegetation carbon dynamics cycles at the global scale. • A two-step method is developed to estimate canopy chlorophyll content (CCC) • Sentinel-3 OLCI top-of-atmosphere (TOA) reflectance is used to estimate CCC • CCC is accurately estimated from TOA reflectance using the two-step method • The two-step method outperforms the traditional one-step method in estimating CCC • A global CCC product (2016–2024) is generated at a 300 m spatial resolution

Remote Sensing of EnvironmentVol. 328
Nanjing Agricultural University (CN), Agricultural Research Service (US), Quality Research (US), Max Planck Institute for Biogeochemistry (DE), Technical University of Munich (DE), University of Sheffield (GB)
National Science Foundation, Battelle, UK Research and Innovation, European Commission, National Natural Science Foundation of China, Office of Science, Agricultural Research Service, National Key Research and Development Program of China
Openalex Percentile: Top 4%
Remote Sensing in Agriculture
6.87
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