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

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 R² 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 R² 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 (R² = 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.

Plant phenotyping relevance

Sentinel-3および航空ハイパースペクトル画像から植物キャノピーのクロロフィル含量を推定する二段階センシング・機械学習手法を開発し、地上測定およびサイト間交差検証で性能評価しているため、植物表現型取得が研究の中心である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.