Abstract. Leaf chlorophyll content (LCC), a prominent plant physiological trait and a proxy for leaf photosynthetic capacity, plays a crucial role in the monitoring of agriculture and carbon cycle modeling. In this study, global 500 m LCC weekly dataset (GLCC) for the period 2003–2012 to 2018–2020 were produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physically-based radiative transfer modeling approach. Firstly, five look-up-tables (LUTs) were generated using PROSAIL-D and PROSPECT-D+4-Scale models for woody and non-woody plants, respectively. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. For the one LUT applicable to non-woody vegetation type, it includes 25 sub-LUTs corresponding to five kinds of canopy structure and five kinds of soil background. The average of the LCC inversion results of all sub-LUTs for each plant function type (PFT) was considered as the retrieval. The LUT algorithm was validated using the synthetic dataset, which gave an R² value higher than 0.79 and an RMSE value lower than 10.5 μg cm⁻². Then, the GLCC dataset was generated using the MERIS/OLCI multispectral data over 2003–2012 and 2018–2020 at a spatial resolution of 500 m and temporal resolution of one week. The GLCC dataset was validated using 161 field measurements, covering six PFTs. The validation results yielded an overall accuracy of R² = 0.41 and RMSE = 8.94 μg cm⁻². Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset developed by Croft et al. (2020). OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling. The GLCC dataset is available at https://doi.org/10.25452/figshare.plus.20439351 (Qian et al., 2022b).

中文翻译：

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源自 MERIS/OLCI 卫星数据 (GLCC) 的 2003-2012 年和 2018-2020 年全球叶绿素含量数据集：算法和验证

摘要。叶片叶绿素含量 (LCC) 是一种突出的植物生理特性，也是叶片光合能力的代表，在农业监测和碳循环建模中起着至关重要的作用。在这项研究中，使用基于物理的辐射传输建模方法，根据 ENVISAT MERIS 和 Sentinel-3 OLCI 卫星数据生成了 2003-2012 年至 2018-2020 年期间的全球 500 m LCC 每周数据集 (GLCC)。首先，使用 PROSAIL-D 和 PROSPECT-D+4-Scale 模型分别为木本和非木本植物生成了五个查找表 (LUT)。对于适用于木本植物的四种LUT，每个LUT包含三个对应三种冠高的子LUT。适用于非木本植被类型的1个LUT包括25个子LUT，对应5种冠层结构和5种土壤背景。将每种植物功能类型（PFT）的所有子LUT的LCC反演结果的平均值视为检索。LUT 算法使用合成数据集进行了验证，该数据集给出了 R²值高于0.79，RMSE 值低于10.5 μg cm ^-2。然后，使用 2003-2012 年和 2018-2020 年的 MERIS/OLCI 多光谱数据生成 GLCC 数据集，空间分辨率为 500 m，时间分辨率为一周。使用 161 次现场测量验证了 GLCC 数据集，涵盖了 6 个 PFT。验证结果产生的总体准确度为 R ² = 0.41 和 RMSE = 8.94 μg cm ^-2. 最后，GLCC 数据集与 Croft 等人开发的现有 MERIS LCC 数据集呈现出可接受的一致性。（2020 年）。OLCI 作为 MERIS 数据的继承者，首次与 MERIS 数据联合生成 2003-2012 年至 2018-2020 年的 LCC 数据。这个跨越近 20 年的新 GLCC 数据集将为监测植被生长和陆地碳循环建模提供宝贵的机会。GLCC 数据集可在 https://doi.org/10.25452/figshare.plus.20439351 获得（Qian 等人，2022b）。