The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical land surface variable for carbon cycle modeling and ecological monitoring. Several global FAPAR products have been released and have become widely used; however, spatiotemporal inconsistency remains a large issue for the current products, and their spatial resolutions and accuracies can hardly meet the user requirements. An effective solution to improve the spatiotemporal continuity and accuracy of FAPAR products is to take better advantage of the temporal information in the satellite data using deep learning approaches. In this study, the latest version (V6) of the FAPAR product with a 250 m resolution was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and other information, as part of the Global LAnd Surface Satellite (GLASS) product suite. In addition, it was aggregated to multiple coarser resolutions (up to 0.25^∘ and monthly). Three existing global FAPAR products (MODIS Collection 6; GLASS V5; and PRoject for On-Board Autonomy–Vegetation, PROBA-V, V1) were used to generate the time-series training samples, which were used to develop a bidirectional long short-term memory (Bi-LSTM) model. Direct validation using high-resolution FAPAR maps from the Validation of Land European Remote sensing Instrument (VALERI) and ImagineS networks revealed that the GLASS V6 FAPAR product has a higher accuracy than PROBA-V, MODIS, and GLASS V5, with an R² value of 0.80 and root-mean-square errors (RMSEs) of 0.10–0.11 at the 250 m, 500 m, and 3 km scales, and a higher percentage (72 %) of retrievals for meeting the accuracy requirement of 0.1. Global spatial evaluation and temporal comparison at the AmeriFlux and National Ecological Observatory Network (NEON) sites revealed that the GLASS V6 FAPAR has a greater spatiotemporal continuity and reflects the variations in the vegetation better than the GLASS V5 FAPAR. The higher quality of the GLASS V6 FAPAR is attributed to the ability of the Bi-LSTM model, which involves high-quality training samples and combines the strengths of the existing FAPAR products, as well as the temporal and spectral information from the MODIS surface reflectance data and other information. The 250 m 8 d GLASS V6 FAPAR product for 2020 is freely available at https://doi.org/10.5281/zenodo.6405564 and https://doi.org/10.5281/zenodo.6430925 (Ma, 2022a, b) as well as at the University of Maryland for 2000–2021 (http://glass.umd.edu/FAPAR/MODIS/250m, last access 1 November 2022).

中文翻译：

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2000 年至 2021 年全球陆地表面 250 m 8 d 吸收光合有效辐射 (FAPAR) 产品的分数

吸收光合有效辐射 (FAPAR) 的分数是碳循环建模和生态监测的关键地表变量。多款全球FAPAR产品发布并得到广泛应用；然而，时空不一致性仍然是目前产品的一大问题，其空间分辨率和精度难以满足用户需求。提高 FAPAR 产品时空连续性和准确性的有效解决方案是使用深度学习方法更好地利用卫星数据中的时间信息。在这项研究中，FAPAR 产品的最新版本 (V6) 具有 250 m 分辨率，是根据中分辨率成像光谱仪 (MODIS) 表面反射率数据和其他信息生成的，作为全球陆地表面卫星 (GLASS) 产品套件的一部分。此外，它被聚合到多个更粗略的分辨率（高达 0.25^∘和每月）。三个现有的全球 FAPAR 产品（MODIS Collection 6；GLASS V5；和 PRoject for On-Board Autonomy–Vegetation，PROBA-V，V1）被用于生成时间序列训练样本，用于开发双向长短-术语记忆 (Bi-LSTM) 模型。使用欧洲陆地遥感仪器验证 (VALERI) 和 ImagineS 网络的高分辨率 FAPAR 地图进行直接验证表明，GLASS V6 FAPAR 产品的精度高于 PROBA-V、MODIS 和 GLASS V5， R ²在 250 m、500 m 和 3 km 尺度上的值为 0.80，均方根误差 (RMSE) 为 0.10–0.11，满足 0.1 精度要求的检索百分比 (72%) 更高。AmeriFlux 和国家生态观测站网络 (NEON) 站点的全球空间评估和时间比较表明，GLASS V6 FAPAR 具有更大的时空连续性，并且比 GLASS V5 FAPAR 更好地反映植被的变化。GLASS V6 FAPAR 的更高质量归功于 Bi-LSTM 模型的能力，它涉及高质量的训练样本并结合了现有 FAPAR 产品的优势，以及来自 MODIS 表面反射率的时间和光谱信息数据和其他信息。2020 年的 250 m 8 d GLASS V6 FAPAR 产品可在以下位置免费获得https://doi.org/10.5281/zenodo.6405564 和https://doi.org/10.5281/zenodo.6430925（Ma，2022a，b）以及马里兰大学 2000-2021（http：/ /glass.umd.edu/FAPAR/MODIS/250m，最后访问时间为 2022 年 11 月 1 日）。