RTSIF dataset

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in measuring SIF, but the short temporal coverage of the data records has limited its applications in long-term studies. This dataset uses machine learning to reconstruct TROPOMI SIF (RTSIF) for 2001-2020 with a spatial resolution of 0.05° and a temporal resolution of 8 days. Our machine learning model has high accuracy on the training and testing data (R² = 0.907, regression slope = 1.001). The RTSIF dataset is in good agreement with the original TROPOMI SIF, and its accuracy is further validated against tower-based SIF. The RTSIF dataset is also compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF). Comparing RTSIF with Gross Primary Production (GPP) illustrates the potential of RTSIF for estimating carbon fluxes. We anticipate that this new dataset will be valuable in assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.

卫星反演日光诱导叶绿素荧光（SIF）可作为光合作用的有效替代指标。搭载于哥白尼哨兵-5P（Copernicus Sentinel-5P）任务的对流层监测仪（TROPOMI）在SIF测量方面实现了显著提升，但数据记录的时间覆盖时长较短，限制了其在长期研究中的应用。本数据集采用机器学习方法，重构了2001-2020年的TROPOMI SIF数据（命名为RTSIF），其空间分辨率为0.05°，时间分辨率为8天。本研究的机器学习模型在训练与测试集上均表现出较高精度（决定系数R²=0.907，回归斜率=1.001）。RTSIF数据集与原始TROPOMI SIF数据一致性良好，并通过塔基观测SIF进一步验证了其精度。该数据集还与其他卫星反演SIF数据（GOME-2 SIF及OCO-2 SIF）开展了对比分析。将RTSIF与总初级生产力（GPP）进行对比，可体现RTSIF在碳通量估算方面的应用潜力。我们期望该新型数据集能够为长期陆地光合作用评估、全球碳预算约束及相关水通量研究提供重要支撑。