Chen Siyuan, Liu Liangyun, Sui Lichun, Liu Xinjie, Ma Yan
PowerChina Northwest Engineering Corporation Limited, Xi'an, 710065, China.
International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094, China.
Sci Data. 2025 Jan 22;12(1):135. doi: 10.1038/s41597-024-04325-6.
Solar-induced chlorophyll fluorescence (SIF) is an indicator of vegetation photosynthesis, and multiple satellite SIF products have been generated in recent years. However, current SIF products are limited for applications toward vegetation photosynthesis monitoring because of low spatial resolution or spatial discontinuity. This study uses a spatial downscaling method to obtain a redistribution of the original TROPOspheric Monitoring Instrument (TROPOMI) SIF (OSIF). As a result, a downscaled SIF dataset (TroDSIF) with fine spatio-temporal resolutions (500 m, 16 days) was generated. Compared with a machine learning (ML) SIF product and OSIF, TroDSIF can better reproduce the OSIF signals with higher R, lower root mean square error (RMSE), and nearly zero residuals at different latitudes. Direct validation on TroDSIF using tower-based SIF measurements demonstrated a good consistency between them. However, TroDSIF is dependent on the linear hypothesis between OSIF and the ML-predicted SIF used in the redistribution process. Nonetheless, we believe TroDSIF is anticipated to be beneficial to conducting global vegetation photosynthesis and climate change studies at precise scales.
太阳诱导叶绿素荧光（SIF）是植被光合作用的一个指标，近年来已生成多种卫星SIF产品。然而，由于空间分辨率低或空间不连续性，目前的SIF产品在用于植被光合作用监测方面受到限制。本研究采用空间降尺度方法对原始对流层监测仪器（TROPOMI）的SIF（OSIF）进行重新分配。结果，生成了一个具有精细时空分辨率（500米，16天）的降尺度SIF数据集（TroDSIF）。与机器学习（ML）SIF产品和OSIF相比，TroDSIF能够以更高的R、更低的均方根误差（RMSE）以及不同纬度处近乎零的残差更好地再现OSIF信号。使用基于塔的SIF测量对TroDSIF进行直接验证表明二者之间具有良好的一致性。然而，TroDSIF依赖于重新分配过程中使用的OSIF与ML预测的SIF之间的线性假设。尽管如此，我们相信TroDSIF有望有助于在精确尺度上开展全球植被光合作用和气候变化研究。
