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<p>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<sup>2</sup>, 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.</p>

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An improved spatially downscaled solar-induced chlorophyll fluorescence dataset from the TROPOMI product

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月22日

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  • Siyuan Chen,
  • Liangyun Liu,
  • Lichun Sui,
  • Xinjie Liu,
  • Yan Ma

摘要

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 R2, 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.