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利用机器学习算法构建的长期 TROPOMI 太阳诱导荧光数据集。

A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms.

机构信息

State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China.

The Key Laboratory of Ecological Protection and High Quality Development in the Upper Yellow River, Qinghai Province, China.

出版信息

Sci Data. 2022 Jul 20;9(1):427. doi: 10.1038/s41597-022-01520-1.

DOI:10.1038/s41597-022-01520-1
PMID:35859094
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9300726/
Abstract

Photosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. This study uses machine learning to reconstruct TROPOMI SIF (RTSIF) over the 2001-2020 period in clear-sky conditions with high spatio-temporal resolutions (0.05° 8-day). Our machine learning model achieves high accuracies on the training and testing datasets (R = 0.907, regression slope = 1.001). The RTSIF dataset is validated against TROPOMI SIF and tower-based SIF, and 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 gross 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.

摘要

光合作用是连接碳和水循环的关键过程，卫星反演的太阳诱导叶绿素荧光（SIF）可以作为光合作用的一个有价值的替代指标。哥白尼哨兵-5P 任务上的对流层监测仪（TROPOMI）能够显著提高提供高时空分辨率 SIF 观测的能力，但数据记录的短时间覆盖范围限制了其在长期研究中的应用。本研究使用机器学习方法，在晴空条件下重建了 TROPOMI SIF（RTSIF），具有高时空分辨率（0.05° 8 天），覆盖 2001-2020 年。我们的机器学习模型在训练和测试数据集上具有很高的准确性（R=0.907，回归斜率=1.001）。RTSIF 数据集与 TROPOMI SIF 和基于塔的 SIF 进行了验证，并与其他卫星衍生的 SIF（GOME-2 SIF 和 OCO-2 SIF）进行了比较。将 RTSIF 与总初级生产力（GPP）进行比较，说明了 RTSIF 估计总碳通量的潜力。我们预计，这个新数据集将有助于评估长期陆地光合作用，并约束全球碳预算和相关的水通量。

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