Solar-induced chlorophyll fluorescence (SIF) is an intuitive and accurate way to measure vegetation photosynthesis. Orbiting Carbon Observatory-2 (OCO-2)-retrieved SIF has shown great potential in estimating terrestrial gross primary production (GPP), but the discontinuous spatial coverage limits its application. Although some researchers have reconstructed OCO-2 SIF data, few have considered the uneven spatial and temporal distribution of the swath-distributed data, which can induce large uncertainties. In this article, we propose a spatiotemporal constrained light gradient boosting machine model (ST-LGBM) to reconstruct a contiguous OCO-2 SIF product (eight days, 0.05°), considering the data distribution characteristics. Two spatial and temporal constraining factors are introduced to utilize the relationships between the swath-distributed OCO-2 samples, combining the geographical regularity and vegetation phenological characteristics. The results indicate that the ST-LGBM method can improve the reconstruction accuracy in the missing data areas ( R2=0.79R^{2}= 0.79 ), with an increment of 0.05 in R2R^{2} . The declined accuracy of the traditional light gradient boosting machine (LightGBM) method in the missing data areas is well alleviated in our results. The real-data comparison with TROPOspheric Monitoring Instrument (TROPOMI) SIF observations also shows that the results of the ST-LGBM method can achieve a much better consistency, in both spatial distribution and temporal variation. The sensitivity analysis also shows that the ST-LGBM can support stable results when using various input combinations or different machine learning models. This approach represents an innovative way to reconstruct a more accurate globally continuous OCO-2 SIF product and also provides references to reconstruct other data with a similar distribution.

中文翻译：

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用于 OCO-2 太阳诱导叶绿素荧光 (SIF) 重建的时空约束机器学习方法


太阳诱导叶绿素荧光（SIF）是一种直观、准确的测量植被光合作用的方法。轨道碳观测站-2（OCO-2）检索的SIF在估算陆地总初级生产力（GPP）方面显示出巨大潜力，但不连续的空间覆盖限制了其应用。尽管一些研究人员重建了OCO-2 SIF数据，但很少有人考虑到测绘带分布数据的时空分布不均匀，这会带来很大的不确定性。在本文中，考虑到数据分布特征，我们提出了一种时空约束光梯度增强机模型（ST-LGBM）来重建连续的 OCO-2 SIF 产品（8 天，0.05°）。结合地理规律和植被物候特征，引入两个时空约束因子，利用条状分布的OCO-2样本之间的关系。结果表明，ST-LGBM方法可以提高缺失数据区域（R2=0.79R^{2}= 0.79）的重建精度，R2R^{2}增量为0.05。在我们的结果中，传统光梯度增强机（LightGBM）方法在缺失数据区域的精度下降得到了很好的缓解。与对流层监测仪器（TROPOMI）SIF观测的真实数据对比也表明，ST-LGBM方法的结果无论在空间分布还是时间变化上都能取得更好的一致性。敏感性分析还表明，当使用各种输入组合或不同的机器学习模型时，ST-LGBM 可以支持稳定的结果。 该方法代表了一种重建更准确的全局连续 OCO-2 SIF 产品的创新方法，也为重建具有类似分布的其他数据提供了参考。