ABSTRACT Solar-Induced chlorophyll Fluorescence (SIF) is associated with vegetation canopy photosynthesis and is potentially used to retrieve Gross Primary Productivity (GPP). However, the coarse resolutions of the currently available SIF satellite data limit their applications. To expand the applicability of the SIF dataset, a framework was developed to disaggregate the Global Ozone Monitoring Experiment-2 (GOME-2) SIF dataset, which was based on statistical relationships between SIF and remotely sensed measurements of the Normalized Difference Vegetation Index (NDVI), the fraction of absorbed photosynthetically active radiation (f PAR), the soil moisture index and Land Surface Temperature (LST). The statistical relationships were established within a zone of n × n pixels (n∈[1, 25]) with a moving window technique. The regression function established within n × n pixels with the smallest Root Mean Square Error (RMSE) and highest coefficient of determination (R 2) was selected for downscaling regression. Compared with the fixed window technique (n = 5) and theglobal regression model, the moving window technique presented low residuals and high R 2 values. Validated with flux-tower eddy covariance measurements, the GPP retrieved within the downscaled SIF data shows the potential to improve vegetation GPP prediction, and the downscaled SIF could trace the seasonal phenology of evergreen forests.

中文翻译：

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基于 GOME-2 和 MODIS 数据，通过统计缩小的太阳诱导叶绿素荧光 (SIF) 检测区域 GPP 变化

摘要 太阳能诱导叶绿素荧光 (SIF) 与植被冠层光合作用有关，并可能用于恢复总初级生产力 (GPP)。然而，目前可用的 SIF 卫星数据的粗分辨率限制了它们的应用。为了扩大 SIF 数据集的适用性，开发了一个框架来分解全球臭氧监测实验 2 (GOME-2) SIF 数据集，该数据集基于 SIF 与归一化差异植被指数 (NDVI) 遥感测量之间的统计关系)、吸收光合有效辐射的分数 (f PAR)、土壤水分指数和地表温度 (LST)。使用移动窗口技术在 n × n 像素 (n∈[1, 25]) 区域内建立统计关系。选择在具有最小均方根误差 (RMSE) 和最高决定系数 (R 2) 的 n × n 像素内建立的回归函数进行降尺度回归。与固定窗口技术（n = 5）和全局回归模型相比，移动窗口技术呈现出低残差和高R 2 值。通过通量塔涡流协方差测量验证，在缩小的 SIF 数据中检索的 GPP 显示了改善植被 GPP 预测的潜力，并且缩小的 SIF 可以追踪常绿森林的季节性物候。移动窗口技术呈现低残差和高R 2 值。通过通量塔涡流协方差测量验证，在缩小的 SIF 数据中检索的 GPP 显示了改善植被 GPP 预测的潜力，并且缩小的 SIF 可以追踪常绿森林的季节性物候。移动窗口技术呈现低残差和高R 2 值。通过通量塔涡流协方差测量验证，在缩小的 SIF 数据中检索到的 GPP 显示了改善植被 GPP 预测的潜力，并且缩小的 SIF 可以追踪常绿森林的季节性物候。