Reliable estimates of Gross Primary Productivity (GPP), crucial for
evaluating climate change initiatives, are currently only available from
sparsely distributed eddy covariance tower sites. This limitation hampers
access to reliable GPP quantification at regional to global scales. Prior
machine learning studies on upscaling \textit{in situ} GPP to global
wall-to-wall maps at sub-daily time steps faced limitations such as lack of
input features at higher temporal resolutions and significant missing values.
This research explored a novel upscaling solution using Temporal Fusion
Transformer (TFT) without relying on past GPP time series. Model development
was supplemented by Random Forest Regressor (RFR) and XGBoost, followed by the
hybrid model of TFT and tree algorithms. The best preforming model yielded to
model performance of 0.704 NSE and 3.54 RMSE. Another contribution of the study
was the breakdown analysis of encoder feature importance based on time and flux
tower sites. Such analysis enhanced the interpretability of the multi-head
attention layer as well as the visual understanding of temporal dynamics of
influential features.

本研究探索了一种新的方法，利用时间融合变压器 (Temporal Fusion Transformer) 将静态变量和时变变量一起考虑，以在缺乏较高时间分辨率输入特征以及存在显著遗漏值的情况下，上采样在局部到全球尺度上量化大小植被原初生产力 (Gross Primary Productivity) 的问题。同时，该研究还对时空因子的影响进行了可解释性的分析。