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Reliable estimates of Gross Primary Productivity (GPP), crucial forevaluating climate change initiatives, are currently only available fromsparsely distributed eddy covariance tower sites. This limitation hampersaccess to reliable GPP quantification at regional to global scales. Priormachine learning studies on upscaling \textit{in situ} GPP to globalwall-to-wall maps at sub-daily time steps faced limitations such as lack ofinput features at higher temporal resolutions and significant missing values.This research explored a novel upscaling solution using Temporal FusionTransformer (TFT) without relying on past GPP time series. Model developmentwas supplemented by Random Forest Regressor (RFR) and XGBoost, followed by thehybrid model of TFT and tree algorithms. The best preforming model yielded tomodel performance of 0.704 NSE and 3.54 RMSE. Another contribution of the studywas the breakdown analysis of encoder feature importance based on time and fluxtower sites. Such analysis enhanced the interpretability of the multi-headattention layer as well as the visual understanding of temporal dynamics ofinfluential features.

Upscaling Global Hourly GPP with Temporal Fusion Transformer (TFT)