Upscaling Global Hourly GPP with Temporal Fusion Transformer (TFT)
Rumi Nakagawa Mary Chau John Calzaretta Trevor Keenan Puya Vahabi Alberto Todeschini Maoya Bassiouni Yanghui Kang
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.