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\begin{document}

\title{Exploring the Potential of Long Short-Term Memory Networks for
Predicting Net CO2 Exchange Across Various Ecosystems With Multi-Source
Data}



\author[1]{Chengcheng Huang}%
\author[2]{Wei He}%
\author[3]{Jinxiu Liu}%
\author[4]{Ngoc Tu Nguyen}%
\author[5]{Hua Yang}%
\author[6]{Yiming Lv}%
\author[1]{Hui Chen}%
\author[7]{Mengyao Zhao}%
\affil[1]{School of Information Engineering, China University of Geosciences}%
\affil[2]{International Institute for Earth System Science, Nanjing University}%
\affil[3]{China University of Geosciences}%
\affil[4]{Hohai University}%
\affil[5]{Beijing Normal University}%
\affil[6]{China University of Geosciences, Beijing}%
\affil[7]{School of Geography and Tourism, Anhui Normal University}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Upscaling flux tower measurements based on machine learning (ML)
algorithms is an essential approach for large-scale net ecosystem CO2
exchange (NEE) estimation, but existing ML upscaling methods face some
challenges, particularly in capturing NEE interannual variations (IAVs)
that may relate to lagged effects. With the capacity of characterizing
temporal memory effects, the Long Short-Term Memory (LSTM) networks are
expected to help solve this problem. Here we explored the potential of
LSTM for predicting NEE across various ecosystems using flux tower data
over 82 sites in North America. The LSTM model with differentiated plant
function types (PFTs) demonstrates the capability to explain 79.19\% (R2
= 0.79) of the monthly variations in NEE within the testing set, with
RMSE and MAE values of 0.89 and 0.57 g C m-2 d-1 respectively (r = 0.89,
p \textless{} 0.001). Moreover, the LSTM model performed robustly in
predicting cross-site variability, with 67.19\% of the sites that can be
predicted by both LSTM models with and without distinguished PFTs
showing improved predictive ability. Most importantly, the IAV of
predicted NEE highly correlated with that in flux observations (r =
0.81, p \textless{} 0.001), clearly outperforming that by the random
forest model (r = -0.21, p = 0.011). Among all nine PFTs, solar-induced
chlorophyll fluorescence, downward shortwave radiation, and leaf area
index are the most important variables for explaining NEE variations,
collectively accounting for approximately 54.01\% in total. This study
highlights the great potential of LSTM for improving carbon flux
upscaling with multi-source remote sensing data.%
\end{abstract}%



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\textbf{Hosted file}

\verb`979209_0_art_file_11580370_s3yzrp.docx`  available at \url{https://authorea.com/users/699314/articles/686738-exploring-the-potential-of-long-short-term-memory-networks-for-predicting-net-co2-exchange-across-various-ecosystems-with-multi-source-data}

\textbf{Hosted file}

\verb`979209_0_supp_11580371_s3yd1v.docx`  available at \url{https://authorea.com/users/699314/articles/686738-exploring-the-potential-of-long-short-term-memory-networks-for-predicting-net-co2-exchange-across-various-ecosystems-with-multi-source-data}

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\end{document}

