Using automated machine learning for the upscaling of gross primary productivity

Gaber, Max; Kang, Yanghui; Schurgers, Guy; Keenan, Trevor

doi:10.5194/bg-21-2447-2024

Using automated machine learning for the upscaling of gross primary productivity

Journal Article · Fri May 24 00:00:00 EDT 2024 · Biogeosciences (Online)

DOI:https://doi.org/10.5194/bg-21-2447-2024· OSTI ID:2404343

^[1]; Kang, Yanghui ^[2]; ^[3]; Keenan, Trevor ^[2]

Univ. of California, Berkeley, CA (United States); Univ. of Copenhagen (Denmark)
Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Univ. of Copenhagen (Denmark)

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r² of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Aeronautics and Space Administration (NASA); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231; SC0021023

OSTI ID:: 2404343

Journal Information:: Biogeosciences (Online), Journal Name: Biogeosciences (Online) Journal Issue: 10 Vol. 21; ISSN 1726-4189

Publisher:: Copernicus Publications, EGUCopyright Statement

Country of Publication:: United States

Language:: English

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