Using automated machine learning for the upscaling of gross primary productivity

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

doi:https://doi.org/10.5194/bg-21-2447-2024

Articles | Volume 21, issue 10

https://doi.org/10.5194/bg-21-2447-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-21-2447-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 21, issue 10

Research article

|

24 May 2024

Research article |

| 24 May 2024

Using automated machine learning for the upscaling of gross primary productivity

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

Data sets

Global MODIS and FLUXNET-derived Daily Gross Primary Production, V2 (2) J. Joiner and Y. Yoshida https://doi.org/10.3334/ORNLDAAC/1835

ERA5-Land monthly averaged data from 1950 to present J. Muñoz Sabater https://doi.org/10.24381/CDS.68D2BB30

A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks (2000-2022) Y. Zhang https://doi.org/10.11888/Ecolo.tpdc.271751

Warm Winter 2020 ecosystem eddy covariance flux product for 73 stations in FLUXNET-Archive format-release 2022-1 (1.0) Warm Winter 2020 Team and ICOS Ecosystem Thematic Centre https://doi.org/10.18160/2G60-ZHAK

MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 M. Friedl and D. Sulla-Menashe https://doi.org/10.5067/MODIS/MCD12Q1.006

MCD15A2H MODIS/Terra+Aqua Leaf Area Index/FPAR 8-day L4 Global 500m SIN Grid V006 R. Myneni et al. https://doi.org/10.5067/MODIS/MCD15A2H.006

MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global - 500m V006 C. Schaaf and Z. Wang https://doi.org/10.5067/MODIS/MCD43A4.006

MOD11A1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V006 Z. Wan et al. https://doi.org/10.5067/MODIS/MOD11A1.006

AmeriFlux AmeriFlux Management Project https://ameriflux.lbl.gov/data/flux-data-products

Data from the ESA CCI Soil Moisture project W. Dorigo et al. https://data.ceda.ac.uk/neodc/esacci/soil_moisture

Bess_Rad Y. Ryu et al. https://www.environment.snu.ac.kr/bess-rad

FLUXNET2015 dataset G. Pastorello et al. https://fluxnet.org/data/fluxnet2015-dataset

Model code and software

AutoML for GPP upscaling v1.0 Max Gaber https://doi.org/10.5281/zenodo.8262618

Short summary

Gross primary productivity (GPP) describes the photosynthetic carbon assimilation, which plays a vital role in the carbon cycle. We can measure GPP locally, but producing larger and continuous estimates is challenging. Here, we present an approach to extrapolate GPP to a global scale using satellite imagery and automated machine learning. We benchmark different models and predictor variables and achieve an estimate that can capture 75 % of the variation in GPP.