气候变化领域集成服务门户(CCPortal): Using automated machine learning for the upscaling of gross primary productivity

CCPortal

DOI	10.5194/bg-21-2447-2024
	Using automated machine learning for the upscaling of gross primary productivity
	Gaber, Max; Kang, Yanghui; Schurgers, Guy; Keenan, Trevor
发表日期	2024
ISSN	1726-4170
EISSN	1726-4189
起始页码	21
结束页码	10
卷号	21 期号:10
英文摘要	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 r 2 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.
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology
WOS类目	Ecology ; Geosciences, Multidisciplinary
WOS记录号	WOS:001230184200001
来源期刊	BIOGEOSCIENCES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/303370
作者单位	University of California System; University of California Berkeley; University of Copenhagen; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory
推荐引用方式 GB/T 7714	Gaber, Max,Kang, Yanghui,Schurgers, Guy,et al. Using automated machine learning for the upscaling of gross primary productivity[J],2024,21(10).
APA	Gaber, Max,Kang, Yanghui,Schurgers, Guy,&Keenan, Trevor.(2024).Using automated machine learning for the upscaling of gross primary productivity.BIOGEOSCIENCES,21(10).
MLA	Gaber, Max,et al."Using automated machine learning for the upscaling of gross primary productivity".BIOGEOSCIENCES 21.10(2024).