Estimation of Turbulent Heat Fluxes and Gross Primary Productivity by Assimilating Land Surface Temperature and Leaf Area Index

He, Xinlei; Xu, Tong; Bateni, Sayed M.; Ki, Seo Jin; Xiao, Jingfeng; Liu, Shaomin; Song, Lisheng

doi:10.1029/2020wr028224

Cited by 8 publications

(1 citation statement)

References 155 publications

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“…To reduce the number of unconstrained parameters, observations on ecosystem properties from various temporal and spatial scales are being used for model calibration (MacBean et al, 2016(MacBean et al, , 2022Xiao et al, 2019). Current calibration of carbon-related parameters mainly focuses on using data that are intensively observed by field measurements or space sensors, such as gross primary productivity (GPP), leaf area index (LAI), soil moisture, and the fraction of absorbed photosynthetically active radiation (FAPAR), as independent or joint constraints (Bacour et al, 2015;Forkel et al, 2019;He et al, 2021;Kumar et al, 2019;Ma et al, 2022a). These land surface variables provide information on fast carbon and water fluxes exchanging, but poorly indicate the ecosystem carbon stock evolution at longer timescales, leading to uncertainties in biomass, respiration, and net biome productivity (NBP) predictions (Santaren et al, 2014;Thum et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Stepwise Calibration of Age‐Dependent Biomass in the Integrated Biosphere Simulator (IBIS) Model

Ma,

Zhang,

Ciais

et al. 2024

J Adv Model Earth Syst

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Many land surface models (LSMs) assume a steady‐state assumption (SS) for forest growth, leading to an overestimation of biomass in young forests. Parameters inversion under SS will potentially result in biased carbon fluxes and stocks in a transient simulation. Incorporating age‐dependent biomass into LSMs can simulate real disequilibrium states, enabling the model to simulate forest growth from planting to its current age, and improving the biased post‐calibration parameters. In this study, we developed a stepwise optimization framework that first calibrates “fast” light‐controlled CO2 fluxes (gross primary productivity, GPP), then leaf area index (LAI), and finally “slow” growth‐controlled biomass using the Global LAnd Surface Satellite (GLASS) GPP and LAI products, and age‐dependent biomass curves for the 25 forests. To reduce the computation time, we used a machine learning‐based model to surrogate the complex integrated biosphere simulator LSM during calibration. Our calibrated model led to an error reduction in GPP, LAI, and biomass by 28.5%, 35.3% and 74.6%, respectively. When compared with net biome productivity (NBP) using no‐age‐calibrated parameters, our age‐calibrated parameters increased NBP by an average of 50 gC m−2 yr−1 across all forests, especially in the boreal needleleaf evergreen forests, the NBP increased by 118 gC m−2 yr−1 on average, increasing the estimate of the carbon sink in young forests. Our work highlights the importance of including forest age in LSMs, and provides a novel framework for better calibrating LSMs using constraints from multiple satellite products at a global scale.

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Section: Introductionmentioning

confidence: 99%

Stepwise Calibration of Age‐Dependent Biomass in the Integrated Biosphere Simulator (IBIS) Model

Ma,

Zhang,

Ciais

et al. 2024

J Adv Model Earth Syst

Self Cite

View full text Add to dashboard Cite

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Comparison of sensible and latent heat fluxes from optical-microwave scintillometers and eddy covariance systems with respect to surface energy balance closure

Zheng

Liu

Song

et al. 2023

Agricultural and Forest Meteorology

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Innovative approach for estimating evapotranspiration and gross primary productivity by integrating land data assimilation, machine learning, and multi-source observations

He,

Liu,

Bateni

et al. 2024

Agricultural and Forest Meteorology

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Estimation of Turbulent Heat Fluxes and Gross Primary Productivity by Assimilating Land Surface Temperature and Leaf Area Index

Abstract: Accurate prediction of sensible (H) and latent (LE) heat fluxes as well as gross primary productivity (GPP) is required for monitoring the energy, water, and carbon cycles (

Cited by 8 publications

References 155 publications

Stepwise Calibration of Age‐Dependent Biomass in the Integrated Biosphere Simulator (IBIS) Model

Stepwise Calibration of Age‐Dependent Biomass in the Integrated Biosphere Simulator (IBIS) Model

Comparison of sensible and latent heat fluxes from optical-microwave scintillometers and eddy covariance systems with respect to surface energy balance closure

Innovative approach for estimating evapotranspiration and gross primary productivity by integrating land data assimilation, machine learning, and multi-source observations

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