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Improved gross primary production estimation in rice fields through integrated multi-scale methodologies.

Bora Lee1, Hyojung Kwon2, Peng Zhao3

  • 1Warm Temperate and Subtropical Forest Research Center National Institute of Forest Science Seogwipo-si 63582 South Korea.

Plant-Environment Interactions (Hoboken, N.J.)
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances rice gross primary production (GPP) estimation using a novel development curve approach. It integrates remote sensing, flux data, and modeling for accurate carbon capture assessment in agricultural ecosystems.

Keywords:
carbon uptakegross primary productionphotosynthesis modelrice paddy

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Area of Science:

  • Agricultural Science
  • Ecosystem Ecology
  • Remote Sensing

Background:

  • Agricultural ecosystems play a crucial role in regional carbon balances and carbon sequestration through crop yield.
  • Accurate estimation of gross primary production (GPP) is vital for understanding these processes, especially for staple crops like rice.

Purpose of the Study:

  • To develop and validate a remote sensing-based method for estimating rice GPP.
  • To improve the accuracy of GPP estimations by integrating multiple data sources and a novel modeling approach.

Main Methods:

  • Combined eddy covariance (EC) flux data, process-based modeling, satellite-derived vegetation indices (VIs), and field surveys.
  • Developed a new approach using consistent rice development curves to estimate leaf area index (LAI) and carboxylation capacity (Vcuptake).
  • Compared estimations from generic regression models with those derived from the consistent development curve approach.

Main Results:

  • Generic regression models showed underestimations in GPP.
  • The new approach using consistent development curves for LAI and Vcuptake achieved high agreement with R2 values from 0.76 to 0.92.
  • Demonstrated improved linkages between ground-based data, flux measurements, process models, and remote sensing for GPP estimation.

Conclusions:

  • The consistent development curve approach offers a more accurate method for estimating rice GPP compared to traditional linear models.
  • Integrating diverse data sources and biological insights enhances the prediction of carbon gain and agricultural yields.
  • This methodology has potential for broader application in estimating GPP in various agricultural systems.