Literature DB >> 31982739

Geographical variations in gross primary production and evapotranspiration of paddy rice in the Korean Peninsula.

Seungtaek Jeong1, Jonghan Ko2, Minseok Kang3, Jongmin Yeom4, Chi Tim Ng5, Seung-Hoon Lee6, Yeon-Gil Lee7, Han-Yong Kim1.   

Abstract

The quantification of canopy photosynthesis and evapotranspiration of crops (ETc) is essential to appreciate the effects of environmental changes on CO2 flux and water availability in agricultural ecosystems and crop productivity. This study simulated the canopy photosynthesis and ET processes of paddy rice (Oryza sativa) based on the development of physiological modules (i.e., gross primary production [GPP] and ETc) and their incorporation into the GRAMI-rice model that uses remote sensing data. We also projected spatiotemporal variations in the GPP, ET, yield, and biomass of paddy rice at maturity using the updated GRAMI-rice model combined with geostationary satellite images to identify the relationships of canopy photosynthesis and ETc with crop productivity. GPP and ET data for paddy rice were obtained from three KoFlux sites in South Korea in 2015 and 2016. Vegetation indices were acquired from the Geostationary Ocean Color Imager (GOCI) of the Communication Ocean and Meteorological Satellite (COMS) from 2012 to 2017 and integrated into GRAMI-rice. GPP and ETc estimates using GRAMI-rice were in close agreement with flux tower estimates with Nash-Sutcliffe efficiency ranges of 0.40-0.79 for GPP and 0.49-0.62 for ETc. Also, GRAMI-rice was reasonably well incorporated with the COMS GOCI imagery and reproduced spatiotemporal variations in the GPP and ET of rice in the Korean peninsula. The current study results demonstrate that the updated GRAMI-rice model with the canopy photosynthesis and ETc modules is capable of reproducing spatiotemporal variations in CO2 assimilation and ET of paddy rice at various geographical scales and for regions of interest that are observable by satellite sensors (e.g., inaccessible North Korea).
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  CO(2) flux; Evapotranspiration; Model; Photosynthesis; Rice

Mesh:

Year:  2020        PMID: 31982739     DOI: 10.1016/j.scitotenv.2020.136632

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  2 in total

1.  Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth.

Authors:  Seungtaek Jeong; Jonghan Ko; Taehwan Shin; Jong-Min Yeom
Journal:  Sci Rep       Date:  2022-05-30       Impact factor: 4.996

2.  Simulation of Wheat Productivity Using a Model Integrated With Proximal and Remotely Controlled Aerial Sensing Information.

Authors:  Taehwan Shin; Jonghan Ko; Seungtaek Jeong; Ashifur Rahman Shawon; Kyung Do Lee; Sang In Shim
Journal:  Front Plant Sci       Date:  2021-03-24       Impact factor: 5.753

  2 in total

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