Mapping rice area and yield in northeastern asia by incorporating a crop model with dense vegetation index profiles from a geostationary satellite

Yeom, Jong-Min and Jeong, Seungtaek and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Ko, Jonghan (2021) Mapping rice area and yield in northeastern asia by incorporating a crop model with dense vegetation index profiles from a geostationary satellite. GIScience and Remote Sensing.

[img]
Preview
Text (Published - ArticleFirst Version)
Mapping rice area and yield in northeastern asia by incorporating a crop model with dense vegetation index profiles from a geostationary satellite.pdf
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (14MB) | Preview

Abstract

Acquiring accurate and timely information on the spatial distribution of paddy rice fields and the corresponding yield is an important first step in meeting the regional and global food security needs. In this study, using dense vegetation index profiles and meteorological parameters from the Communication, Ocean, and Meteorological Satellite (COMS) geostationary satellite, we estimated paddy areas and applied a novel approach based on a remote sensing-integrated crop model (RSCM) to simulate spatiotemporal variations in rice yield in Northeastern Asia. Estimated seasonal vegetation profiles of plant canopy from the Geostationary Ocean Color Imager (GOCI) were constructed to classify paddy fields as well as their productivity based on a bidirectional reflectance distribution function model (BRDF) and adjusted normalized difference vegetation indices (VIs). In the case of classification, the overall accuracy for detected paddy fields was 78.8% and the spatial distribution of the paddy area was well represented for each selected county based on synthetic applications of dense-time GOCI vegetation index and MODIS water index. For most of the Northeast Asian administrative districts investigated between 2011 and 2017, simulated rice mean yields for each study site agreed with the measured rice yields, with a root-mean-square error of 0.674 t ha−1, a coefficient of determination of 0.823, a Nash-Sutcliffe efficiency of 0.524, and without significant differences (p-value = 0.235) according to a sample t-test (α = 0.05) for the entire study period. A well-calibrated RSCM, driven by GOCI images, can facilitate the development of novel approaches for the monitoring and management of crop productivity over classified paddy areas, thereby enhancing agricultural decision support systems.


Statistics for USQ ePrint 40509
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online: 11 January 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivatives License (http://creativecommons.org/licenses/by-ncnd/ 4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 12 Jan 2021 00:05
Last Modified: 12 Jan 2021 00:14
Uncontrolled Keywords: crop model; rice classification; rice yield; geostationary satellite; northeast Asia
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070104 Agricultural Spatial Analysis and Modelling
05 Environmental Sciences > 0502 Environmental Science and Management > 050299 Environmental Science and Management not elsewhere classified
Fields of Research (2020): 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300206 Agricultural spatial analysis and modelling
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
Socio-Economic Objectives (2020): 18 ENVIRONMENTAL MANAGEMENT > 1899 Other environmental management > 189999 Other environmental management not elsewhere classified
Identification Number or DOI: https://doi.org/10.1080/15481603.2020.1853352
URI: http://eprints.usq.edu.au/id/eprint/40509

Actions (login required)

View Item Archive Repository Staff Only