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JOURNALS // Computer Optics // Archive

Computer Optics, 2021 Volume 45, Issue 3, Pages 438–448 (Mi co927)

This article is cited in 7 papers

IMAGE PROCESSING, PATTERN RECOGNITION

Rice growth vegetation index 2 for improving estimation of rice plant phenology in costal ecosystems

K. Choudharyab, W. Shia, Y. Dongac

a Department of Land Surveying and Geo-informatics, Smart Cities Research Institute, The Hong Kong Polytechnic University, Hong Kong
b Samara National Research University, Moskovskoye Shosse 34, Samara, 443086, Russia
c Institute of Geophysics & Geomatics, China University of Geoscience, Wuhan, PR China

Abstract: Crop growth is one of the most important parameters of a crop and its knowledge before harvest is essential to help farmers, scientists, governments and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate rice crop growth in a single year. Sentinel 2 data provides frequent and consistent information to facilitate coastal monitoring from field scales. The aims of this study were to modify the rice growth vegetation index to improve rice growth phenology in the coastal areas. The rice growth vegetation index 2 is the best vegetation index, compared with 11 vegetation indices, plant height and biomass. The results demonstrate that the coefficient of rice growth vegetation index 2 was 0.83, has the highest correlation with plant height. Rice growth vegetation index 2 is more appropriate for enhancing and obtaining rice phenology information. This study analyses the best spectral vegetation indices for estimating rice growth.

Keywords: crop growth, spectral indices, phenology, rice growth vegetation index 2.

Received: 29.10.2020
Accepted: 25.02.2021

Language: English

DOI: 10.18287/2412-6179-CO-827



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