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Computer Optics, 2020, Volume 44, Issue 3, paper published in the English version journal
DOI: https://doi.org/10.18287/2412-6179-CO-635
(Mi co803)
 

This article is cited in 25 scientific papers (total in 25 papers)

IMAGE PROCESSING, PATTERN RECOGNITION

Crop growth monitoring through Sentinel and Landsat data based NDVI time-series

M. Booriabc, K. Choudharycad, A. V. Kupriyanovae

a Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia
b American Sentinel University, Colorado, USA
c University of Rennes 2, Rennes, France
d The Hong Kong Polytechnic University, Kowloon, Hong Kong
e IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS, Molodogvardeyskaya 151, 443001, Samara, Russia
References:
Abstract: Crop growth monitoring is an important phenomenon for agriculture classification, yield estimation, agriculture field management, improve productivity, irrigation, fertilizer management, sustainable agricultural development, food security and to understand how environment and climate change effect on crops especially in Russia as it has a large and diverse agricultural production. In this study, we assimilated monthly crop phenology from January to December 2018 by using the NDVI time series derived from moderate to high Spatio-temporal resolution Sentinel and Landsat data in cropland field at Samara airport area, Russia. The results support the potential of Sentinel and Landsat data derived NDVI time series for accurate crop phenological monitoring with all crop growth stages such as active tillering, jointing, maturity and harvesting according to crop calendar with reasonable thematic accuracy. This satellite data generated NDVI based work has great potential to provide valuable support for assessing crop growth status and the above-mentioned objectives with sustainable agriculture development.
Keywords: crop phenology, NDVI time-series, Sentinel-2 & Landsat, remote sensing.
Funding agency Grant number
Ministry of Science and Higher Education of the Russian Federation 0026-2018-0102
Russian Foundation for Basic Research 15-29-03823 р_а
16-41-630761 р_а
17-01-00972 а
18-37-00418 а
This work was partially supported by the Ministry of education and science of the Russian Federation in the framework of the implementation of the Program of increasing the competitiveness of Samara University among the world’s leading scientific and educational centers for 2013-2020 years; by the Russian Foundation for Basic Research grants (# 15-29-03823, # 16-41-630761, # 17-01-00972, # 18-37-00418), in the framework of the state task #0026-2018-0102 "Optoinformation technologies for obtaining and processing hyperspectral data".
Received: 16.09.2019
Accepted: 27.02.2020
Document Type: Article
Language: Russian
Citation: M. Boori, K. Choudhary, A. V. Kupriyanov
Citation in format AMSBIB
\Bibitem{BooChoKup20}
\by M.~Boori, K.~Choudhary, A.~V.~Kupriyanov
\mathnet{http://mi.mathnet.ru/co803}
\crossref{https://doi.org/10.18287/2412-6179-CO-635}
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  • https://www.mathnet.ru/eng/co803
  • This publication is cited in the following 25 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Computer Optics
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    References:19
     
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