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JOURNALS // Computer Optics // Archive

Computer Optics, 2020 Volume 44, Issue 3, Pages 409–419 (Mi co803)

This article is cited in 32 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

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.

Received: 16.09.2019
Accepted: 27.02.2020

DOI: 10.18287/2412-6179-CO-635



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