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Crop growth monitoring through Sentinel and Landsat data based NDVI time-series
M.S. Boori 1,2,4, K. Choudhary 1,3,4, A.V. Kupriyanov 1,5
1 Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia,
2 American Sentinel University, Colorado, USA,
3 The Hong Kong Polytechnic University, Kowloon, Hong Kong,
4 University of Rennes 2, Rennes, France,
5 IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
Molodogvardeyskaya 151, 443001, Samara, Russia
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Язык статьи: English
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.
crop phenology, NDVI time-series, Sentinel-2 & Landsat, remote sensing.
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".
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