(44-3) 12 * << * >> * Русский * English * Содержание * Все выпуски
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
PDF, 4312 kB
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".
Boori MS, Choudhary K, Kupriyanov AV. Crop growth monitoring through Sentinel and Landsat data based NDVI time-series. Computer Optics 2020; 44(3): 409-419. DOI: 10.18287/2412-6179-CO-635.
- Boori MS, Choudhary K, Kupriyanov A, Kovelskiy V. Satellite data for Singapore, Manila and Kuala Lumpur city growth analysis. Data in Brief 2016; 7: 1576-1583. DOI: 10.1016/j.dib.2016.04.028.
- Griffiths P, Nendel C, Hostert P. Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sensing of Environment 2019; 220: 135-151.
- Boori MS, Choudhary K, Kupriyanov A, Sugimoto A, Evers M. Natural and environmental vulnerability analysis through remote sensing and GIS techniques: A case study of Indigirka River basin, Eastern Siberia, Russia. Proc SPIE 2016; 10005: 100050U. DOI: 10.1117/12.2240917.
- Becker-Reshef I, Justice C, Sullivan M, Vermote E, Tucker C, Anyamba A, Small J, Pak E, Masuoka E, Schmaltz J, Hansen M, Pittman K, Birkett C, Williams D, Reynolds C, Doorn B. Monitoring global croplands with coarse resolution earth observations: the global agriculture monitoring (GLAM) project. Remote Sens 2010; 2: 1589-1609.
- Lebourgeois V, Dupuy S, Vintrou E, Ameline M, Butler S, Bégué A. A combined random forest and OBIA classification scheme for mapping smallholder agriculture at different nomenclature levels using multisource data (simulated Sentinel-2 time series, VHRS and DEM). Remote Sens 2017; 9: 259.
- Skakun S, Franch B, Vermote E, Roger JC, Justice C, Masek J, Murphy E. Winter wheat yield assessment using Landsat 8 and Sentinel-2 data. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018; 5964-5967.
- Matton N, Canto GS, Waldner F, Valero S, Morin D, Inglada J, Arias M, Bontemps S, Koetz B, Defourny P. An automated method for annual cropland mapping along the season for various globally-distributed agrosystems using high spatial and temporal resolution time series. Remote Sens 2015; 7: 13208-13232.
- Hufkens K, Melaas EK, Foster T, Ceballos F, Robles M, Kramer B. Monitoring crop phenology using a smartphone based near-surface remote sensing approach. Agricultural and Forest Meteorology 2019; 265: 327-337.
- Wang Y, Xue Z, Chen J. Spatio-temporal analysis of phenology in Yangtze River Delta based on MODIS NDVI time series from 2001 to 2015. Front Earth Sci 2019; 13(1): 92-110. DOI: 10.1007/s11707-018-0713-0.
- Jin X, Kumar L, Li Z, Feng H, Xu X, Yang G, Wang J. A review of data assimilation of remote sensing and crop models. Eur J Agron 2018; 92: 141-152.
- Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H. A crop phenology detection method using time-series MODIS data. Remote Sens Environ 2005; 96: 366-374.
- Zheng H, Cheng T, Yao X, Deng X, Tian Y, Cao W, Zhu Y. Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Res 2016; 198: 131-139.
- Manuel CT, Haro FJG, Busetto L, Luigi R, Beatriz M, Gilabert MA, Gustau CV, Fernando C, Mirco B. A critical comparison of remote sensing leaf area index estimates over rice-cultivated areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar system. Remote Sens 2018; 0(5): 763.
- Liu S, Li Q, Mao X, Zhang J. Evaluation on consistency between HJ-1 CCDand TM images in monitoring fractional green vegetation cover. International Geoscience and Remote Sensing Symposium (IGARSS) 2011: 1005-1008. DOI: 10.1109/IGARSS.2011.6049303.
- Li Q, Wang C, Zhang B, Lu L. Object-based crop classification with Landsat-MODIS enhanced time-series data. Remote Sens 2015; 7(12): 16091-16107.
- Boori MS, Choudhary K, Kupriyanov A, Kovelskiy V. Urbanization data of Samara City, Russia. Data in Brief 2016; 6: 885-889. DOI: 10.1016/j.dib.2016.01.056.
- Kundu A, Patel NR, Saha SK, Dutta D. Desertification in western Rajasthan (India): an assessment using remote sensing derived rain-use efficiency and residual trend methods. Natural Hazards 2017, 86: 297-313.
- Patel NR, Yadav K. Monitoring spatio-temporal pattern of drought stress using integrated drought index over Bundelkhand region, India. Natural Hazards 2015, 77: 663-677.
- Earth observatory NASA. Source: <http://earthobservatory.nasa.gov/Features/MeasuringVegetation/>.
- Congalton RG, Green K. Assessing the accuracy of remotely sensed data: Principles and practices. 2nd ed. Boca Raton: Lewis Publishers; 2009.
- Justice CO, Townshend JRG, Holben BN, Tucker CJ. Analysis of the phenology of global vegetation using meteorological satellite data. Int J Remote Sens 1985, 6: 1271-1318.
- Leff B, Ramankutty N, Foley JA. Geographic distribution of major crops across the world. Global Biogeochem Cycles 2004; 18: GB1009.
- World population prospects: The 2015 revision. In Book: Population division of the department of economic and social affairs of the United Nations Secretariat. New York: Department of Economic and Social Affairs; 2015.
© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: email@example.com ; тел: +7 (846) 242-41-24 (ответственный
секретарь), +7 (846)
332-56-22 (технический редактор), факс: +7 (846) 332-56-20