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Agriculture Phenology Monitoring Using NDVI Time Series Based on Remote Sensing Satellites: A Case Study of Guangdong, China

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Abstract—

This article presents the use of the Normalized Differences Vegetation Index (NDVI) time series based change detection method for agriculture phenology monitoring. NDVI make use of the multi-spectral remote sensing data band combinations techniques to find out landscape such as agriculture, vegetation, land use/cover, water bodies and forest. Geographic Information System (GIS) technology is becoming an essential tool to combing multiple maps and information from different sources as satellite, field and socio-economic data. Landsat 8 and Sentinel-2 satellite data were used to generate NDVI time series from Sep. 2017 to Nov. 2018. This research work was the procedure by pre-processing, signal filtering and interpolation of monthly NDVI time series that represent a complete crop phonological cycle. NDVI method is applied according to its specialty range from –1 to +1. We divided whole agriculture area into five part according to NDVI Values such as no agriculture, low agriculture, medium agriculture, high agriculture and very high agriculture area. The simulation results show that the NDVI is highly useful in detecting the surface feature of the area, which is extremely beneficial for sustainable development of agriculture and decision making. The methodology of reform NDVI time series had been providing feasible to improve crop phenology mapping.

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Funding

This work is done through a PhD scholarship from PolyU Hong Kong and research grants from the Research Grants Council (HKSAR) grant project codes B-Q49D and 1-ZVE8. Authors would also like to acknowledge the support drawn from the Lands department and Agriculture department of Guangdong, China.

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Authors and Affiliations

  1. The Hong Kong Polytechnic University, Hong Kong, China

    Komal Choudhary & Wenzhong Shi

  2. Samara National Research University, 443086, Samara, Russia

    Komal Choudhary & Mukesh Singh Boori

  3. American Sentinel University, Colorado, United States

    Mukesh Singh Boori

  4. University of Rennes 2, Rennes, France

    Komal Choudhary & Samuel Corgne

Authors
  1. Komal Choudhary
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  2. Wenzhong Shi
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  3. Mukesh Singh Boori
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  4. Samuel Corgne
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Corresponding author

Correspondence to Komal Choudhary.

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Komal Choudhary, Shi, W., Boori, M.S. et al. Agriculture Phenology Monitoring Using NDVI Time Series Based on Remote Sensing Satellites: A Case Study of Guangdong, China. Opt. Mem. Neural Networks 28, 204–214 (2019). https://doi.org/10.3103/S1060992X19030093

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