Spatial analysis of rice phenology using sentinel 2 and UAV in Parakansalak, Sukabumi District, Indonesia

Rokhmatuloh, Supriatna, Adi Wibowo, Iqbal Putut Ash Shidiq

Research output: Contribution to journalArticle

Abstract

West Java is the third province with the largest area of paddy field. On the province scale, there are five districts with more than 60,000 hectares of the paddy field area. Sukabumi is the fifth largest district, with 6.8 percent of the area covered with paddy field. Although it is not the greatest number, Sukabumi is the largest producer of paddy with more than 6 tons per hectare, especially in 2015. Rice is the primary food for most Indonesian. Therefore, monitoring the rice planting regarding the phenology, planting area, and productivity is a critical process. Information from the process is very important to address the national issues on food security. This study uses the excellence of remote sensing technology to cover a big area of paddy field in Sukabumi. The images from Sentinel 2 and Unmanned Aerial Vehicle (UAV) are utilized to generate the Normalized Difference Vegetation Index (NDVI). The objectives of this study are two folds: (i) to create NDVI map from both Sentinel 2 and UAV; and (ii) to analyze rice crop phenology from the NDVI value. With NDVI, this study can determine the growth stage of paddy by discriminating each stage based on the spectral value. The planting phases that have discovered in the area are divided into land preparation, vegetative, generative, and harvesting. Based on the NDVI value, it is known that the vegetative stage ranges from 0.18 to 0.80. The study concludes that results from both Sentinel 2 and UAV can be used to show the distribution of paddy based on different growth stages.

Original languageEnglish
Pages (from-to)205-210
Number of pages6
JournalInternational Journal of GEOMATE
Volume19
Issue number72
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • Rice crop phenology
  • Sentinel 2
  • Spatial analysis
  • UAV
  • Vegetation index

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