Evaluating an unmanned aerial vehicle-based remote sensing system for estimation of rice nitrogen status

Junjun Lu, Yuxin Miao, Yanbo Huang, Wei Shi, Xiaoyi Hu, Xinbing Wang, Jun Wan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Active crop canopy sensors have been successfully used to estimate rice nitrogen (N) status non-destructively and guide in-season site-specific N management. However, It is time-consuming and challenging to carry the hand-held crop sensors and walk across large paddy fields. Satellite remote sensing is potentially more efficient for monitoring crop growth status across large areas, but is often limited by bad weather conditions, spatial resolution of the sensors, or repeat cycle of the satellite systems. Unmanned aerial vehicle (UAV)-based remote sensing is a promising approach to overcoming the limitations of ground sensing and satellite remote sensing. The objective of this study was to evaluate an UAV-based remote sensing system for estimating rice N status in Northeast China. Two N rate experiments including 11-leave variety Longjing 31 and 12-leave variety Longjing 21 were conducted in 2014 at Jiansanjiang Experiment Station of China Agricultural University, Heilongjiang Province, Northeast China. An Octocopter UAV equipped with a Mini Multi-Camera Array (Mini-MCA) imaging system was used in this study. Fifteen vegetation indices were evaluated to estimate aboveground biomass, plant N uptake, and leaf area index (LAI) at the panicle initiation and the stem elongation growth stages of the rice varieties. The preliminary results indicated that the Red Edge Difference Vegetation Index (REDVI) was best for estimating aboveground biomass (R2=0.85) and plant N uptake (R2=0.87), and the Difference Vegetation Index (DVI) was best for estimating LAI (R2=0.80) at panicle initiation stage. At stem elongation stage, the Red Edge Simple Ratio Index (RESRI) explained 75% and 69% of aboveground biomass and LAI variability respectively. The MERIS Terrestrial Chlorophyll Index (MTCI) and Soil Adjusted Vegetation Index (SAVI) explained 69% and 68% of plant N concentration and uptake variability, respectively. The UAV-based remote sensing system has good potential for estimating in-season rice N status and guiding topdressing N application. More studies are needed to develop UAV remote sensing-based precision N management strategies to improve N use efficiency of large scale rice farming in Northeast China.

Original languageEnglish (US)
Title of host publication2015 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages198-203
Number of pages6
ISBN (Electronic)9781467380874
DOIs
StatePublished - Sep 9 2015
Event4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015 - Istanbul, Turkey
Duration: Jul 20 2015Jul 24 2015

Publication series

Name2015 4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015

Other

Other4th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2015
CountryTurkey
CityIstanbul
Period7/20/157/24/15

Keywords

  • Biomass
  • Leaf area index
  • Plant nitrogen concentration
  • Plant nitrogen uptake
  • Precision agriculture
  • Precision nitrogen management

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