Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications

C.M. Gevaert, J. Tang, F.J. García-Haro, J. Suomalainen, L. Kooistra

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

10 Citations (Scopus)

Abstract

Remote sensing is a key tool for precision agriculture applications as it is capable of capturing spatial and temporal variations in crop status. However, satellites often have an inadequate spatial resolution for precision agriculture applications. High-resolution Unmanned Aerial Vehicles (UAV) imagery can be obtained at flexible dates, but operational costs may limit the collection frequency. The current study utilizes data fusion to create a dataset which benefits from the temporal resolution of Formosat-2 imagery and the spatial resolution of UAV imagery with the purpose of monitoring crop growth in a potato field. The correlation of the Weighted Difference Vegetation Index (WDVI) from fused imagery to measured crop indicators at field level and added value of the enhanced spatial and temporal resolution are discussed. The results of the STARFM method were restrained by the requirement of same-day base imagery. However, the unmixing-based method provided a high correlation to the field data and accurately captured the WDVI temporal variation at field level (r=0.969).

Original languageEnglish
Title of host publication6th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2014
PublisherIEEE computer society
Number of pages4
ISBN (Electronic)9781467390125
ISBN (Print)9781467390132
DOIs
Publication statusPublished - 26 Oct 2017
Event6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 - Lausanne, Switzerland
Duration: 24 Jun 201427 Jun 2014

Conference

Conference6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014
CountrySwitzerland
CityLausanne
Period24/06/1427/06/14

Fingerprint

Unmanned aerial vehicles (UAV)
Agriculture
Crops
Data fusion
Remote sensing
Satellites
Monitoring
Costs

Keywords

  • precision agriculture
  • STARFM
  • UAV
  • unmixing-based data fusion
  • WDVI

Cite this

Gevaert, C. M., Tang, J., García-Haro, F. J., Suomalainen, J., & Kooistra, L. (2017). Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications. In 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 [8077607] IEEE computer society. https://doi.org/10.1109/WHISPERS.2014.8077607
Gevaert, C.M. ; Tang, J. ; García-Haro, F.J. ; Suomalainen, J. ; Kooistra, L. / Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications. 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014. IEEE computer society, 2017.
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Gevaert, CM, Tang, J, García-Haro, FJ, Suomalainen, J & Kooistra, L 2017, Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications. in 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014., 8077607, IEEE computer society, 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014, Lausanne, Switzerland, 24/06/14. https://doi.org/10.1109/WHISPERS.2014.8077607

Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications. / Gevaert, C.M.; Tang, J.; García-Haro, F.J.; Suomalainen, J.; Kooistra, L.

6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014. IEEE computer society, 2017. 8077607.

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

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Gevaert CM, Tang J, García-Haro FJ, Suomalainen J, Kooistra L. Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications. In 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014. IEEE computer society. 2017. 8077607 https://doi.org/10.1109/WHISPERS.2014.8077607