6533b7d9fe1ef96bd126c377

RESEARCH PRODUCT

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

C.m. GevaertLammert KooistraJing TangJuha SuomalainenFrancisco Javier García-haro

subject

precision agricultureComputer sciencebusiness.industryUAVMultispectral imageHyperspectral imagingcomputer.software_genreSensor fusionPE&RCField (geography)Laboratory of Geo-information Science and Remote SensingWDVIunmixing-based data fusionTemporal resolutionComputer visionLaboratorium voor Geo-informatiekunde en Remote SensingArtificial intelligencePrecision agricultureSTARFMbusinesscomputerImage resolutionData integrationRemote sensing

description

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).

yearjournalcountryeditionlanguage
2014-06-01
10.1109/whispers.2014.8077607https://doi.org/10.1109/whispers.2014.8077607