Show simple item record

dc.date.accessioned2021-05-03T08:31:18Z
dc.date.available2021-05-03T08:31:18Z
dc.date.issued2021-04-20
dc.identifierdoi:10.17170/kobra-202104293776
dc.identifier.urihttp://hdl.handle.net/123456789/12775
dc.description.sponsorshipGefördert durch den Publikationsfonds der Universität Kasselger
dc.language.isoengeng
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmonsoon cropseng
dc.subjectleaf area indexeng
dc.subjectleaf chlorophyll concentrationeng
dc.subjectcrop water contenteng
dc.subjectmultispectraleng
dc.subjecthyperspectraleng
dc.subject.ddc580
dc.subject.ddc600
dc.titleComparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameterseng
dc.typeAufsatz
dcterms.abstractVarious remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.eng
dcterms.accessRightsopen access
dcterms.creatorWijesingha, Jayan
dcterms.creatorDayananda, Supriya
dcterms.creatorWachendorf, Michael
dcterms.creatorAstor, Thomas
dc.relation.doidoi:10.3390/s21082886
dc.subject.swdIndienger
dc.subject.swdMonsunger
dc.subject.swdErnteger
dc.subject.swdBlattflächenindexger
dc.subject.swdChlorophyllkonzentrationger
dc.subject.swdWassergehaltger
dc.subject.swdFernerkundungger
dc.subject.swdMultispektraltechnikger
dc.subject.swdHyperspektraler Sensorger
dc.type.versionpublishedVersion
dcterms.source.identifierEISSN 1424-8220
dcterms.source.issueIssue 8
dcterms.source.journalSenorseng
dcterms.source.volumeVolume 21
kup.iskupfalse
dcterms.source.articlenumber2886


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Namensnennung 4.0 International
Except where otherwise noted, this item's license is described as Namensnennung 4.0 International