Post on 20-Jan-2016
HYPERSPECTRALREMOTE SENSINGOF VEGETATIONI '
HYPERSPECTRALREMOTE SENSINGOF VEGETATION
Edited by
Prasad S. ThenkabailJohn G. LyonAlfredo Huete
HYPERSPECTRALREMOTE SENSINGOF VEGETATION
CRC Press is an imprint of theTaylor & Francis Group, an informa business
Boca Raton London New York
CRC PressTaylor & Francis Group6000 Broken Sound Parkway NW, Suite 300Boca Raton, FL 33487-2742
2012 by Taylor & Francis Group, LLCCRC Press is an imprint of Taylor & Francis Group, an Informa business
No claim to original U.S. Government works
Printed in the United States of America on acid-free paperVersion Date: 2011908
International Standard Book Number: 978-1-4398-4537-0 (Hardback)
This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the valid-ity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or uti-lized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopy-ing, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers.
For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged.
Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.Visit the Taylor & Francis Web site athttp://www.taylorandfrancis.comand the CRC Press Web site athttp://www.crcpress.com
vContentsForeword............................................................................................................................................ixPreface...............................................................................................................................................xiAcknowledgments.......................................................................................................................... .xiiiEditors.............................................................................................................................................xviiList.of.Acronyms.and.Abbreviations...............................................................................................xxiContributors...................................................................................................................................xxxi
Part I Introduction and Overview
Chapter 1 Advances.in.Hyperspectral.Remote.Sensing.of.Vegetation.and.Agricultural.Croplands......................................................................................................................3
Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete
Part II Hyperspectral Sensor Systems
Chapter 2 Hyperspectral.Sensor.Characteristics:.Airborne,.Spaceborne,.Hand-Held,.andTruck-Mounted;.Integration.of.Hyperspectral.Data.with.LIDAR....................... 39
Fred Ortenberg
Chapter 3 Hyperspectral.Remote.Sensing.in.Global.Change.Studies.........................................69
Jiaguo Qi, Yoshio Inoue, and Narumon Wiangwang
Part III Data Mining, algorithms, Indices
Chapter 4 Hyperspectral.Data.Mining.........................................................................................93
Sreekala G. Bajwa and Subodh S. Kulkarni
Chapter 5 Hyperspectral.Data.Processing.Algorithms.............................................................. 121
Antonio Plaza, Javier Plaza, GabrielMartn, and Sergio Snchez
vi Contents
Part IV Leaf and Plant Biophysical and Biochemical Properties
Chapter 6 Nondestructive.Estimation.of.Foliar.Pigment.(Chlorophylls,.Carotenoids,.andAnthocyanins).Contents:.Evaluating.a.Semianalytical.Three-Band.Model...... 141
Anatoly A. Gitelson
Chapter 7 Forest.Leaf.Chlorophyll.Study.Using.Hyperspectral.Remote.Sensing..................... 167
Yongqin Zhang
Chapter 8 Estimating.Leaf.Nitrogen.Concentration.(LNC).of.Cereal.Crops.withHyperspectral.Data........................................................................................... 187
Yan Zhu, Wei Wang, and Xia Yao
Chapter 9 Characterization.on.Pastures.Using.Field.and.Imaging.Spectrometers....................207
Izaya Numata
Chapter 10 Optical.Remote.Sensing.of.Vegetation.Water.Content............................................. 227
Colombo Roberto, Busetto Lorenzo, MeroniMichele, Rossini Micol, andPanigada Cinzia
Chapter 11 Estimation.of.NitrogenContent.in.Crops.and.Pastures.Using.Hyperspectral.Vegetation.Indices.....................................................................................................245
Daniela Stroppiana, F. Fava, M. Boschetti, and P.A. Brivio
Part V Vegetation Biophysical Properties
Chapter 12 Spectral.Bioindicators.of.Photosynthetic.Efficiency.and.Vegetation.Stress.............265
Elizabeth M. Middleton, K. Fred Huemmrich, Yen-Ben Cheng, and Hank A. Margolis
Chapter 13 Spectral.and.Spatial.Methods.of.Hyperspectral.Image.Analysis.for.Estimation.of.Biophysical.and.Biochemical.Properties.of.Agricultural.Crops........................... 289
Victor Alchanatis and Yafit Cohen
Chapter 14 Hyperspectral.Vegetation.Indices.............................................................................309
Dar A. Roberts, Keely L. Roth, and Ryan L. Perroy
Chapter 15 Remote.Sensing.Estimation.ofCrop.Biophysical.Characteristics.atVariousScales...............................................................................................329
Anatoly A. Gitelson
viiContents
Part VI Vegetation Processes and Function (Et,Water Use, GPP, LUE, Phenology)
Chapter 16 Hyperspectral.Remote.Sensing.Tools.for.Quantifying.Plant.Litter.and.Invasive.Species.in.Arid.Ecosystems...................................................................................... 361
Pamela Lynn Nagler, B.B. Maruthi Sridhar, AarynDyamiOlsson, WillemJ.D.van Leeuwen, and Edward P. Glenn
Part VII Species Identification
Chapter 17 Crop.Type.Discrimination.Using.Hyperspectral.Data.............................................. 397
Lnio Soares Galvo, Jos Carlos Neves Epiphanio, FbioMarcelo Breunig, and Antnio Roberto Formaggio
Chapter 18 Identification.of.Canopy.Species.in.Tropical.Forests.UsingHyperspectralData.................................................................................423
Matthew L. Clark
Chapter 19 Detecting.and.Mapping.Invasive.Plant.Species.by.Using.Hyperspectral.Data........447
Ruiliang Pu
Part VIII Land Cover applications
Chapter 20 Hyperspectral.Remote.Sensing.for.Forest.Management...........................................469
Valerie Thomas
Chapter 21 Hyperspectral.Remote.Sensing.of.Wetland.Vegetation............................................487
Elijah Ramsey III and Amina Rangoonwala
Chapter 22 Characterization.of.Soil.Properties.Using.Reflectance.Spectroscopy...................... 513
E. Ben-Dor
Part IX Detecting Crop Management, Plant Stress, and Disease
Chapter 23 Analysis.of.the.Effects.of.Heavy.Metals.on.Vegetation.Hyperspectral.Reflectance.Properties............................................................................................... 561
E. Terrence Slonecker
viii Contents
Chapter 24 Hyperspectral.Narrowbands.and.Their.Indices.onAssessing.Nitrogen.Contents.ofCotton.Crop.Applications..................................................................................... 579
Jianlong Li, Cherry Li, Dehua Zhao, and Chengcheng Gang
Chapter 25 Using.Hyperspectral.Data.in.Precision.Farming.Applications................................. 591
Haibo Yao, Lie Tang, Lei Tian, Robert L. Brown, Deepak Bhatnagar, andThomas E. Cleveland
Part X Hyperspectral Data in Global Change Studies
Chapter 26 Hyperspectral.Data.in.Long-Term,.Cross-Sensor.Continuity.Studies...................... 611
Tomoaki Miura and Hiroki Yoshioka
Part XI Hyperspectral remote Sensing of Outer Planets
Chapter 27 Hyperspectral.Analysis.of.Rocky.Surfaces.on.the.Earth.and.Other.Planetary.Bodies........................................................................................................................ 637
R. Greg Vaughan, Timothy N. Titus, Jeffery R. Johnson, Justin J. Hagerty, LisaR. Gaddis, Laurence A.Soderblom, and Paul E. Geissler
Part XII Conclusions and Way Forward
Chapter 28 Hyperspectral.Remote.Sensing.of.Vegetation.and.Agricultural.Crops:.Knowledge.Gain.and.Knowledge.Gap.after.40.Years.of.Research..........................663
Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete
ix
ForewordThe.publication.of.this.book,.Hyperspectral Remote Sensing of Vegetation,.marks.a.milestone.in.the.application.of.imaging.spectrometry.to.studies.of.70%.of.the.Earths.landmass.that.is.vegetated..This.book.shows.not.only.the.breadth.of.international.involvement.in.the.use.of.hyperspectral.data,.but.also. the.breadth.of. innovative.application.of.mathematical. techniques. to.extract. information.from.the.image.data.
Imaging.spectrometry.evolved. from.a.combination.of. insights. from. the.vast.heterogeneity.of.reflectance.signatures.from.the.Earths.surface.seen.in.the.ERTS-1.(Landsat-1).four-band.images.and.the.field.spectra.that.were.acquired.to.help.more.fully.understand.the.causes.of.the.signatures..It.was.not.until.1979.when.the.first.hybrid.area.array.detectors,.mercury-cadmium-telluride.on.silicon.CCDs,.became.available.that.it.was.possible.to.build.an.imaging.spectrometer.capable.of.operating.at.wavelengths.beyond.1.0.m..The.AIS.(airborne.imaging.spectrometer),.developed.at.NASA/JPL,.had.only.32.cross-track.pixels.but.that.was.enough.for.geologists.to.clamor.for.its.development.to.see.between.the.bushes.to.determine.the.mineralogy.of.the.substrate..In.those.early.years,.vegetation.cover.was.just.a.nuisance!
In.the.early.1980s,.spectroscopic.analysis.was.driven.by.the.interest.to.identify.mineralogical.composition.by.exploiting.absorptions.found.in.the.SWIR.region.from.overtone.and.combination.bands.of.fundamental.vibrations.found.in.the.mid-IR.region.beyond.3.m.and.the.electronic.transi-tions.in.transition.elements.appearing,.primarily,.short.of.1.0.m..The.interests.of.the.geologists.had.been.incorporated.in.the.Landsat.TM.sensor.in.the.form.of.the.add-on.band.7.in.the.2.2.m.region.based.on.field.spectroscopic.measurements..However,.one.band,.even.in.combination.with.the.other.six,.did.not.meet.the.needs.for.mineral.identification..A.summary.of.mineralogical.analyses.is.pre-sented.by.Vaughan.et.al..in.this.volume..A.summary.of.the.historical.development.of.hyperspectral.imaging.can.be.found.in.Goetz.(2009).
At.the.time.of.the.first.major.publication.of.the.AIS.results.(Goetz.et.al.,.1985),.very.little.work.on.vegetation.analysis.using.imaging.spectroscopy.had.been.undertaken..The.primary.interest.was.in.identifying.the.relationship.of.the.chlorophyll.absorption.red-edge.to.stress.and.substrate.composi-tion.that.had.been.seen.in.airborne.profiling.and.in.field.spectral.reflectance.measurements..Most.of.the.published.literature.concerned.analyzing.NDVI,.which.only.required.two.spectral.bands.
In.the.time.leading.up.to.the.1985.publication,.we.had.only.an.inkling.of.the.potential.infor-mation.content.in.the.hundreds.of.contiguous.spectral.bands.that.would.be.available.to.us.with.the.advent.of.AVIRIS.(airborne.visible.and.infrared.imaging.spectrometer)..One.of.the.authors,.Jerry.Solomon,.presciently.added.the.term.hyperspectral.to.the.text.of.the.paper.to.describe.the.multidimensional.character.of.the.spectral.data.set,.or,.in.other.words,.the.mathemati-cally,.overdetermined.nature.of.hyperspectral.data.sets..The.term.hyperspectral.as.opposed.to.multispectral.data.moved.into.the.remote.sensing.vernacular.and.was.additionally.popularized.by.the.military.and.intelligence.community.
In.the.early.1990s,.as.higher.quality.AVIRIS.data.became.available,.the.first.analyses.of.vegeta-tion.using.statistical.techniques.borrowed.from.chemometrics,.also.known.as.NIRS.analysis.used.in.the.food.and.grain.industry,.were.undertaken.by.John.Aber.and.Mary.Martin.of.the.University.of.New.Hampshire..Here.nitrogen.contents.of.tree.canopies.were.predicted.from.reflectance.spectra.by.regression.techniques.using.reference.measurements.from.laboratory.wet.chemical.analyses.of.needle.and.leaf.samples.acquired.by.shooting.down.branches..At.the.same.time,.the.remote.sensing.community.began.to.recognize.the.value.of.too.many.spectral.bands.and.the.concomitant.wealth.of.spatial.information.that.was.amenable.to.information.extraction.by.statistical.techniques..One.of.
x Foreword
them.was.Eyal.Ben-Dor.who.pioneered.soil.analyses.using.hyperspectral.imaging.and.who.is.one.of.the.contributors.to.this.volume.
As.the.quality.of.AVIRIS.data.grew,.manifested.in.increasing.SNR,.an.ever-increasing.amount.of.information.could.be.extracted.from.the.data..This.quality.was.reflected.in.the.increasing.number.of.nearly.noiseless.principal.components.that.could.be.obtained.from.the.data.or,.in.other.words,.its.dimensionality..The.explosive.advances.in.desktop.computing.made.possible.the.application.of.image.processing.and.statistical.analyses.that.revolutionized.the.uses.of.hyperspectral.imaging..Joe.Boardman.and.others.at.the.University.of.Colorado.developed.what.has.become.the.ENVI.software.package.to.make.possible.the.routine.analysis.of.hyperspectral.image.data.using.unmixing.tech-niques.to.derive.the.relative.abundance.of.surface.materials.on.a.pixel.by.pixel.basis.
Many.of.the.analysis.techniques.discussed.in.this.volume,.such.as.band.selection.and.various.indices,.are.rooted.in.principal.components.analysis..The.eigenvector.loadings.or.factors.indicate.which.spectral.bands.are.the.most.heavily.weighted.allowing.others.to.be.discarded.to.reduce.the.noise.contribution..As.sensors.become.better,.more.information.will.be.extractable.and.fewer.bands.will.be.discarded..This.is.the.beauty.of.hyperspectral.imaging,.allowing.the.choice.of.the.number.of.eigenvectors.to.be.used.for.a.particular.problem..Computing.power.has.reached.such.a.high.level.that.it.is.no.longer.necessary.to.choose.a.subset.of.bands.just.to.minimize.the.computational.time.
As. regression. techniques. such.as.PLS. (partial. least. squares).become. increasingly.adopted. to.relate.a.particular.vegetation.parameter.to.reflectance.spectra,.it.must.be.remembered.that.the.qual-ity.of.the.calibration.model.is.a.function.of.both.the.spectra.and.the.reference.measurement..With.spectral.measurements.of.organic.and.inorganic.compounds.under.laboratory.conditions,.we.have.found.that.a.poor.model.with.a.low.coefficient.of.determination.(r2).is.most.often.associated.with.inaccurate. reference. measurements,. leading. to. the. previously. intuitive. conclusion. that. spectra.dont.lie.
Up.to.this.point,.AVIRIS.has.provided.the.bulk.of.high-quality.hyperspectral. image.data.but.on.an.infrequent.basis..Although.Hyperion.has.provided.some.time.series.data,.there.is.no.hyper-spectral.imager.yet.in.orbit.that.is.capable.of.providing.routine,.high-quality.images.of.the.whole.Earth.on.a.consistent.basis..The.hope.is.that.in.the.next.decade.HyspIRI.will.be.providing.VNIR.and.SWIR.hyperspectral. images. every. three.weeks. and.multispectral. thermal.data. every.week..This.resource.will.revolutionize.the.field.of.vegetation.remote.sensing.since.so.much.of.the.useful.information.is.bound.up.in.the.seasonal.growth.cycle..The.combination.of.the.spectral,.spatial,.and.temporal.dimensions.will.be.ripe.for.the.application.of.statistical.techniques.and.the.results.will.be.extraordinary.
Dr. Alexander F. H. Goetz, PhDChairman and Chief Scientist, ASD Inc.
Boulder, Colorado
REFERENCES
Goetz,.A.F.H..2009..Three.decades.of.hyperspectral.imaging.of.the.Earth:.A.personal.view,.Remote Sensing of Environment,.113,.S5S16.
Goetz,.A.F.H.,.G..Vane,.J..Solomon,.and.B.N..Rock..1985..Imaging.spectrometry.for.Earth.remote.sensing,.Science,.228,.11471153.
xi
PrefaceOver. the. years,. I. have. seen. a. real. need. for. a. book. on. hyperspectral. remote. sensing. (imaging..spectroscopy). of. vegetation,. especially. given. the. recent. rapid. advances. made. in. imaging. spec-troscopy.and.opportunities.for.unique.applications.hitherto.thought.to.be.infeasible.using.broad-band. remote. sensing.. The. need. for. a. book. to. catalogue. these. knowledge. advances. in. the. study.of. terrestrial.vegetation.using.hyperspectral.narrowband.data. is.of.critical. importance. to.a.wide.spectrum.of.scientific.community,.students,.and.professional.application.practitioners..Given.this.need,. the.goal.of.this.book.was.to.provide.a.comprehensive.set.of.chapters.documenting.knowl-edge.advances.made.in.applying.hyperspectral.remote.sensing.technology.in.the.study.of.terrestrial.vegetation..This.is.a.very.practical.offering.about.a.complex.subject.that.is.rapidly.advancing.its.knowledge-base. and. practical. utility. in. wide. array. of. applications.. In. a. very. practical. way,. the.book.demonstrates.the.experience,.utility,.methods,.and.models.used.in.studying.vegetation.using.hyperspectral.data..Written.by.leading.experts.in.the.global.arena,.each.chapter.(a).focuses.on.spe-cific..applications,.(b)reviews.state-of-the-art.knowledge,.(c).highlights.the.advances.made,.and.(d)provides. .guidance.for.appropriate.use.of.hyperspectral.(or. imaging.spectroscopy).data.in.the.study.of.vegetation.such.as.crop.yield.modeling,.crop.biophysical.and.biochemical.property.charac-terization,.crop.moisture.assessment,.species.identification,.spectrally.separating.vegetation.types,.and.modeling.biophysical.and.biochemical.quantities.
This. book. focuses. specifically. on. hyperspectral. remote. sensing. as. applied. to. terrestrial. veg-etation.applications..This.is.a.big.market.area,.and.the.chapters.discuss.in.detail.a.wide.array.of.applications.such.as.agricultural.croplands;.study.of.crop.moisture.and.forests;.and.numerous.other.applications.such.as.droughts,.crop.stress,.crop.productivity,.and.water.productivity..To.the.best.of.our.knowledge,.there.is.no.comparable.book,.source,.and/or.organization.that.has.brought.this.body.of.knowledge.together.in.one.place,.making.this.a.must.buy.for.professionals..This.is.clearly.a.unique.contribution.whose.time.has.come..The.highlights.of.the.book.include:
. 1..Best.global.expertise.on.hyperspectral. remote.sensing.of.vegetation,.agricultural.crops,.crop.water.use,.plant.species.detection,.crop.productivity,.wetland.studies,.forest.type.and.species.characterization,.carbon.flux.assessments.from.vegetation,.and.water.productivity.mapping.and.modeling.
. 2..Clear.articulation.of.methods.to.conduct.the.work;.very.practical.
. 3..Comprehensive.review.of.the.existing.technology.and.clear.guidance.on.how.best.to.use.hyperspectral.data.for.various.applications.
. 4..Case.studies.from.a.variety.of.continents.with.their.own.subtle.requirements.
. 5..Complete.solutions.from.methods.to.applications.inventory,.modeling,.and.mapping.
Hyperspectral.narrow-band.spectral.data,.as.discussed.in.various.chapters.of.this.book,.are.already.fast.emerging.as.practical.solutions.in.modeling.and.mapping.vegetation..Recent.research.has.dem-onstrated.the.effectiveness.of.hyperspectral.data,.as.discussed.in.the.28.chapters.of.this.book,.in.(a).quantifying.agricultural.crops.with.regard.to.their.biophysical.and.harvest.yield.characteristics,.(b).modeling.forest.canopy.biochemical.properties,.(c).establishing.plant.and.soil.moisture.condi-tions,. (d).detecting.crop.stress.and.disease,. (e).mapping. leaf.chlorophyll.content.as. it. influences.crop.production,.(f).identifying.plants.affected.by.contaminants.such.as.arsenic,.(g).demonstrating.sensitivity.to.plant.nitrogen.content,.and.(h).invasive.species.mapping..The.ability.to.significantly.better.quantify,.model,.and.map.plant.chemical,.physical,.and.water.properties.using.hyperspectral.narrowband.data.is.well.established.and.has.great.utility..The.chapters.discuss.in-depth.approaches.
xii Preface
and.methods.of.modeling.and.mapping..vegetation.using.optimal.hyperspectral.narrowbands.(by.dropping.redundant.bands),.hyperspectral.vegetation.indices,.and.whole.spectral.analysis.
Even.though.these.accomplishments.and.capabilities.have.been.reported.in.various.places,.the.need.for.a.collective.knowledge.bank.that.links.these.various.advances.in.one.place.was.miss-ing..Further,.most.scientific.papers.address.specific.aspects.of.research,.neither.providing.a.com-prehensive.assessment.of.advances.that.have.been.made.nor.providing.any.information.as.to.how.the.professional.can.implement.those.advances.in.their.work..For.example,.even.though.scientific.journals.report.practical.applications.of.hyperspectral.narrow-bands,.one.has.still. to.canvass. the.literature.broadly.to.obtain.the.pertinent.facts..Since.several.papers.report.this,.there.is.a.need.to.synthesize.these.findings.so.that.the.reader.can.get.the.correct.picture.of.issues.such.as.selecting.the.best.wavebands.for.their.practical.applications,.best.approaches.and.methods.for.efficient.classifica-tion.of.hyperspectral.data.to.study.vegetation,.and.best.models.for.plant.biophysical.and.biochemi-cal.quantities..Approaches.and.strategies.to.tackle.these.issues.are.discussed.in.details.in.various.chapters..Studies.may.differ.when.describing. the.best.methods.for.detecting.parameters.such.as.crop.moisture.variability,.chlorophyll.content,.and.stress.levels..Thereby,.a.professional.will.need.the.sort.of.synthesis.and.detail.provided.in.each.chapter.of.this.book.in.order.to.adopt.best.practices.for.their.own.work.
This.book.can.be.used.for.advanced.graduate.courses.as.well.as.by.professionals,.policy..makers,.governments,.and.research.organizations.when.in.need.of.proven.methods.to.address.a.wide.array.of. issues. pertaining. to. study. of. the. Planet. Earth. using. hyperspectral. (imaging. spectroscopy)..narrowband.data.
Dr. Prasad S. ThenkabailEditor in Chief
Hyperspectral Remote Sensing of Vegetation
xiii
AcknowledgmentsThe.book.was.made.possible.by.sterling.contributions.from.leading.professionals.from.around.the.world.in.the.area.of.hyperspectral.remote.sensing.(or.imaging.spectroscopy).of.vegetation.and.agri-cultural.crops..As.can.be.seen.from.the.list.of.authors.and.coauthors,.these.are.basically.the.who.is.who.in.hyperspectral.remote.sensing.of.vegetation..All. the.contributors.have.written.insight-ful. chapters,. which. is. an. outcome. of. years. of. dedicated. research,. to. make. the. book. appealing.to. a. broad. section. of. readers. dealing. with. remote. sensing.. My. gratitude. goes. to. (mentioned. in.no.particular.order).the.following.authors.(names.of.lead.authors.of.the.chapters.appear.in.bold):.Drs. Fred Ortenberg.(Technion,.Israel.Institute.of.Technology,.Israel),.Jiaguo Qi.(Michigan.State.University,. United. States),. Sreekala Bajawa. (University. of. Arkansas,. United. States),. Antonio Plaza. (University. of. Extremadura,. Spain),. Anatoly Gitelson. (University. of. Nebraska-Lincoln,.United.States),.Yongqin Zhang.(University.of.Toronto,.Canada),.Yan Zhu.(Nanjing.Agricultural.University,. China),. Izaya Numata. (South. Dakota. State. University,. United. States),. Roberto Colombo.(University.of.Milan-Bicocca,.Italy),.Daniela Stroppiana.(Institute.for.Electromagnetic.Sensing.of. the.Environment,.Italy),.Elizabeth Middleton. (NASA.Goddard.Space.Flight.Center,.United. States),. Victor Alchanatis. (Agricultural. Research. Organization,. Volcani. Center,. Israel),.Dar Roberts. (University. of. California. at. Santa. Barbara,. United. States),. Pamela Nagler. (U.S..Geological.Survey.[USGS],.United.States),.Lnio Soares Galvo.(Instituto.Nacional.de.Pesquisas.Espaciais.[INPE]Brazil),.Matthew L. Clark.(Sonoma.State.University,.United.States),.Ruiliang Pu.(University.of.South.Florida,.United.States),.Valerie Thomas.(Virginia.Tech.,.United.States),.Elijah W. Ramsey III.(USGS,.United.States),.Eyan Ben Dor.(Tel.Aviv.University,.Israel),.Terry Slonecker.(USGS,.United.States),.Jianlong Li.(Nanjing.University,.China),.Haibo Yao.(Mississippi.State.University,.United.States),.Tomoaki Miura.(University.of.Hawaii,.United.States),.R. Greg Vaughan. (USGS,. United. States),. Yoshio. Inoue. (National. Institute. for. Agro-Environmental.Sciences,.Japan),.Dr..Narumon.Wiangwang.(Royal.Thai.Government,.Thailand),.Subodh.Kulkarni.(University.of.Arkansas,.United.States),.Javier.Plaza.(University.of.Extremadura,.Spain),.Gabriel.Martin. (University. of. Extremadura,. Spain),. Segio. Snchez. (University. of. Extremadura,. Spain),.Wei. Wang. (Nanjing. Agricultural. University,. China),. Xia. Yao. (Nanjing. Agricultural. University,.China),.Busetto.Lorenzo.(Universit.Milano-Bicocca,.Italy),.Meroni.Michele.(Universit.Milano-Bicocca,. Italy),. Rossini. Micol. (Universit. Milano-Bicocca,. Italy),. Panigada. Cinzia. (Universit.Milano-Bicocca,.Italy),.Fava,.F..(Universit.degli.Studi.di.Sassari,.Italy),.Boschetti,.M..(Institute.for.Electromagnetic.Sensing.of.the.Environment,.Italy),.Brivio,.P..A..(Institute.for.Electromagnetic.Sensing. of. the. Environment,. Italy),. K.. Fred. Huemmrich. (University. of. Maryland,. Baltimore.County,.United.States),.Yen-Ben.Cheng.(Earth.Resources.Technology,.Inc.,.United.States),.Hank.A..Margolis.(Centre.dtudes.de.la.Fort,.Canada),.Yafit.Cohen.(Agricultural.Research.Organization,.Volcani.Center,.Israel),.Kelly.Roth.(University.of.California.at.Santa.Barbara,.United.States),.Ryan.L..Perroy. (University.of.Wisconsin-La.Crosse,.United.States),.Wei.Wang. (Nanjing.Agricultural.University,.China),.Dr..Xia.Yao.(Nanjing.Agricultural.University,.China),.Keely.L..Roth.(University.of. California,. Santa. Barbara,. United. States),. B.. B.. Marithi. Sridhar. (Bowling. Green. University,.United.States),.Aaryan.Dyami.Olsson.(Northern.Arizona.University,.United.States),.Willem.Van.Leeuwen. (University. of. Arizona,. United. States),. Edward. Glenn. (University. of. Arizona,. United.States),.Jos Carlos Neves Epiphanio. (INPE,.Brazil),.Fbio Marcelo Breunig. (INPE,.Brazil),.Antnio Roberto Formaggio. (INPE,. Brazil),. Amina. Rangoonwala. (IAP. World. Services,.Lafayette,.Los.Angeles),.Cheryl.Li.(Nanjing.University,.China),.Deghua.Zhao.(Nanjing.University,.
xiv Acknowledgments
China),. Cengcheng. Gang. (Nanjing. University,. China),. Lie. Tang. (Mississippi. State. University,.United.States),.Lei.Tian.(Mississippi.State.University,.United.States),.Robert.Brown.(Mississippi.State.University,.United.States),.Deepak.Bhatnagar. (Mississippi.State.University,.United.States),.Thomas.Cleveland.(Mississippi.State.University,.United.States),.Hiroki.Yoshioka.(Aichi.Prefectural.University,.Japan),.T..N..Titus.(USGS,.United.States),.J..R..Johnson.(USGS,.United.States),.J..J..Hagerty.(USGS,.United.States),.L..Gaddis.(USGS,.United.States),.L..A..Soderblom.(USGS,.United.States),.and.P..Geissler.(USGS,.United.States).
My.two.coeditors,.Professor.John.G..Lyon.and.Professor.Alfredo.Huete,.have.done.an..outstanding.job..Their.enormous.knowledge.of.hyperspectral.remote.sensing,.combined.with.the.vastness.and.depth.of. their.understanding.of.remote.sensing,. in.general,.and.hyperspectral.remote.sensing,. in.particular,.made.my.job.that.much.easier..I.have.learned.a.lot.from.them.and.continue.to.do.so..Both.of.them.edited.each.of.the.28.chapters.of.the.book.and.also.helped.structure.the.chapters.for.a.flawless.reading..I.am.indebted.to.their.insights,.guidance,.support,.motivation,.and.encouragement.throughout.the.project..Each.chapter.of.the.book.had.two.or.three.peer.reviewers..So,.for.all.the.28.chapters.there.were.about.70.peer.reviewers..I.am.grateful.to.these.anonymous.reviewers.who.contributed.their.valuable.time.and.insights.to.the.peer-review.process.
My.coeditors.and.I.are.grateful.to.Dr..Alexander.F..H..Goetz.for.writing.the.foreword.for.the.book..Dr..Goetz.is.one.of.the.pioneers.of.hyperspectral.remote.sensing.and.certainly.needs.no.introduction..He.started.his.career.working.on.spectroscopic.reflectance.and.emittance.studies.of.Moon.and.Mars..He.was.a.principal. investigator.of.Apollo-8.and.Apollo-12.multispectral.photography.studies..He.later. turned.his.attention.to.remote.sensing.of.the.Earth.working.in.collaboration.with.Dr..Gene.Shoemaker.to.map.geology.of.Coconino.County.(Arizona).using.Landsat-1.data..He.then.became.an.investigator.in.further.Landsat,.Skylab,.Shuttle,.and.EO-1.missions..At.NASA/JPL,.he.pioneered.field.spectral.measurements.and.initiated.the.develop-ment.of.hyperspectral.imaging..He.spent.21.years.on.the.faculty.of.the.University.of.Colorado,.Boulder,.and.retired.in.2006.as.an.emeritus.professor.of.geological.sciences.and.an.emeritus.director.of.the.Center.for.the.Study.of.Earth.from.Space..Since.then.he.has.been.chairman.and.chief.scientist.of.ASD.Inc.,.a.company.that.has.provided.more.than.850.research.laboratories.in.over.60.countries.with.field.spectrometers..His.foreword.is.a.must.read.for.anyone.studying.this.book.
I.am.blessed.to.have.had.the.support.and.encouragement.(professional.and.or.personal).of.my.U.S..Geological.Survey.(USGS).colleagues..In.particular,.I.would.like.to.mention.Mr..Edwin.Pfeifer,.Dr. Susan. Benjamin,. Dr.. Dennis. Dye,. Mr.. Miguel. Velasco,. Dr.. Chandra. Giri,. and. Dr. Thomas.Loveland..There.are.many.other.colleagues.who.made.my.job.at.USGS.that.much.easier.
My.wife.(Sharmila.Prasad).and.daughter.(Spandana.Thenkabail).are.the.two.great.pillars.of.my.life..I.am.always.indebted.to.their.patience,.support,.and.love.
Finally,.kindly.bear.with.me.for.sharing.a.personal.turmoil..The.year.2010.was.a.difficult.year.for.me.personally.and.for.my.family.in.particular..Just.when.I.started.working.on.the.book.project,.I. learned.that.I.had.colon.cancer..I.had.to.undergo.a.major.surgery.(relatively.easy.part).and.six.months.of.chemotherapy.(difficult.part)..It.is.quite.satisfying.that.on.my.last.day.of.chemotherapy,.wedelivered.the.book.by.FedEx.to.Taylor.&.Francis.Group!.Luckily,.we.(editors).never.had.to.post-pone.this.project..Iam.very.grateful.to.some.extraordinary.people.who.helped.me.through.these.dif-ficult.times:.Dr.Parvasthu.Ramanujam.(surgeon);.Dr..Paramjeet.K..Bangar.(oncologist);.three.great.nurses.(Irene,.Becky,.and.Maryam).at.Banner.Boswell.Hospital.(Sun.City,.AZ,.United.States);.the.courage,.love,.patience,.and.prayers.from.my.wife,.daughter,.and.several.family.members,.friends,.and.colleagues;.and.support.from.numerous.others.that.I.have.not.named.here..During.this.phase,.Ilearned.a.lot.about.cancer,.and.it.gave.me.an.enlightened.perspective.of.life..I.would.certainly.not.have.completed.editing. this.book.without.being.a.survivor..Yes,.my.prayers.were.answered..Ilearned.a.great.deal.about.lifegood.and.bad..I.pray.for.all.those.cancer.and.other.patients.with.dire.illnesses,.and.Ihope.that.we.will.soon.find.a.simple.cure.
xvAcknowledgments
Finally. I.would. like. to. thank.a.number.of.people.at.Taylor.&.Francis/CRC.Press..They. include.Irma.Shagla;.Arunkumar.Aranganathan,.project.manager,.SPi.Global,.and.his.team.of.editors;.and.Jennifer.Ahringer..Shagla.got.the.approval.for.the.book.project.and.played.a.pivotal.role.in.support-ing.it;.Ahringer.coordinated.book.materials;.and.Aranganathan.and.his.team.did.an.outstanding.job.in.editing.the.book..Their.highly.professional.efforts.are.deeply.appreciated.
Dr. Prasad S. Thenkabail, PhDEditor in Chief
Hyperspectral Remote Sensing of Vegetation
xvii
EditorsDr. Prasad S. Thenkabail.has.more.than.25.years.experience.working. as. a. well. recognized. international. expert. in. remote.sensing. and. geographic. information. systems. (RS\GIS). and. its.applications.to.agriculture,.natural.resource.management,.water.resources,. sustainable. development,. and. environmental. stud-ies..His.work.experience.spans.over.25.countries.spread.across.West. and. Central. Africa. (Republic. of. Benin,. Burkina. Faso,.Cameroon,. Central. African. Republic,. Cte. dIvoire,. Gambia,.Ghana,. Mali,. Nigeria,. Senegal,. and. Togo),. Southern. Africa.(Mozambique,. South. Africa),. South. Asia. (Bangladesh,. India,.Myanmar,. Nepal,. and. Sri. Lanka),. Southeast. Asia. (Cambodia),.
Middle.East.(Israel,.Syria),.East.Asia.(China),.Central.Asia.(Uzbekistan),.North.America.(United.States),.South.America.(Brazil),.and.Pacific.(Japan).
Dr..Thenkabail. has. a.wealth. of.work. experience. in. premier. global. institutes,. holding.key. lead.research. positions.. Currently,. he. is. a. research. geographer. at. the. U.S.. Geological. Survey. (USGS)..His.roles.include.being.a.lead.researcher.for.a.number.of.projects.(e.g.,.irrigated.cropland.water.pro-ductivity. in.California,.global.croplands.and. their.water.use,. imaging.spectroscopy.of.vegetation),.coordinator. (2010present). of. the. Committee. for. Earth. Observation. Systems. (CEOS). Agriculture.Societal.Beneficial.Area.(SBA),.and.a.science.advisor.(2010present).to.the.Land.Surface.Imaging.Constellation.for.CEOS,.GEO,.GEOSS..He.co-leads.an.IEEE.Water.for.the.World.project..He.is.also.an.adjunct.professor,.Department.of.Soil,.Water,.and.Environmental.Science.(SWES),.University.of.Arizona.(UoA).
Prior.to.USGS,.Dr..Thenkabail.worked.as.a.principal.researcher.in.the.Global.Research.Division.and.as.head.of.the.Remote.Sensing.and.Geographic.Information.Systems.(RS\GIS).at.the.International.Water.Management.Institute.(IWMI).headquartered.in.Sri.Lanka.with.a.network.of.offices.in.Asia.and.Africa..During.this.period.that.he.led.projects.such.as.the.global.irrigated.area.mapping.(GIAM);.Global.Map.of.Rainfed.Cropland.Areas.(GMRCA).(http://www.iwmigiam.org);.water.productivity.mapping.in.Central.Asia;.wetland.mapping.in.Africa;.drought.monitoring.system.for.Afghanistan,.Pakistan,.and.parts.of.India;.and.the.IWMI.data.storehouse.pathway.(http://www.iwmidsp.org)..All.of. these.projects. were. pioneering. efforts. using. advanced. remote. sensing. data,. methods,. and. approaches.conceptualized,.led,.and.managed.by.Dr..Thenkabail.
Dr..Thenkabail.did.pioneering.work.on.hyperspectral.remote.sensing.of.vegetation.and.agricul-tural.crops,.especially.when.he.worked.as.associate.research.scientist.(a.research.faculty.position).at.the.Yale.Center.for.Earth.Observation.(YCEO).at.the.Yale.University,.New.Haven,.Connecticut..In.this.position,.he.worked.in.NASA-funded.research.in.Africa.and.Asia..He.was.theprincipal.inves-tigator.for.the.NASA-funded.project.called.Characterization.of.Eco-regions.in.Africa.(CERA)..He.also.worked.on.hyperspectral.remote.sensing.and.carbon.stock.estimations.from.remote.sensing.in.African.rain.forests.and.savannas..This.work.resulted.in.Dr..Thenkabails.appointment.to.the.scientific.advisory.board.of.Rapideye,.a.private.German.Earth.Resources.Satellite.Company..He.played.a.pivotal.role.in.recommending.the.design.of.wave.bands.in.the.Rapideye.sensor.onboard.a. constellation. of. five satellites. [TACHYS. (Rapid),. MATI. (Eye),. CHOMA. (Earth),. CHOROS.(Space),. TROCHIA. (Orbit)]. launched. recently. by. Rapideye.. His. research. played. a. key. role. in.
xviii Editors
the. selection. of. red-edge. band.. Prior. to. this,. for. nearly. five. years,. he. led. the. remote. sensing.programs.at.the.International.Institute.of.Tropical.Agriculture.(IITA),.working.mostly.in.West.and.Central.African.countries.based.in.Nigeria.and.the.International.Center.for.Integrated.Mountain.Development. (ICIMOD). working. in. the. Hindu-Kush. Himalayan. countries. based. in. Katmandu,.Nepal..Duringthis.period,.he.led.the.remote.sensing.component.of.the.inland.valley.wetland.char-acterization.and.mapping.using.Landsat.TM.and.SPOT.HRV.data.for.the.West.and.Central.African.Nations.
Dr..Thenkabail. is. the.main.editor.of. the.book.entitled.Remote Sensing of Global Croplands for Food Security.(Publisher:.Taylor.&.Francis.Group,.2009)..He.coedited.a.special.issue.for.the.Journal of Remote Sensing.on. the.subject.of.global.croplands. that.has.21.excellent.papers.on.the. topic. of. global. cropland. and. their. water. use. (http://www.mdpi.com/journal/remotesensing/.
special_issues/croplands/)..The.USGS.and.NASA.selected.him.to.be.on.the.Landsat.Science.Team.for.a.period.of.5.years.starting.2006.(http://ldcm.usgs.gov/intro.php)..He.is.also.one.of.the.editors.of.Remote Sensing of Environment..In.June.2007,.Dr..Thenkabails.team.was.recognized.by.the.Environmental.System.Research.Institute.(ESRI).for.special.achievement.in.GIS.(SAG.award).for.their.tsunami-related.work.and.for.their.innovative.spatial.data.portals.(http://www.iwmidsp.org).and.science.applications.(http://www.iwmigiam.org)..In.2008,.he.and.his.coauthors.were.the.second.place.recipients.of.the.2008.John.I..Davidson.ASPRS.Presidents.Award.for.practical.papers.(for.their.paper.on.spectral.matching.techniques.used.in.mapping.global.irrigated.areas)..He.won.the.1994.Autometric.Award.of.the.American.Society.of.Photogrammetric.Engineering.and.Remote.Sensing.(ASPRS).for.superior.publication.in.remote.sensing..Dr..Thenkabails.publications.were.selected.as.one.of.the.best.five.papers.consecutively.for.three.years.(20042006).in.the.IWMIs.annual. research. meeting. (ARM).. His. team. was. also. awarded. the. best. team. at. IWMI. during.ARM.2006..
Early.in.his.career,.Dr..Thenkabail.worked.as.a.scientist.with.the.National.Remote.Sensing.Agency. (NRSA),. Department. of. Space,. and. Government. of. India.. He. began. his. professional.career.as.a. lecturer. in.hydrology,.water. resources,.hydraulics,.hydraulics. laboratory,.and.open.channel. flow. in. the. colleges. affiliated. to. Bangalore. and. Mysore. University. in. India.. He. has.more.than.80+.publications,.mostly.peer-reviewed.and.published.in.major.international.remote.sensingjournals.
Dr. John G. Lyons.research.has.involved.advanced.remote.sensing.and.GIS.applications. to. water. and. wetland. resources,. agriculture,. natural. resources,.and. engineering. applications.. He. is. the. author. of. books. on. wetland. land-scape.characterization,.wetland.and.environmental.applications.of.GIS,.and.accuracy.assessment.of.GIS.and.remote.sensing.technologies..Lyon.was.edu-cated.at.Reed.College.in.Portland,.OR,.and.the.University.of.Michigan,.Ann.Arbor,.and.has.previously.served.as.a.professor.of.civil.engineering.and.natu-ral.resources.at.Ohio.State.University.(19811999)..For.approximately.eight.years,.he.was.the.director.(SES).of.the.U.S..Environmental.Protection.Agency.Office. of. Research. and. Developments. (ORD). Environmental. Sciences.Division,.which.conducts. research.on. remote.sensing.and.GIS. technologies.
as.applied.to.environmental.issues,.including.landscape.characterization.and.ecology,.and.haz-ardous.wastes..Lyon.currently.serves.as.a.senior.scientist.(ST).in.the.EPA.Office.of.the.Science.Advisor. in.Washington,.District.of.Columbia,. and. is. co-lead. for.work.on. the.Group.on.Earth.Observations.and.the.Global.Earth.Observation.System.of.Systems,.and.research.on.geospatial.issues.in.the.agency.
xixEditors
Dr. Alfredo Huete. is. currently. a. professor. in. the. Faculty. of.Science,. Plant. Functional. Biology. and. Climate. Change. Cluster,.at. the. University. of. Technology. Sydney,. Australia.. Prior. to. this.appointment,.he.was.professor.of.soils.and.remote.sensing.in.the.Department. of. Soil,. Water,. and. Environmental. Science. at. the.University.of.Arizona,.Tucson,.Arizona.
Dr..Huete. received.his.professional.degrees. at. the.University.of.California.at.Berkeley.(MSc).and.University.of.Arizona.(PhD)..He. serves. on. the. editorial. review. boards. of. Remote Sensing of Environment,.Revisa.de.Teledeteccin.de.la.Asociacin.Espaola.de.Teledeteccin.(AET),.and.the.online.journal.Remote Sensing..
He.is.a.member.of.NASA-EOS.MODIS.science.team.and.has.led.the.development.and.implementa-tion.of.the.MODIS.vegetation.index.products..He.is.also.part.of.the.Japanese.JAXA.GCOM-SGLI.science.team.and.European.PROBA-V.IUC.user.expert.group..He.was.also.a.member.of.the.EO-1.Hyperion.science.team.and.NASA.advisory.team.to.evaluate.NPOESS.and.NPP.vegetation.index.products.for.land.monitoring,.environmental.data.records.(EDRs).and.long-term.climate.data.records.(CDRs)..Professor.Huete.is.also.an.active.member.of.the.International.Society.of.Photogrammetry.and.Remote.Sensing.(ISPRS).Commission.on.Remote.Sensing.Applications.and.Policies,.where.he.currently.serves.as.chair.of.Working.Group.VIII.on.Land.
Dr..Huetes.research.interests.focus.on.understanding.large-scale.soilvegetationclimate.inter-actions,. processes,. and. changes. with. remotely. sensed. measurements. from. satellites.. He. is. also.involved.with.field-based.and.tower.optical. instrumentation.in.support.of.remote.sensing.studies.coupling.satellite.observations.with.eddy.covariance.tower.flux.measurements..His.research.areas.of.interest.also.include.phenology.measures.and.shifts.in.seasonality.in.response.to.climate.forc-ings,. land.use.activities,. and. their. coupling. to.carbon.and.water.models..He.has.done.extensive.research.in.the.phenology.of.tropical.rain.forests.and.savannas.in.the.Amazon.and.Southeast.Asia.and.has.over.100.research.publications.in.peer-reviewed.journals,.a.book,.and.more.than.20.chapter.contributions.
xxi
List of Acronyms and Abbreviations. stretching.vibration. bending.vibration1DL_DGVI. first-order.derivative.green.vegetation.index.derived.using.local.baseline1DZ_DGVI. first-order.derivative.green.vegetation.index.derived.using.zero.baseline3S. HEAD5S. simulation.of.satellite.signal.in.the.solar.spectrum6S. second.simulation.of.the.satellite.signal.in.the.solar.spectrumACI. anthocyanin.content.indexACORN. atmospheric.CORrection.now.programADEOS. advanced.earth.observing.satelliteAERONET. aerosol.robotic.networkAET. actual.evapotranspirationAISA. airborne.imaging.spectroradiometer.for.applicationALI. advanced.land.imagerAMEE. automatic.morphological.endmember.extractionANC. abundance.non-negativity.constraintANN. artificial.neural.networksANOVA. one-way.analysis.of.varianceAOT. aerosol.optical.thicknessAOTF. acousto-optic.tunable.filterAPAR. absorbed.photosynthetically.active.radiationAR.HTBVI. atmospherically.resistant.hyperspectral.two-band.vegetation.indicesARI. anthocyanin.reflectance.indexARVI. atmospherically.resistant.vegetation.indexASC.. abundance.sum-to-one.constraintASD. analytical.spectral.devicesASI. Agenzia.Spaziale.ItalianaASTER. advanced.space-borne.thermal.emission.and.reflection.radiometerATCOR. ATmospheric.CORrection.programATREM. ATmospheric.REMoval.programATSAVI. adjusted.transformed.soil-adjusted.vegetation.indexAVHRR/NOAA-17. .advanced. very. high. resolution. radiometer/national. oceanic. and. atmo-
spheric.administration-17AVHRR. advanced.very.high.resolution.radiometerAVIRIS. airborne.visible/infrared.imaging.spectrometerBB-PAC. biophysical.and.biochemical.properties.of.agricultural.cropsBBVI. broadband.vegetation.index.modelsBD-RDP. beamlet-decorated.recursive.dyadic.partitioningBDRF. bidirectional.reflectance.functionBE. blue.edgeBmND. derivative-based.modified.normalized.differenceBmSR. derivative-based.modified.simple.ratioBRDF. bidirectional.reflectance.distribution.functionBRDI. bromus.distachyonCAI. cellulose.absorption.index
xxii List of Acronyms and Abbreviations
CAI. cloud.aerosol.imagerCAO. Carnegie.airborne.observatoryCAPY. carduus.pychnocephalusCARI. chlorophyll.absorption.reflectance.indexCART. classification.and.regression.treeCASI. compact.airborne.spectrographic.imagerCBERS-2. ChinaBrazil.earth.resources.satelliteCCA. Convex.cone.analysisCC. chlorophyll.contentCCCI. canopy.chlorophyll.content.indexCCD/CBERS-2. charge-coupled.device/ChinaBrazil.earth.resources.satellite-2CCD. charge-coupled.deviceCCSM. cross.correlogram.spectral.matchingCDA. canonical.discriminant.analysisCd. cadmiumChlgreen. chlorophyll.index.using.green.reflectanceChlred-edge. chlorophyll.index.using.red-edge.reflectanceCHRIS/PROBA. .compact. high-resolution. imaging. spectrometer/project. for. on. board.
autonomyCHRIS. compact.high-resolution.imaging.spectrometerCIgreen.and.CIred.edge. green-.and.red-edge.chlorophyll.indices,.respectivelyCIR. color.infraredCIred.edge. chlorophyll.red-edge.indexCMF. color.matching.functionsCMG. climate.modeling.gridCNES. Centre.National.dEtudes.SpatialesCNPq. .Conselho.nacional.de.desenvolvimento.cientfico.e.tecnolgicoCP. crude.protein.(%)CRDR. continuum.removal.derivative.reflectanceCRI1.and.2. carotenoid.reflectance.indexCRI. carotenoid.reflectance.indexCu. copperD. absorption.band.depthDAIS. digital.airborne.imaging.spectrometerDD. double.differenceDEM. digital.elevation.modelDLR.. German.aerospace.agencyDN. digital.numberDOAS. differential.optical.absorption.spectroscopyDoD. Department.of.DefenseDT. decision.treeDVI. difference.vegetation.indexDWAB. dry.weight.of.aboveground.biomassDWSI. disease.water.stress.indexDWT. discrete.wavelet.transformE. radianceECHO. extraction.and.classification.of.homogenous.objects`ED. Euclidean.distanceEGU. European.Geoscience.UnionEMS. electromagnetic.spectrumEnMAP. environmental.mapping.and.analysis.program
xxiiiList of Acronyms and Abbreviations
ENVI. environment.for.visualizing.imagesENVISAT. environmental.satelliteEO-1. earth.observing-1EO-1. earth.observing-1.satelliteEOS. earth.observing.systemEPPD. effective.photon.penetration.depthERDAS. earth.resource.data.analysis.systemERS. earth.remote.sensingESA. European.Space.AgencyET. evapotranspirationETM+/Landsat-7. enhanced.thematic.mapper.plus/Landsat-7ETM+. Landsat-7.enhanced.thematic.mapper.plusEUFAR. EUropean.Facility.for.Airborne.ResearchEVI2. two-band.enhanced.vegetation.indexEVI. enhanced.vegetation.indexFAO. Food.and.Agriculture.AdministrationFAPAR. fraction.of.absorbed.photosynthetically.active.radiationFAPESP. Fundao.de.amparo.a.pesquisa.do.estado.de.so.pauloFDR. first.derivative.reflectanceFEDM. frequent.domain.electromagneticFLAASH. ENVIs.fast.line-of-sight.atmospheric.analysis.of.spectral.hypercubesFLAASH. fast.line-of-sight.atmospheric.analysis.of.spectral.hypercubesFNIR. far-near.infrared.(11001300.nm)FORMOSAT. .Taiwanese.satellite.operated.by.Taiwanese.National.Space.Organization.
(NSPO)..Data.marketed.by.SPOTFOV. field.of.viewFPAR. fraction.of.photosynthetically.active.radiationFPGAs. field.programmable.gate.arraysFR. full.resolutionFS. Fort.Sherman,.PanamaFSI. full.spectral.imagingFTHSI. Fourier.transform.hyperspectral.imagerFTIR. Fourier.transform.infraredFTS. Fourier.transform.spectrometersGAC. global.area.coverageGA. genetic.algorithmGCOM-C. global.change.observation.mission-climateGEOEYE-1.and.2. providing.data.in.0.25.to.1.65.m.resolutionGERIS. .geophysical.and.environmental.research.imaging.spectrometerGI. green.indexGIS. geographic.information.systemGLI. global.imagerGO-RT. geometrical.optical.and.radiative.transferGO. geometrical.opticalGOME. Global.Ozone.Monitoring.ExperimentGOSAT. greenhouse.gases.observing.satelliteGP. green.peakGPP. gross.primary.productionGPR. ground-penetrating.radarGPS. global.positioning.systemGPS. ground.positioning.system
xxiv List of Acronyms and Abbreviations
GPUs.. graphics.processing.unitsGV. green.vegetationHATCH. high-accuracy.ATmosphere.correction.for.hyperspectral.dataHDGVI. hyperspectral.derivative.greenness.vegetation.indicesHD. hard.diskHHVI. hyperspectral.hybrid.vegetation.indicesHICO. hyperspectral.imager.for.the.coastal.oceanHIS. hyperspectral.ImagersHMBM. hyperspectral.multiple-band.modelsHPLC. high-performance.liquid.chromatographyHRG/SPOT-5. .high.geometric.resolution.instrument/systeme.pour.lobservation.de.la.
terre-5HRS. hyperspectral.remote.sensingHS. hyperspectralHSR. hyper.spectral.remote.sensingHSS. hyperspectral.sensorHTBVI. hyperspectral.two-band.vegetation.indexHTV. H-2.Transfer.VehicleHVI. hyperspectral.vegetation.indexHVIST. hyperspectral.vegetation.indices.of.SWIR.and.TIR.bandsHYDICE. hyperspectral.digital.imagery.collection.experimentHyMap. airborne.hyperspectral.scannersHyMAP. hyperpectral.MAPpping.sensorHYMAP. hyperspectral.mapperHYPER-I-NET. Hyperspectral.imaging.networkHyperion. first.spaceborne.hyperspectral.sensor.onboard.earth.observing-1(EO-1)HypspIRI. hyperspectral.imaging.spectrometer.and.infrared.imagerHySI. hyperspectral.imagerHyspIRI. hyperspectral.infrared.imagerICA. independent.component.analysisICAMM. independent.component.analysis-based.mixed.modelICARE. International.Conference.on.Airborne.Research.for.the.EnvironmentIEA. iterative.error.analysisIFOV. instantaneous.field.of.viewIG. inverted.GaussianIKONOS. Greek.word.for.imageIKONOS. high-resolution.satellite.operated.by.GeoEyeIMZ. intensive.measurement.zonesINS. inertial.navigation.systemIPS. invasive.plant.speciesIR. infraredIRS-1C/D-LISS. Indian.remote.sensing.satellite/linear.imaging.self-scannerIRS-P6-AWiFS. Indian.remote.sensing.satellite/advanced.wide.field.sensorIS. imaging.spectroscopyISS. International.Space.StationITC. individual.tree.crownJAXA. Japan.Aerospace.Exploration.AgencyJD. Julian.dayJPSS. joint.polar.satellite.systemK-T. Kaufman-Tanr.aerosol.retrievalKFD. Kernel.Fisher.Discriminant
xxvList of Acronyms and Abbreviations
KOMFOSAT. Korean.multipurpose.satellite..Data.marketed.by.SPOT.imageL. irradianceLAD. leaf.angle.distributionLAI. leaf.area.indexLAI. leaf.area.index.(m2/m2)Landsat-1,.2,.3.MSS. multispectral.scannerLandsat-4,.5.TM. thematic.mapperLandsat-7.ETM+. enhanced.thematic.mapper.plusLANDSAT-TM. land.satellite.thematic.mapper.sensorLandsat. land.remote.sensing.satellite.programLANDSAT. MSS-land.satellite.multi.spectral.sensorLCI. leaf.chlorophyll.indexLDA. linear.discriminant.analysisLEO. low.earth.orbitLFM. live.fuel.moistureLICOR. An.instrument-used.to.measure.leaf.are.indexLiDAR. light.detection.and.rangingLI. Lepidium.IndexLNA. leaf.nitrogen.accumulationLNC. leaf.nitrogen.concentrationLOWTRAN. LOW.resolution.model.for.prediction.atmosphere.TRANsitionLSBS. La.selva.biological.station,.Costa.RicaLUE. light.use.efficiencyLUT. lookup.tableLWVI-2. leaf.water.vegetation.index-2MACI. modified.anthocyanin.content.indexMAE. mean.absolute.errorMARI. modified.anthocyanin.reflectance.indexMaxAE. maximum.absolute.errorMCARI. modified.chlorophyll.absorption.ratio.indexMDA. multiple.discriminant.analysisMERIS. medium.resolution.imaging.spectrometerMESMA. multiple-endmember.spectral.mixture.analysisMF. Matched.FilterMIA. mutual.information.analysisMIC. mutual.information.criterionMightySat. Mighty.SatelliteMLC. maximum.likelihood.classificationMLP. multilayer.perceptronMLR. multiple.linear.regressionMLR. multivariate.linear.regressionMMI. minimal.mutual.informationmND680. modified.normalized.differencemND705. modified.normalized.differencemND. modified.normalized.differenceMNDVI. modified.normalized.differential.vegetation.indexMNF. minimum.noise.fractionMODIS. moderate.imaging.spectral.radiometerMODIS. moderate.resolution.imaging.spectrometerMODIS. moderate.resolution.imaging.spectroradiometerMODTRAN. MODerate.resolution.atmospheric.TRANsmittance.and.radiance
xxvi List of Acronyms and Abbreviations
MRF. Markov.random.fieldMSAVI2. modified.second.soil-adjusted.vegetation.indexMSAVI. improved.soil-adjusted.vegetation.indexMSI. moisture.stress.indexMSMISat. multi-sensor.micro-satellite.imager.satelliteMS. multispectralmSR705. modified.simple.ratiomSR. modified.simple.ratioMTCI. MERIS.terrestrial.chlorophyll.indexMTMF. mixture.tuned.matched.filteringMWIR. medium-wave.infraredN. nitrogenNASA. National.Aeronautics.and.Space.AdministrationNDII. normalized.difference.infrared.indexNDI. normalized.difference.indexNDLI. normalized.difference.lignin.indexNDNI. normalized.difference.nitrogen.indexND. normalized.differenceNDRE. normalized.difference.red.edgeNDVI. normalized.difference.vegetation.indexNDWI. normalized.difference.water.indexNEE. net.ecosystem.carbon.dioxide.exchangeNE. noise.equivalentNIR. near-infrared.reflectanceNIRS. near-infrared.spectroscopynm. nanometerNMP. NASAs.new.millennium.programNN. neural.networkNOAA. National.Oceanic.and.Atmospheric.AdministrationNPCI. normalized.pigment.chlorophyll.ratio.indexNPOESS. National.Polar-Orbiting.Operational.Environmental.Satellite.SystemNPP. NPOESS.preparatory.projectNPVAI. non-photosynthetic.vegetation.area.indexNPV. non-photosynthetic.vegetationNRL. Naval.Research.LaboratoryNSA. normalized.spectral.areaNSMI. normalized.soil.moisture.indexOLS. ordinary.least.squaresOMI. ozone.monitoring.instrumentOM. organic.matterORASIS.. optical.real-time.adaptive.spectral.identification.systemOSAVI. optimized.soil-adjusted.vegetation.indicesOSP. orthogonal.subspace.projectionPAR. photosynthetically.active.radiationPBI. plant.biochemical.indexPb. leadPCA. principal.component.analysisPC. principal.componentPCR. principal.components.regressionPET. potential.evapotranspirationPHI. azimuth.angle
xxviiList of Acronyms and Abbreviations
PI2. pigment.index.2PIMA. field.portable.infrared.spectrometerPLNTHT. plant.height.(mm)PLS. partial.least.squaresPLSR. partial.least.square.regressionPNM. Parque.Natural.Metropolitano,.PanamaPOS. penetrating.optical.sensorPPI.. pixel.purity.indexPP. projection.pursuitPPR. plant.pigment.ratioPRI. photochemical/physiological.reflectance.indexPRI. photochemical.reflectance.indexPRI. photosynthetic.reflectance.indexPRI. physiological.reflectance.indexPRISMA. hyperspectral.precursor.and.application.missionPRISMA. PRecursore.IperSpettrale.della.Missione.ApplicativaPROBA. project.for.on-board.autonomyPSF. point.spread.functionPSND. pigment-specific.normalized.differencePSRI. plant.senescence.reflectance.indexPSSR. pigment-specific.spectral.ratioPV. photosynthetic.vegetationQUICKBIRD. satellite.from.DigitalGlobe,.a.private.company.in.the.United.StatesR. reflectanceR1. reproductive.stage.1.(beginning.bloom)R2. coefficient.of.determinationR3. reproductive.stage.3.(beginning.pod)RAPID.EYE..A/E. satellite.constellation.from.Rapideye,.a.German.companyRARS. ratio.analysis.of.reflectance.spectraRBF:. radial.basis.functionRDP. recursive.dyadic.partitioningRE. red.edgeRENDVI. red-edge.normalized.difference.vegetation.indexREP. red-edge.positionRESOURSESAT. satellite.launched.by.IndiaRGB. red.green.blueRGRI. red/green.ratio.indexRGR. red:green.ratioRMSE. root.mean.square.errorRMS. root.mean.squareROI. region.of.interestROSIS.. reflective.optics.spectrographic.imaging.systemRPD. ratio.of.prediction.to.deviationRRMSE. relative.root.mean.squareRT. radiative.transferRTM. radiative.transfer.modelsRVI. ratio.vegetation.indexRVIhyp. hyperspectral.ratio.VIRVSI. red-edge.vegetation.stress.indexRWC. relative.water.contentSA.HTBVI. soil-adjusted.hyperspectral.two-band.vegetation.indices
xxviii List of Acronyms and Abbreviations
SAM. spectral.angle.mapperSAVI2. second.soil-adjusted.vegetation.indexSAVI. soil-adjusted.vegetating.indexSBFS. sequential.backward.floating.selectionSBS. sequential.backward.selectionSBUV. solar.backscatter.ultravioletSCIAMACHY. scanning.imaging.absorption.spectrometer.for.atmospheric.cartographySCM. spectral.correlation.measureSCR. spatially.coherent.regionsSeaWiFS. sea-viewing.wide.field-of-view.sensorSFFS. sequential.forward.floating.selectionSFS. sequential.forward.selectionSGI. sum.green.indexSGLI. second.generation.global.imagerSGR. summed.green.reflectanceSIPI. structurally.insensitive.pigment.indexSLA. specific.leaf.areaSLR. stepwise.linear.regressionSMA. spectral.mixture.analysisSMGM. soil.moisture.Gaussian.modelSNR. signal-to-noise.ratioSPECIM. SPECtral.ImagingSPOT. satellites.pour.lobservation.de.la.terre.or.earth-observing.satellitesSPP.. spatial.preprocessingSPSS. statistical.product.and.service.solutionsSR. simple.ratioSSEE.. spatial.spectral.endmember.extractionSVD.. singular.value.decompositionSVM. support.vector.machineSWIR. shortwave.infraredSWIR. shortwave.infrared.(13002500.nm)SZA. solar.zenith.angleTAU. Tel.Aviv.UniversityTCARI/OSVAI. .transformed.chlorophyll.absorption.in.reflectance.index/optimized.soil-
adjusted.vegetation.indexTEM. total.entropy.measureTES. tropospheric.emission.spectrometerTF. tropical.forestTIR. thermal.infraredTML. total.metal.levelTOA. top.of.atmosphereTOC. top.of.canopyTOMS. total.ozone.mapping.spectrometerTSAVI. transformed.soil-adjusted.vegetation.indexTSVMs.. transductive.SVMsTVI. triangular.vegetation.indexU.S.. United.StatesUAV. unmanned.aerial.vehicleUHF. ultrahigh.frequencyUMV. unmanned.vehicleUNFCCC. United.Nations.Framework.Convention.on.Climate.Change
xxixList of Acronyms and Abbreviations
USAD. United.States.Department.of.AgricultureUSGS.. United.States.Geological.SurveyUV. ultravioletUV. ultra.waveVARI. vegetation.atmospherically.resistant.indexVARI. visible.atmospherically.resistant.indexVCA.. vertex.component.analysisVD.. virtual.dimensionalityVF. vegetation.fractionVI. (spectral).vegetation.indexVIGreen. vegetation.index.greenVIg. visible.green.indexVIIRS. visible.infrared.imager.radiometer.suiteVIP. variable.importance.for.projectionVIS. visibleVMC. volumetric.moisture.contentVNIR. visible.near-infrared.sensorVNIR. visual.and.near.infraredVOG-1. Vogelmann.red-edge.index-1VPD. vapor.pressure.deficitVSWIR. visible.and.short.wave.infraredVZA. view.zenith.angleW. waterWAA. water.absorption.areaWAD. water.absorption.depthWBI. water.band.indexWBM. wet.biomass.(kg/m2)WDRVI. wide.dynamic.range.vegetation.indexWFIS. wide.field-of-view.imaging.spectrometerWI. water.indexWORLDVIEW. DigitalGlobes.earth.imaging.satelliteWSC. World.Soil.CongressWT. wavelet.transformYE. yellow.edgeYI. yellowness.indexZn. zinc
xxxi
Contributors
Victor AlchanatisInstitute.of.Agricultural.EngineeringAgricultural.Research.OrganizationBet.Dagan,.Israel
Sreekala G. BajwaDivision.of.AgricultureUniversity.of.ArkansasFayetteville,.Arkansas
E. Ben-DorDepartment.of.GeographyTel-Aviv.UniversityTel-Aviv,.Israel
Deepak BhatnagarU.S..Department.of.AgricultureAgricultural.Research.ServiceSouthern.Regional.Research.CenterNew.Orleans,.Louisiana
M. BoschettiNational.Research.CouncilInstitute.for.Electromagnetic.Sensing.
oftheEnvironmentMilan,.Italy
Fbio Marcelo BreunigInstituto.Nacional.de.Pesquisas.EspaciaisSo.Jos.dos.Campos,.So.Paulo,.Brazil
P.A. BrivioNational.Research.CouncilInstitute.for.Electromagnetic.Sensing.
oftheEnvironmentMilan,.Italy
Robert L. BrownU.S..Department.of.AgricultureAgricultural.Research.ServiceSouthern.Regional.Research.CenterNew.Orleans,.Louisiana
Yen-Ben ChengEarth.Resources.Technology,.Inc.Laurel,.Maryland
Panigada CinziaRemote.Sensing.of.Environmental.Dynamics.
LaboratoryDipartimento.di.Scienze.dellAmbiente.e.del.
TerritorioUniversit.Milano-BicoccaMilano,.Italy
Matthew L. ClarkCenter.for.Interdisciplinary.Geospatial.
AnalysisDepartment.of.Geography.and.Global.StudiesSonoma.State.UniversityRohnert.Park,.California
Thomas E. ClevelandU.S..Department.of.AgricultureAgricultural.Research.ServiceSouthern.Regional.Research.CenterNew.Orleans,.Louisiana
Yafit CohenInstitute.of.Agricultural.EngineeringAgricultural.Research.OrganizationBet.Dagan,.Israel
Jos Carlos Neves EpiphanioInstituto.Nacional.de.Pesquisas.EspaciaisSo.Jos.dos.Campos,.So.Paulo,.Brazil
F. FavaDesertification.Research.GroupUniversit.degli.Studi.di.SassariSassari,.Italy
Antnio Roberto FormaggioInstituto.Nacional.de.Pesquisas.EspaciaisSo.Jos.dos.Campos,.So.Paulo,.Brazil
Lisa R. GaddisAstrogeology.Science.CenterU.S..Geological.SurveyFlagstaff,.Arizona
Lnio Soares GalvoInstituto.Nacional.de.Pesquisas.EspaciaisSo.Jos.dos.Campos,.So.Paulo,.Brazil
xxxii Contributors
Chengcheng GangCollege.of.Life.ScienceNanjing.UniversityNanjing,.Peoples.Republic.of.China
Paul E. GeisslerAstrogeology.Science.CenterU.S..Geological.SurveyFlagstaff,.Arizona
Anatoly A. GitelsonUniversity.of.Nebraska-LincolnLincoln,.Nebraska
Edward P. GlennDepartment.of.Soil,.Water,.and.Environmental.
ScienceEnvironmental.Research.LaboratoryThe.University.of.ArizonaTucson,.Arizona
Justin J. HagertyAstrogeology.Science.CenterU.S..Geological.SurveyFlagstaff,.Arizona
K. Fred HuemmrichUniversity.of.Maryland,.Baltimore.CountyCollege.Park,.Maryland
and
Joint.Center.for.Earth.Systems.TechnologyBaltimore,.Maryland
Alfredo HueteSchool.of.Environmental.SciencesUniversity.of.Technology.SydneySydney,.New.South.Wales,.Australia
and
Department.of.Soil,.Water,.and.Environmental.Sciences
The.University.of.ArizonaTucson,.Arizona
Yoshio InoueAgro-Ecosystem.Informatics.ResearchNational.Institute.for.Agro-Environmental.
SciencesTsukuba,.Ibaraki,.Japan
Jeffery R. JohnsonAstrogeology.Science.CenterU.S..Geological.SurveyFlagstaff,.Arizona
Subodh S. KulkarniDivision.of.AgricultureUniversity.of.ArkansasFayetteville,.Arkansas
Willem J.D. van LeeuwenSchool.of.Geography.and.DevelopmentandSchool.of.Natural.Resources.
andtheEnvironmentOffice.of.Arid.Lands.StudiesArizona.Remote.Sensing.CenterThe.University.of.ArizonaTucson,.Arizona
Cherry LiCollege.of.Life.ScienceNanjing.UniversityNanjing,.Peoples.Republic.of.China
Jianlong LiCollege.of.Life.ScienceNanjing.UniversityNanjing,.Peoples.Republic.of.China
Busetto LorenzoRemote.Sensing.of.Environmental.Dynamics.
LaboratoryDipartimento.di.Scienze.dellAmbiente.e.del.
TerritorioUniversit.Milano-BicoccaMilano,.Italy
John G. LyonLas.Vegas.LaboratoryUnited.States.Environmental.Protection.
AgencyLas.Vegas,.Nevada
Hank A. MargolisFacult.de.ForesterieCentre.dtudes.de.la.Fortde.Gographie.et.de.GomatiqueUniversit.LavalLaval,.Quebec,.Canada
xxxiiiContributors
Gabriel MartnHyperspectral.Computing.LaboratoryDepartment.of.Technology.of.Computers.
andCommunicationsEscuela.Politcnica.de.CceresUniversity.of.ExtremaduraCceres,.Spain
Meroni MicheleRemote.Sensing.of.Environmental.Dynamics.
LaboratoryDipartimento.di.Scienze.dellAmbiente.e.del.
TerritorioUniversit.Milano-BicoccaMilano,.Italy
Rossini MicolRemote.Sensing.of.Environmental.Dynamics.
LaboratoryDipartimento.di.Scienze.dellAmbiente.e.del.
TerritorioUniversit.Milano-BicoccaMilano,.Italy
Elizabeth M. MiddletonGoddard.Space.Flight.CenterNational.Aeronautics.and.Space.
AdministrationGreenbelt,.Maryland
Tomoaki MiuraDepartment.of.Natural.Resources.
andEnvironmental.ManagementUniversity.of.Hawaii.at.ManoaHonolulu,.Hawaii
Pamela Lynn NaglerU.S..Geological.SurveySouthwest.Biological.Science.CenterSonoran.Desert.Research.StationTucson,.Arizona
Izaya NumataSouth.Dakota.State.UniversityBrookings,.South.Dakota
Aaryn Dyami OlssonLaboratory.of.Landscape.Ecology.
andConservation.BiologyCollege.of.Engineering,.Forestry.&.Natural.
SciencesNorthern.Arizona.UniversityFlagstaff,.Arizona
Fred OrtenbergTechnionIsrael.Institute.of.TechnologyHaifa,.Israel
Ryan L. PerroyDepartment.of.Geography.and.Earth.ScienceUniversity.of.Wisconsin-La.CrosseLa.Crosse,.Wisconsin
Antonio PlazaHyperspectral.Computing.LaboratoryDepartment.of.Technology.of.Computers.
andCommunicationsEscuela.Politcnica.de.CceresUniversity.of.ExtremaduraCceres,.Spain
Javier PlazaHyperspectral.Computing.LaboratoryDepartment.of.Technology.of.Computers.
andCommunicationsEscuela.Politcnica.de.CceresUniversity.of.ExtremaduraCceres,.Spain
Ruiliang PuDepartment.of.GeographyUniversity.of.South.FloridaTampa,.Florida
Jiaguo QiDepartment.of.GeographyandCenter.for.Global.Change.&.Earth.
ObservationsMichigan.State.UniversityEast.Lansing,.Michigan
Elijah Ramsey IIIU.S..Geological.SurveyNational.Wetland.Research.CenterLafayette,.Louisiana
Amina RangoonwalaFive.Rivers.Services,.LLCU.S..Geological.SurveyNational.Wetland.Research.CenterLafayette,.Louisiana
xxxiv Contributors
Colombo RobertoRemote.Sensing.of.Environmental.Dynamics.
LaboratoryDipartimento.di.Scienze.dellAmbiente.e.del.
TerritorioUniversit.Milano-BicoccaMilano,.Italy
Dar A. RobertsDepartment.of.GeographyUniversity.of.CaliforniaSanta.Barbara,.California
Keely L. RothDepartment.of.GeographyUniversity.of.CaliforniaSanta.Barbara,.California
Sergio SnchezHyperspectral.Computing.LaboratoryDepartment.of.Technology.of.Computers.
andCommunicationsEscuela.Politcnica.de.CceresUniversity.of.ExtremaduraCceres,.Spain
E. Terrence SloneckerEastern.Geographic.Science.CenterU.S..Geological.SurveyReston,.Virginia
Laurence A. SoderblomAstrogeology.Science.CenterU.S..Geological.SurveyFlagstaff,.Arizona
B.B. Maruthi SridharDepartment.of.GeologyBowling.Green.State.UniversityBowling.Green,.Ohio
Daniela StroppianaNational.Research.CouncilInstitute.for.Electromagnetic.Sensing.
oftheEnvironmentMilan,.Italy
Lie TangDepartment.of.Agricultural.and.Biosystems.
EngineeringIowa.State.UniversityAmes,.Iowa
Prasad S. ThenkabailWestern.Geographic.Science.CenterU.S..Geological.SurveyFlagstaff,.Arizona
Valerie ThomasDepartment.of.Forest.Resources.
andEnvironmental.ConservationVirginia.TechBlacksburg,.Virginia
Lei TianDepartment.of.Biological.and.Agricultural.
EngineeringUniversity.of.Illinois.at.Urbana.ChampaignChampaign,.Illinois
Timothy N. TitusAstrogeology.Science.CenterU.S..Geological.SurveyFlagstaff,.Arizona
R. Greg VaughanAstrogeology.Science.CenterU.S..Geological.SurveyFlagstaff,.Arizona
Wei WangJiangsu.Key.Laboratory.for.Information.
AgricultureNational.Engineering.and.Technology.Center.
for.Information.AgricultureCollege.of.AgricultureNanjing.Agricultural.UniversityNanjing,.Jiangsu,.Peoples.Republic.of.China
Narumon WiangwangDepartment.of.FisheriesInformation.Technology.CenterRoyal.Thai.GovernmentBangkok,.Thailand
xxxvContributors
Haibo YaoStennis.Space.CenterGeosystems.Research.InstituteMississippi.State.UniversityStarkville,.Mississippi
Xia YaoJiangsu.Key.Laboratory.for.Information.
AgricultureNational.Engineering.and.Technology.Center.
for.Information.AgricultureCollege.of.AgricultureNanjing.Agricultural.UniversityNanjing,.Jiangsu,.Peoples.Republic.of.China
Hiroki YoshiokaDepartment.of.Information.Science.
andTechnologyAichi.Prefectural.UniversityAichi,.Japan
Yongqin ZhangDivision.of.Biological.and.Physical.SciencesDelta.State.UniversityCleveland,.Mississippi
Dehua ZhaoCollege.of.Life.ScienceNanjing.UniversityNanjing,.Peoples.Republic.of.China
Yan ZhuJiangsu.Key.Laboratory.for.Information.
AgricultureNational.Engineering.and.Technology.Center.
for.Information.AgricultureCollege.of.AgricultureNanjing.Agricultural.UniversityNanjing,.Jiangsu,.Peoples.Republic.of.China
Part I
Introduction and Overview
31 Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Croplands
Prasad S. Thenkabail, John G. Lyon, and Alfredo Huete
CONTENTS
1.1 IntroductionandRationale.......................................................................................................41.2 HyperspectralRemoteSensingofVegetationandAgriculturalCrops.................................. 121.3 HyperspectralDataCompositionforStudyofVegetationandAgriculturalCrops............... 151.4 MethodsandApproachesofHyperspectralDataAnalysisforVegetation
andAgriculturalCrops........................................................................................................... 171.4.1 Lambda(1)byLambda(2)Plots............................................................................. 171.4.2 PrincipalComponentAnalysis................................................................................... 181.4.3 OtherHyperspectralDataMiningAlgorithms.......................................................... 18
1.5 OptimalHyperspectralNarrowbands:HyperspectralVegetationIndicestoStudyVegetationandCropBiophysicalandBiochemicalProperties.............................................. 191.5.1 HyperspectralTwoBandVegetationIndex................................................................201.5.2 HyperspectralMultiple-BandModels........................................................................ 211.5.3 HyperspectralDerivativeGreennessVegetationIndices........................................... 211.5.4 HyperspectralHybridVegetationIndices..................................................................22
1.5.4.1 Soil-AdjustedHyperspectralTwoBandVegetationIndices........................221.5.4.2 AtmosphericallyResistantHyperspectralTwoBandVegetation
Indices...................................................................................................231.5.4.3 HyperspectralVegetationIndicesofSWIRandTIRBands.......................23
1.6 OtherMethodsofHyperspectralDataAnalysis....................................................................231.7 BroadbandVegetationIndexModels......................................................................................241.8 SeparatingVegetationClassesandAgriculturalCropsUsingHyperspectral
NarrowbandData....................................................................................................................241.8.1 ClassSeparabilityUsingUniqueHyperspectralNarrowbands..................................241.8.2 ClassSeparabilityUsingStatisticalMethods.............................................................251.8.3 AccuracyAssessmentsofVegetationandCropClassificationUsing
HyperspectralNarrowbands.......................................................................................261.9 OptimalHyperspectralNarrowbandsinStudyofVegetationandAgriculturalCrops.........271.10 Conclusions.............................................................................................................................30Acknowledgments............................................................................................................................ 31References........................................................................................................................................ 31
4 Hyperspectral Remote Sensing of Vegetation
1.1 INTRODUCTIONANDRATIONALE
Recentadvances inhyperspectral remotesensing(or imagingspectroscopy)demonstrateagreatutilityforavarietyoflandmonitoringapplications.Itisnowpossibletobediagnosticinsensingspeciesandplantcommunitiesusingremotelysenseddataandtodosoinadirectandinformedmannerusingmoderntoolsandanalyses.Hyperspectraldataanalysesaresuperiortotraditionalbroadband analyses in spectral information. Many investigations explore and document remotesensingofvegetationandagriculturalcroplands.Someexamplesinclude(a)detectingplantstress[1],(b)measuringchlorophyllcontentofplants[2],(c)identifyingsmalldifferencesinpercentofgreenvegetationcover[3],(d)extractingbiochemicalvariablessuchasnitrogenandlignin[2,46],(e)dis-criminatingland-covertypes[7],(f)detectingcropmoisturevariations[8],(g)sensingsubtlevaria-tionsinleafpigmentconcentrations[2,9,10],(h)modelingbiophysicalandyieldcharacteristicsofagriculturalcrops[6,11,12],(i)improvingthedetectionofchangesinsparsevegetation[13],and(j)assessingabsolutewatercontentinplantleaves[14].Thisisafairlydetailedlistbutnotexhaus-tive,meanttoprovidethereaderwithameasureofthecurrent,provenexperimentalcapabilities,andoperationalapplications,andstimulateinvestigationsofnew,ambitiousapplications.
Thespectralpropertiesofvegetationarestronglydeterminedbytheirbiophysicalandbiochemi-calattributes, suchas leafarea index (LAI), theamountof livebiomassandsenescedbiomass,moisturecontent,pigments(e.g.,chlorophyll),andspatialarrangementofstructures[15,16].Wearecapableofmeasuringthosephenomenonandprocessestotesthypothesesandvaluableapplicationsonavarietyofecosystems.Forexample,assessmentofbiophysicalandbiochemicalpropertiesofvegetationsuchasrangelands[17,18],agriculturalcrops[1,7,11,12,19],andweeds[7]areessentialforevaluatingproductivity,providing informationneeded for local farmersand institutions,andassessinggrazingpotentialforlivestock.Eventhoughremotesensinghasbeenrecognizedasareli-ablemethodforestimatingthesebiophysicalandbiochemicalvegetationvariables,existingbroad-band sensorshaveproven inadequateor supplied limited information for thepurpose [1,7,2023].Clearly, broadbands have limitations in providing adequate information on properties such ascropgrowthstageidentification,croptypedifferentiation,generationofagriculturalcropstatistics,foresttypeandspeciesidentification,characterizingcomplexforestversusnonforestinteractions,anddetailmappingoflandcoverclassesofinteresttodiversescientificandotherusercommuni-ties(1,11).Thelimitationsofbroadbandanalysesareillustratedbyvegetationindices(VIs),whichsaturatebeyondacertainlevelofbiomassandLAI[12].Forexample,VIstypicallyincreaseoveranLAIrangefrom0tobetween3and5beforeanasymptoteisreached.Whileextremelyusefulovertheyears,theupperlimitofthissensitivityapparentlydiffersamongvegetationtypesandcanonlybedrivensofartoasolutionforagivenapplication.Saturationismorepronouncedforplanophilecanopies[11,12].However,comparedwitherectophilecanopiesofthesameLAI,planophilecano-piesarelessinfluencedbysoilbrightnessvariations[19].Incontrast,hyperspectraldatasetsallowidentification of features, direct measurement of canopy variables, such as biochemical content(e.g.,chlorophyll,nitrogen, lignin), forestspecies,chemistrydistribution, timbervolumes,water,etc.[2],andbiophysical(e.g.,LAI,biomass)andyieldcharacteristics[7,10,12,24].
Hyperspectralsensorsgathernear-continuousspectrafromimagingspectrometerssuchastheNational Aeronautics and Space Administrations (NASA)-designed Airborne Visible-infraredImagingSpectrometer(AVIRIS)andCompactAirborneSpectrographicImager(CASI).Thisnewgenerationofsensorsofferstremendousimprovementsinspatial,spectral,radiometric,andtemporalresolutionsaswellasimprovementsinopticsandmechanicswhencomparedwitholdergenerationofsensors(Table1.1).ThepromiseandpotentialofhyperspectralnarrowbandsensorsforawidearrayofEarthresourceapplicationshasmotivateddesignandalsothelaunchofspacebornesensorssuchasHyperiononboardtheEarthObserving-1(EO-1)[25,26],andtheupcomingHyperspectralImagingSpectrometerandInfraredImager(HypspIRI).Thesesensorsgatherdatain210220nar-rowbands from380 to2500nmat60mresolutionorbetter (Table1.1).TheHyspIRIsThermal
5A
dvan
ces in H
ypersp
ectral Rem
ote Sen
sing o
f Vegetatio
n an
d A
gricultu
ral Cro
plan
ds
TABLE1.1BroadbandandNarrowbandSatelliteSensorSpatial,Spectral,Radiometric,Waveband,andOtherDataCharacteristicsa
Sensor Spatial(m) Spectral(#)Radiometric
(Bit) BandRange(m)BandWidths
(m)Irradiance
(Wm2sr1m1)DataPoints(#perha)
FrequencyofRevisit
(Days)
A.Coarseresolutionbroadbandsensors
1.AVHRR 1,000 4 11 0.580.68 0.10 1,390 0.01 daily
0.7251.1 0.375 1,410
3.553.93 0.38 1,510
10.3010.95 0.65 0
10.9511.65 0.7 0
B.Coarseresolutionnarrowbandsensors
2.MODIS 250,500,1,000 36/7 12 0.620.67 0.05 1,528.2 0.16,0.04,0.01 daily
0.840.876 0.036 974.3 0.16,0.04,0.01
0.4590.479 0.02 2,053
0.5450.565 0.02 1,719.8
1.231.25 0.02 447.4
1.631.65 0.02 227.4
2.112.16 0.05 86.7
C.Multispectralbroadbandsensors
3.Landsat-1,2,3MSS 5679 4 6 0.50.6 0.1 1,970 2.26 16
0.60.7 0.1 1,843
0.70.8 0.1 1,555
0.81.1 0.3 1,047
4.Landsat-4,5TM 30 7 8 0.450.52 0.07 1,970 11.1 16
0.520.60 0.80 1,843
0.630.69 0.60 1,555
0.760.90 0.14 1,047
1.551.74 0.19 227.1
10.412.5 2.10 0
2.082.35 0.25 80.53
(continued)
6H
ypersp
ectral Rem
ote Sen
sing o
f Vegetatio
n
TABLE1.1(continued)BroadbandandNarrowbandSatelliteSensorSpatial,Spectral,Radiometric,Waveband,andOtherDataCharacteristicsa
Sensor Spatial(m) Spectral(#)Radiometric
(Bit) BandRange(m)BandWidths
(m)Irradiance
(Wm2sr1m1)DataPoints(#perha)
FrequencyofRevisit
(Days)
5.Landsat-7ETM+ 30 8 8 0.450.52 0.65 1,970 44.4,11.1 16
0.520.60 0.80 1,843
0.630.69 0.60 1,555
0.500.75 0.150 1,047
0.750.90 0.200 227.1
10.012.5 2.5 0
1.751.55 0.2 1,368
0.520.90(p) 0.38 1,352.71
6.ASTER 15,30,90 15 8 0.520.63 0.11 1,846.9 44.4,11.1,1.23 16
0.630.69 0.06 1,546.0
0.760.86 0.1 1,117.6
0.760.86 0.1 1,117.6
1.601.70 0.1 232.5
2.1452.185 0.04 80.32
2.1852.225 0.04 74.96
2.2352.285 0.05 69.20
2.2952.365 0.07 59.82
2.3602.430 0.07 57.32
12 8.1258.475 0.35 0
8.4758.825 0.35 0
8.9259.275 0.35 0
10.2510.95 0.7 0
10.9511.65 0.7 0
7A
dvan
ces in H
ypersp
ectral Rem
ote Sen
sing o
f Vegetatio
n an
d A
gricultu
ral Cro
plan
ds
7.ALI 30 10 12 0.0480.69(p) 0.64 1,747.8600
0.4330.453 0.20 1,849.5 11.1 16
0.4500.515 0.65 1,985.0714
0.4250.605 0.80 1,732.1765
0.6330.690 0.57 1,485.2308
0.7750.805 0.30 1,134.2857
0.8450.890 0.45 948.36364
1.2001.300 1.00 439.61905
1.5501.750 2.00 223.39024
2.0802.350 2.70 78.072727
8.SPOT-1 2.520 15 16 0.500.59 0.09 1,858 1,600,25 35
-2 0.610.68 0.07 1,575
-3 0.790.89 0.1 1,047
-4 1.51.75 0.25 234
0.510.73(p) 0.22 1,773
9.IRS-1C 23.5 15 8 0.520.59 0.07 1,851.1 18.1 16
0.620.68 0.06 1,583.8
0.770.86 0.09 1,102.5
1.551.70 0.15 240.4
0.50.75(P) 0.25 1,627.1
10.IRS-1 23.5 15 8 0.520.59 0.07 1,852.1 18.1 16
0.620.68 0.06 1,577.38
0.770.86 0.09 1,096.7
1.551.70 0.15 240.4
0.50.75(P) 0.25 1,603.9
11.IRS-P6-AWiFS 56 4 10 0.520.59 0.07 1,857.7 3.19 16
0.620.68 0.06 1,556.4
0.770.86 0.09 1,082.4
1.551.70 0.15 239.84
(continued)
8H
ypersp
ectral Rem
ote Sen
sing o
f Vegetatio
n
TABLE1.1(continued)BroadbandandNarrowbandSatelliteSensorSpatial,Spectral,Radiometric,Waveband,andOtherDataCharacteristicsa
Sensor Spatial(m) Spectral(#)Radiometric
(Bit) BandRange(m)BandWidths
(m)Irradiance
(Wm2sr1m1)DataPoints(#perha)
FrequencyofRevisit
(Days)
12.CBERS-2
-3B}-3
-4 }20mpan 11 0.510.73 0.22 1,934.03 25,25
20mMS 0.450.52 0.07 1,787.10
5mpan 0.520.59 0.07 1,587.97 400,25
20mMS 0.630.69 0.06 1,069.21
0.770.89 0.12 1,664.3
D.Hyperspectralnarrowbandsensors
13.Hyperion 30 220(196b) 16 196effective 10nmwide SeedatainNeckelandLabs(1984).PlotitandobtainvaluesforHyperionbands
11.1 16
Calibratedbands (approx.)forall
VNIR(band857 196bands
427.55925.85nm
SWIR(band79224)
932.722,395.53nm
14.ASDspectroradiometer 1,134cm2at1.2m
2,100bands 16 2,100effectivebands
1nmwide(approx.)in4002500nm
SeedatainNeckelandLabs(1984).PlotitandobtainvaluesforHyperionbands
88,183 516
Nadirview18Fieldofview
1nmwidthbetween
4002,500nm
15.HyspIRIVSWIR 60 210 16 210bandsin 10nmwide(approx.)forall210bands
SeedatainNeckelandLabs(1984).Plotit
2.77 19
3802,500nm
9A
dvan
ces in H
ypersp
ectral Rem
ote Sen
sing o
f Vegetatio
n an
d A
gricultu
ral Cro
plan
ds
16.HyspIRITIR 60 8 16 7bandsin7,50012,000nmand1bandin3,0005,000nm(3,980nmcenter)
7bandsin7,50012,000nm
SeedatainNeckelandLabs(1984).Plotit
2.77 5
E.Hyperspatialbroadbandsensors
17.IKONOS 14 4 11 0.4450.516 0.71 1,930.9 10,000,625 5
0.5060.595 0.89 1,854.8
0.6320.698 0.66 1,156.5
0.7570.853 0.96 1,156.9
18.QUICKBIRD 0.612.44 4 11 0.450.52 0.07 1,381.79 14,872,625 5
0.520.60 0.08 1,924.59
0.630.69 0.06 1,843.08
0.760.89 0.13 1,574.77
19.RESOURSESAT 5.8 3 10 0.520.59 0.07 1,853.6 33.64 24
0.620.68 0.06 1,581.6
0.770.86 0.09 1,114.3
20.RAPIDEYEA 6.5 5 12 0.440.51 0.07 1,979.33 236.7 12
E 0.520.59 0.07 1,752.33
0.630.68 0.05 1,499.18
0.690.73 0.04 1,343.67
0.770.89 0.12 1,039.88
21.WORLDVIEW 0.55 1 11 0.450.51 0.06 1,996.77 40,000 1.75.9
22.FORMOSAT-2 28 5 11 0.450.52 0.07 1,974.93 2,500,156.25 daily
0.520.60 0.08 1,743.12
0.630.69 0.06 1,485.23
0.760.90 0.14 1,041.28
0.450.90(p) 0.45 1,450
(continued)
10H
ypersp
ectral Rem
ote Sen
sing o
f Vegetatio
n
TABLE1.1(continued)BroadbandandNarrowbandSatelliteSensorSpatial,Spectral,Radiometric,Waveband,andOtherDataCharacteristicsa
Sensor Spatial(m) Spectral(#)Radiometric
(Bit) BandRange(m)BandWidths
(m)Irradiance
(Wm2sr1m1)DataPoints(#perha)
FrequencyofRevisit
(Days)
23.KOMPSAT-2 14 5 10 0.50.9 0.4 1,379.46 10,000,625 328
0.450.52 0.07 1,974.93
0.520.6 0.08 1,743.12
0.630.59 0.04 1,485.23
0.760.90 0.14 1,041.28
Source: EditedandadaptedfromThenkabail,P.S.etal.,Globalcroplandsandtheirwateruseremotesensingandnon-remotesensingperspectives.BookChapter.Chapter16.In:Weng,Q.(ed.),Advances in Environmental Remote Sensing: Sensors,Algorithms, and Applications,Taylor&Francis,BocaRaton,FL,2010;Melesse,A.M.etal.,Sens. J.7,3209,2007.http://www.mdpi.org/sensors/papers/s7123209.pdf
ASD,AnalyticalSpectralDevicesInc.Providerofhand-heldspectroradiometers;ASTER,AdvancedSpaceborneThermalEmissionandReflectionRadiometer;ALI,AdvancedLandImager;AVHRR,AdvancedVeryHighResolutionRadiometer;CBERS-2,China-BrazilEarthResourcesSatellite;CP,CrudeProtein(%);FNIR,Farnear-infrared(11001300nm);FORMOSAT,TaiwaneseSatelliteOperatedbyTaiwaneseNationalSpaceOrganizationNSPO.DataMarketedbySPOT;GEOEYE-1and2,ProvidingDatain0.251.65mresolution;Hyperion,FirstSpaceborneHyperspectralSensorOnboardEarthObserving-1(EO-1);HyspIRI,HyperspectralInfraredImager;IKONOS,High-ResolutionSatelliteOperatedbyGeoEye;IRS-1C/D-LISS,IndianRemoteSensingSatellite/LinearImagingSelfScanner;IRS-P6-AWiFS,IndianRemoteSensingSatellite/AdvancedWideFieldSensor;KOMFOSAT,KoreanMultipurposeSatellite.DataMarketedbySPOTImage;LAI,Leafareaindex(m2/m2);Landsat-1,2,3MSS,MultiSpectralScanner;Landsat-4,5TM,ThematicMapper;Landsat-7ETM+,EnhancedThematicMapperPlus;LiDAR,LightDetectionandRanging;LNA,LeafNitrogenAccumulation;MODIS,ModerateImagingSpectralRadioMeter;NIR,Near-infrared(7401100nm);N,Nitrogen(%);PLNTHT,PlantHeight(mm);QUICKBIRD,SatellitefromDigitalGlobe,aprivatecompanyintheUSA;RAPIDEYEA/E,SatelliteConstellationfromRapideye,aGermancompany;RESOURSESAT,SatelliteLaunchedbytheIndia;SPOT,SatellitesPourlObservationdelaTerreorEarth-ObservingSatellites;SWIR,ShortWaveInfrared(13002500nm);TIR,ThermalInfrared;VNIR,VisibleNear-InfraredSensor;VSWIR,VisibleandShortWaveInfrared;WBM,WetBiomass(kgm2);WORLDVIEW,DigitalGlobesEarthImagingSatellite.a Ofthe242bands,196areuniqueandcalibrated.Theseare(a)band8(427.55nm)toband57(925.85nm)thatareacquiredbyvisibleandnear-infrared(VNIR)sensor;and(b)band79
(932.72nm)toband224(2395.53nm)thatareacquiredbytheshortwaveinfrared(SWIR)sensor.
11Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Croplands
Infrared(TIR)has7bandsin7,50012,000nmthatsaturateat400kand1bandin3,0005,000nm(centeredat3,980nm)thatsaturatesat1400kandacquiresdatawith60mspatialresolution.
However,itmustbenotedthatusinghyperspectraldataismuchmorecomplexthanmultispec-traldata.Hyperspectralsystemscollectlargevolumesofdatainashorttimeleadingtoanumberofissuesthatneedtobeaddressed.Forexample,Hyperion,thefirstspacebornehyperspectralsensor,onboardEO-1launchedbytheNASAsNewMillenniumProgram(NMP)gathersnear-continuousdatain220discretenarrowbandsalongthe4002500nmspectralrangeat30mspatialresolutionandin12bits.Eachimageis7.5kminswathby100kmalongtrack.ThevolumeofdatacollectedusingHyperionforanareaequivalenttoLandsatTMimageareaisroughly37timesthedatavolumeoftheTMscene.
Increasesindatavolumeposegreatchallengesindatahandling.Theissuesincludedatastoragevolume,data storage rate,or transmissionbandwidth, real-timeanalog todigitalbandwidthandresolution,computingbottlenecksindataanalysis,andtheneedfornewalgorithmsfordatautiliza-tion(e.g.,atmosphericcorrectionismorecomplicated)[1,11].Theseissuesmakeitimperativethatmethodsandtechniquesbeadvancedanddevelopedtohandlehigher-dimensionaldatasets.
Futuregenerationsofsatellitesmaycarryspecializedoptimalsensorsdesignedtogatherdatafortargetedapplications.Ortheymaycarryanarrow-wavebandhyperspectralsensorlikeHyperionandHyspIRI fromwhichuserswithdifferent applicationneeds can extract appropriateoptimalwavebands.However,havingcontinuousspectralcoveragewithmanynarrowbandsdoesnotneces-sarilymeanmoreinformation.Indeed,mostofthesebands,andespeciallytheonesthatareclosetooneanother,provideredundantinformation.Thisredundancycanrequireuserstodevotesub-stantialtimeindatamining,complexprocessingtoidentifyandremoveredundantbands,andputsaheavyburdenoncomputing-processing-storageresources.
Afarbetteroptionistofocusonthedesignofanoptimalsensorforagivenapplication,suchasforvegetationstudies,andbyexcludingredundantbands.Evenwhenthedataareacquiredinfullrangeofhundredsorthousandsofhyperspectralnarrowbands,aprioriknowledgeofoptimalbandsforaparticularapplicationhelps.Investigatorscanquicklyselectthesebandsandmoreefficientlyspendtimeandexpertiseresourcesinusingthesefortherequiredapplication.Optimalhyperspectralsen-sorswillhelpreducedatavolumes,eliminatetheproblemsofhigh-dimensionalityofHyperspectraldatasets,andmakeitfeasibletoapplytraditionalclassificationmethodsonafewselectedbands(optimalbands)thatcapturemostoftheinformationofcropcharacteristics[4,7,9,11,27].Thereby,knowledgeofapplicationspecificoptimalbandsforhigh-dimensionaldatasets,suchasHyperionandHyspIRIiscrucialtoreducecostsindataanalysisandcomputerresources.
Table1.1comparesthespectralandspatialresolutionofnarrowbandandbroadbanddatathatarecurrentlyinuseandthosesoontobeused.Anumberofrecentstudieshaveindicatedtheadvan-tagesofusingdiscretenarrowbanddatafromaspecificportionofthespectrumwhencomparedwithbroadbanddatatoarriveatoptimalquantitativeorqualitativeinformationoncroporvegeta-tioncharacteristics[2,4,7,9,11,12,27].Hence,thisapproachordirectionhascaughttheattentionofinvestigatorsandtheremotesensingcommunityismovinginthisdirection.
Theoverarchinggoalofthischapteristoexploreanddeterminetheoptimalhyperspectralnar-rowbandsforuseinthestudyofvegetationandagriculturalcropsandtoenumeratemethodsandapproaches.ThisispartiallytoovercomethecurseofhighdimensionalityorHughesphenom-enon,wheretheratioofthenumberofpixelswithknownclassidentity(i.e.,trainingpixels)andthenumberofbandsmustbemaintainedatoraboveminimumvaluetoachievestatisticalconfidenceandfunctionality.Inhyperspectraldata,withhundredsoreventhousandsofwavebands,thenum-beroftrainingpixelsneededgrowsexponentially(Hughesphenomenon),makingitverydifficulttoaddressthisspectraldiversity.
Ourfirstobstacleistoidentifyhyperspectralnarrowbandsthatarebestsuitedforstudyingnaturalvegetationandagriculturalcroplands. In theprocess,wehavedetectedandeliminated redundantbandsorexamplesthatsupplylittleknowledgetotheapplication.Wethenhighlightoptimalhyper-spectralwavebands,in4002500nmrange,bestsuitedtostudyvegetationandagriculturalcrops.
12 Hyperspectral Remote Sensing of Vegetation
Thereareanumberofstudies[1,4,5,7,20,2729]thatindicatethatthenarrowwavebandslocatedinspecificportionsofthespectrumhavetheabilitytoproviderequiredoptimalinformationsoughtforagivenapplication.However,thereisaclearneedforsynthesisofthestudiesconductedindifferentpartsoftheworldtofindageneralconsensusofoptimalwavebandsthatappliestothevaryingvegeta-tiontypesandagricultura