Hyperspectral Remote Sensing of Vegetation Prasad S. Thenkabail, John G. Lyon, Alfredo Huete

HYPERSPECTRALREMOTE SENSINGOF VEGETATIONI '

HYPERSPECTRALREMOTE SENSINGOF VEGETATION

Edited by

Prasad S. ThenkabailJohn G. LyonAlfredo Huete

HYPERSPECTRALREMOTE SENSINGOF VEGETATION

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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


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.


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.



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.



Rossini MicolRemote.Sensing.of.Environmental.Dynamics.



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.


Javier PlazaHyperspectral.Computing.LaboratoryDepartment.of.Technology.of.Computers.


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.



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.


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.


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.



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.



Part I

Introduction and Overview

31 Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Croplands


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

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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)

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TABLE1.1(continued)BroadbandandNarrowbandSatelliteSensorSpatial,Spectral,Radiometric,Waveband,andOtherDataCharacteristicsa



(m)Irradiance


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

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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)

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(m)Irradiance


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

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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)

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(m)Irradiance


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

Hyperspectral Remote Sensing of Vegetation Prasad S. Thenkabail, John G. Lyon, Alfredo Huete

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