Remote Sensing of Soils andGeomorphology

19 May 2003

Outline

• Soils– Properties that are detectable from remote

sensing using optical and radar sensors

• Geomorphology– Patterns as an indication of processes

Factors Influencing Soil Reflectance

• mineral composition

• soil moisture

• organic matter content

• texture and roughness

• salinity

Soils: Mineral Composition

• Affects the visible, NIR, and thermal portions of thereflectance spectrum

• Increasing reflectance from visible to NIR

• Iron and clays are detectable– oxidized iron appear red

– reduced iron appear green-blue

– clays affect water and OH+ absorption features

Basic Dry Soil Spectrum

20

60

100Pe

rcen

t Ref

lect

ance

0.5 0.7 1.1 1.30

Wavelength (mm)

80

40

0.9 1.5 1.7 1.9 2.1 2.3 2.5

Silt

Sand

10

30

50

70

90

Key characteristic of soil spectrum: increasing reflectance with increasingwavelength through the visible, near and mid infrared portions of the spectrum

Iron Oxide

• Iron oxide causes absorption in the UV, blue and green wavelengths as well as absorptionin the NIR (850 to 900 nm). Also, scattering in the red is higher for soils with iron oxide,leading to a red color.

Iron Oxide

• Iron content in the Santa Monica mountains mapped using AVIRIS(Palacios-Orueta et al. 1999)

Soils: Organic Matter Content

• OM affects soil color, heat capacity, waterholding capacity, nutrient exchange, structure,and erodability

• Dark color generally associated with high OM

• Landsat TM bands 5 and 6 have negativecorrelation with OM

Soil Organic Matter

• Organic matter is a strong absorber of EMR, so more organic matterleads to darker soils (lower reflectance curves)

Soil Organic Matter

• Organic matter content in the Santa Monica mountains mappedusing AVIRIS (Palacios-Orueta et al. 1999)

Soils: Texture

• Texture– %sand, %silt, %clay (these are size classes)

0.002 0.05 0.1 0.25 0.5 1 2 mm

Particle size relative to a grain of sand 0.15 mm in diameter

Sand

SiltClay

Clay Silt Sandv. fine fine medium coarse v. coarse Gravel

0.002 0.06 0.2 2 mm

Clay Silt SandGravel Stones

fine medium coarse

0.006 0.02 0.6

0.15 mm

fine medium coarse

76.2

0.002 0.2 2 mm

Clay Silt SandGravel

0.02fine coarse

a. Soil Science Society of America and U.S. Department of Agriculture Soil Particle Size Scale

b. MIT and British Standards Institute

c. International Society of Soil Science

Soil Texture• Proportion of sand, silt and clay

in a soil (or horizon), usuallycalculated as % weight for eachtype of particle

• These percentages are divideddifferent soil texture classes

100

90

80

70

60

50

40

30

20

10

100100 90 80 70 60 50 40 30 20 10

90

80

70

60

50

40

30

20

10

Clay

(%)

Sand (%)

Silt (%)

read

read

read

Clay

SiltSand

loamysand

sandy loam

Loam

sandy clayloam

silty clay

silty clayloam

clay loam

sandy clay

silt loam

Basic Dry Soil Spectrum

20

60

100Pe

rcen

t Ref

lect

ance

0.5 0.7 1.1 1.30

Wavelength (mm)

80

40

0.9 1.5 1.7 1.9 2.1 2.3 2.5

Silt

Sand

10

30

50

70

90

Key characteristic of soil spectrum: increasing reflectance with increasingwavelength through the visible, near and mid infrared portions of the spectrum

Soil Moisture

• Water is a strong absorber, sosoils with more moisture will bedarker over most of the VNIRand SWIR portions of thespectrum than drier soils.

• The depths of the waterabsorption bands at 1.4, 1.9and 2.7 mm can be used toestimate soil moisture.

specularreflectance

incident energy

interstitialair space

specularreflectance

soil water

a.

b.

dry soil

wet soil

volume reflectancespecular reflectance

incident energy

Soil Moisture and Texture

• Since clayey soil holdswater more tightly thansandy soil, the waterabsorption features willbe more prominent inclayey soils given thesame amount of timesince the lastprecipitation orwatering.

• AVIRIS can be usefulfor quantifying theseabsorption features.

20

60

Perc

ent R

efle

ctan

ce

0.5 0.7 1.1 1.30

40

0.9 1.5 1.7 1.9 2.1 2.3 2.5

22 – 32%

10

30

50Sand

20

60

0.5 0.7 1.1 1.30

Wavelength (mm)

40

0.9 1.5 1.7 1.9 2.1 2.3 2.5

35 – 40% 10

30

50 2 – 6%

0 – 4% moisture content

5 – 12%

Clay

a.

b.

Perc

ent R

efle

ctan

ce

SandSandSand

ClayClayClay

Soils: Moisture Content

• Theoretically, soil moisture is directly relatedto surface soil moisture content

• SAR estimates of soil moisture are alsoinfluenced by:– roughness– topography– vegetation density

Soil Moisture from RADAR

• Higher dielectricconstants (moremoisture) yields higherRADAR backscatter.

Melfort, Saskatchewan, Canada,ERS-1: Rainfall was incident on thelower half of the image but not on theupper half.

Surface Soil Moisture from Synthetic Aperture Radar

Soil Moisture from Thermal Sensors

• Water has a higherthermal capacity thansoil and rock.

• Moist soils will changein temperature moreslowly than dry soils

• Apparent thermal inertiacan be used todistinguish dry frommoist soils

Soil Moisture from Thermal Sensors

Daedalus thermal image(night time). If we had adaytime image tocompare it to, we couldsee the amount ofchange in temperatureand make inferences onthe soil moisturecontent (less change =more moisture).

Soils: Salinity

• Saline soils are a major limiting factor forvegetation growth– drawdown of coastal aquifers allows saltwater

intrusion into groundwater

– increase in irrigation in regions of salt-bearingminerals will activate salts into the groundwater

– evaporation of salt-bearing irrigation water willincrease soil salinity over time

Application: Remote Sensing of SwellingClay Soils

• Smectite clays (e.g. bentonite) can absorb more than theirweight in water, have >100% increases in volume

• Responsible for massive damage to structures throughout U.S.

Colorado Front RangeGeologic Map

Pierre shale containshigh concentrations ofbentonite clays

AVIRIS data acquiredto try to usehyperspectral imageryfor mapping bentonite(Chabrillat and Goetz,1999)

Bentonite clay exposurenear Denver, CO

increasingswellingpotential

Geomorphology

• Landforms: patterns and processes– eolian

– igneous

– tectonic

– karstic

– fluvial

– shoreline

– glacial

Landforms

• Typically identified by shape, not spectralproperties

• Use shadowing to identify features– optical (VIS/NIR imagery)

– synthetic aperture radar• useful in regions frequent cloud cover

Eolian landforms

crescentic dunes from SAR crescentic dunes from ASTER

longitudinal dunes from ASTER(w/clays between dunes)

star dunes from Landsat TM

Igneous landforms

Mount Rainier, Washington

stratovolcano

Western Australia granite-greenstone complex: Pilbara Block

granite batholiths intruded into metamorphosed basalt

Tectonic Landforms

Landsat TMimage ofNevada basinand rangeregion

Colorado Rocky Mountainsfrom Landsat TM (band 4)

Canadian Rocky Mountainsfrom Radarsat

Karstic Landforms

• Karst topography develops by chemical dissolution of limestonerocks

• Creates caves, sinkholes, solution valleys, towers, pittedlandscapes

Landsat TM image of Guangxiprovince, China (karst areas are dark)

photo of karstic towers in Guangxi,province China

Fluvial Landforms

• dendritic networks are easily recognized

Radar image of dendritic river network in east-central Colombia

Flat floodplains haveshifting river channelsproducing meanders andoxbows

Edge of floodplain ismarked by bluffs onwhich is maturevegetation

Lower Mississippi River Floodplain

Shoreline Landforms

MISR image of Mississippi Delta (birdsfoot delta)multi-angle composite false color composite

true color composite

Quickbird imageof Bora Boraisland (atoll)

Landsat image offjords on Iceland

Glacial Landforms

ASTER image of receding glaciers in Bhutan(note lakes forming at glacier termini)

Landsat TM image of “fluting” causedby massive glacial erosion of bedrock onthe Canadian shield during the last iceage