Remote Sensing on land Surface Properties

Menglin Jin

Modified from Paolo Antonelli CIMSS, University of Wisconsin-Madison, Paolo Antonelli CIMSS, University of Wisconsin-Madison, M. D. King UMCP lecture, and P. MentzelM. D. King UMCP lecture, and P. Mentzel

outline

• Reflectance and albedo

• Vegetation retrieval

• Surface temperature retrieval

• Theory of clouds and fire retrieval

MODIS Land Cover Classification(M. A. Friedl, A. H. Strahler et al. – Boston University)

0 Water

1 Evergreen Needleleaf Forest

2 Evergreen Broadleaf Forest3 Deciduous Needleleaf Forest

4 Deciduous Broadleaf Forest

5 Mixed Forests

6 Closed Shrublands

7 Open Shrublands

8 Woody Savannas

9 Savannas

10 Grasslands

11 Permanent Wetlands

12 Croplands

13 Urban and Built-Up

14 Cropland/Natural Veg. Mosaic

15 Snow and Ice16 Barren or Sparsely Vegetated

17 Tundra

ReflectanceReflectance• The physical quantity is the Reflectance i.e. The physical quantity is the Reflectance i.e.

the fraction of solar energy reflected by the the fraction of solar energy reflected by the observed targetobserved target

• To properly compare different reflective To properly compare different reflective channels we need to convert observed channels we need to convert observed radiance into a target physical propertyradiance into a target physical property

• In the In the visiblevisible and and near infrarednear infrared this is done this is done through the ratio of the observed radiance through the ratio of the observed radiance divided by the incoming energy at the top of divided by the incoming energy at the top of the atmospherethe atmosphere

SoilSoil

VegetationVegetation

SnowSnow

OceanOcean

MODIS multi-channels

– Band 1 (0.65 m) – clouds and snow reflecting – Band 2 (0.86 m) – contrast between vegetation and

clouds diminished– Band 26 (1.38 m) – only high clouds and moisture

detected– Band 20 (3.7 m) – thermal emission plus solar

reflection– Band 31 (11 m) – clouds colder than rest of scene

-- Band 35 (13.9 m) – only upper atmospheric thermal emission detected

MODIS BAND 1 (RED)MODIS BAND 1 (RED)

Low reflectance in Low reflectance in Vegetated areasVegetated areas

Higher reflectance inHigher reflectance inNon-vegetated land areasNon-vegetated land areas

MODIS BAND 2 (NIR)MODIS BAND 2 (NIR)

Higher reflectance in Higher reflectance in Vegetated areasVegetated areas

Lower reflectance inLower reflectance inNon-vegetated land areasNon-vegetated land areas

REDREDNIRNIR

Dense VegetationDense Vegetation

Barren SoilBarren Soil

Vegetation: NDVI

• Subsequent work has shown that the NDVI is directly related to the photosynthetic capacity and hence energy absorption of plant canopies.

The NDVI is calculated from these individual measurements as follows:

NIR-RED

NIR+REDNDVI =

NDVI –Normalized Difference Vegetation Index

Satellite maps of vegetation show the density of plant growth over the entire globe. The most common measurement is called the Normalized Difference Vegetation Index (NDVI). Very low values of NDVI (0.1 and below) correspond to barren areas of rock, sand, or snow. Moderate values represent shrub and grassland (0.2 to 0.3), while high values indicate temperate and tropical rainforests (0.6 to 0.8).

NDVI

• Vegetation appears very different at visible and near-infrared wavelengths. In visible light (top), vegetated areas are very dark, almost black, while desert regions (like the Sahara) are light. At near-infrared wavelengths, the vegetation is brighter and deserts are about the same. By comparing visible and infrared light, scientists measure the relative amount of vegetation.

NDVI represents greenness

NDVI as an Indicator of Drought August 1993

In most climates, vegetation growth is limited by water so the relative density of vegetation is a good indicator of agricultural drought

Enhanced Vegetation Index (EVI)

• In December 1999, NASA launched the Terra spacecraft, the flagship in the agency’s Earth Observing System (EOS) program. Aboard Terra flies a sensor called the Moderate-resolution Imaging Spectroradiometer, or MODIS, that greatly improves scientists’ ability to measure plant growth on a global scale.

• EVI is calculated similarly to NDVI, it corrects for some distortions in the reflected light caused by the particles in the air as well as the ground cover below the vegetation.

• does not become saturated as easily as the NDVI when viewing rainforests and other areas of the Earth with large amounts of chlorophyll

Electromagnetic spectrum

0.001m1m1000 m1m1000m

1,000,000 m = 1m

GammaX raysUlt

ravi

olet

(U

V)

Infrared (IR)MicrowaveRadio waves

Red(0.7m)

Orange(0.6m)

YellowGreen

(0.5m)Blue

Violet(0.4m)

Visible

Longer waves Shorter waves

• Spectral albedo needed for retrievals over land surfaces

• Spatially complete surface albedo datasets have been generated– Uses high-quality operational MODIS surface albedo dataset

(MOD43B3)– Imposes phenological curve and ecosystem-dependent

variability – White- and black-sky albedos produced for 7 spectral bands

and 3 broadbands

• See modis-atmos.gsfc.nasa.gov for data access and further descriptions

Spectral Surface Albedo(E. G. Moody, M. D. King, S. Platnick, C. B. Schaaf, F. Gao

– GSFC, BU)

Conditioned Spectral Albedo Maps(C. B. Schaaf, F. Gao, A. H. Strahler

- Boston University)

MOD43B3

Indian Subcontinent during MonsoonJune 10-26, 2002

Spatially Complete Spectral Albedo Maps(E. G. Moody, M. D. King, S. Platnick, C. B.

Schaaf, F. Gao – GSFC, BU)

Used near real-time ice and snow extent (NISE) dataset– Distinguishes land snow and sea ice (away from coastal

regions)– Identifies wet vs dry snow

» Projected onto an equal-area 1’ angle grid (~2 km)

Aggregate snow albedo from MOD43B3 product– Surface albedo flagged as snow

» Aggregate only snow pixels whose composite NISE snow type is >90% is flagged as either wet or dry snow in any 16-day period

– Hemispherical multiyear statistics» Separate spectral albedo by ecosystem (MOD12Q1)

Spectral Albedo of Snow

Albedo by IGBP EcosystemNorthern Hemisphere Multiyear Average (2000-2004)

urbancropland

???

???

Surface Temperature: Skin Temperature

• The term “skin temperature” has been used for “radiometric surface temperature” (Jin et al. 1997).

• can be measured by either a hand-held or aircraft-mounted radiation thermometer, as derived from upward longwave radiation based on the Stefan-Boltzmann law (Holmes 1969; Oke 1987)

Surface Temperature: Skin Temperature

• The retrieval techniques for obtaining Tskin from satellite measurements for land applications have developed substantially in the last two decades (Price 1984).

Tskinb = B-1

( L)

Include emissivity effect:

Tskinb = B-1 [(L-(1- )L )/ ]

Two unknowns!!

Surface Temperature: Skin Temperature

• Split Window Algorith• Retrieving Tskin using the two channels (i.e., SWT)

was first proposed in the 1970s (Anding and Kauth 1970).

For example:

The NOAA Advanced Very High Resolution Radiometer (AVHRR), which has spectral channels centered around 10.5 μm and 11.2 μm, has been widely used in this regard for both land and sea surface temperature estimation

Surface Temperature: Skin Temperature

Split-window algorithms are usually written in “classical" form, as suggested by Prabhakara (1974)(after Stephens 1994):

Tskin ≈ Tb,1 + f(Tb,1 – Tb,2), – where Tb,1 , Tb,2 are brightness measurements in

two thermal channels, and f is function of atmospheric optical depth of the two channels.

– A more typical form of the split-window isTskin = aT1 + b(T1 –T2) – c

where a, b and c are functions of spectral emissivity of the the two channels and relate radiative transfer model simulations or field measurements of Tskin to the remotely sensed observations

MODIS SST Algorithm

• Bands 31 (11 m) and 32 (12 m) of MODIS are sensitive to changes in sea surface temperature, because the atmosphere is almost (but not completely) transparent at these wavelengths. An estimate of the sea surface temperature (SST) can be made from band 31, with a water vapor correction derived from the difference between the band 31 and band 32 brightness temperatures:

• SST ≈ B31 + (B31 – B32) (just this simple!)

MODIS SST

Accuracy of Retrieved Tskin

• Accuracy of Tskin retrievals with SWT ranges from ≤ 1 to ≥ 5 K ( Prata 1993, Schmugge et al. 1998).

• Error sources:split window equation;Specifically, split window techniques rely on

assumptions of Lambertian surface properties, surface spectral emissivity, view angle, and approximations of surface temperature relative to temperatures in the lower atmosphere (which vary more slowly). An assumption of invariant emissivity, for example, can induce errors of 1-2 K per 1% variation in emissivity.

MODIS 2000-2007 averaged monthly Tskin

200

220

240

260

280

300

320

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Land cover

Land

sur

face

tem

pera

ture

Jan

Apr

Jul

Oct

Modis land cover. 1. Evergreen Needleleaf Forest;2,Evergreen Broadleaf Forest; 3,Deciduous Needleleaf Forest; 4,Deciduous Broadleaf Forest; 5,Mixed Forest; 6,Closed Shrubland; 7,Open Shrubland; 8,Woody Savannas; 9,Savannas; 10,Grassland; 11,Permanent Wetland; 12,Croplands; 13,Urban and Built-Up; 14,Cropland/Narural Vegetation Mosaic; 15,Snow and Ice; 16,Barren or Sparsely Vegetated

Land Tskin vs Albedo

Land Tskin vs. Water Vapor

Band 4 Band 4 (0.56 µm)(0.56 µm)

Band 1Band 1Band 4Band 4Band 3Band 3

snowsnow

cloudsclouds

sea

sea

desertdesert

Transects of ReflectanceTransects of Reflectance

Band 26Band 261.38 micron1.38 micronStrong HStrong H2200

1.38 1.38 μμm m Only High Clouds Only High Clouds Are VisibleAre Visible

Band 26Band 261.38 µm1.38 µm

VisibleVisible(Reflective Bands)(Reflective Bands)

InfraredInfrared(Emissive Bands)(Emissive Bands)

VisibleVisible(Reflective Bands)(Reflective Bands)

InfraredInfrared(Emissive Bands)(Emissive Bands)

Emissive BandsEmissive Bands

Used to observe terrestrial energy emitted by the Earth Used to observe terrestrial energy emitted by the Earth system in the IR between 4 and 15 µm system in the IR between 4 and 15 µm

• About 99% of the energy observed in this range is About 99% of the energy observed in this range is emitted by the Earthemitted by the Earth

• Only 1% is observed below 4 µmOnly 1% is observed below 4 µm• At 4 µm the solar reflected energy can significantly At 4 µm the solar reflected energy can significantly

affect the observations of the Earth emitted energyaffect the observations of the Earth emitted energy

Spectral Characteristics of Spectral Characteristics of Energy Sources and Sensing SystemsEnergy Sources and Sensing Systems

IRIR

4 µm4 µm11 µm11 µm

NIR (0.86 µm)NIR (0.86 µm)Green (0.55 µm)Green (0.55 µm) Red (0.67 µm)Red (0.67 µm)

RGBRGB NIRNIR

OceanOcean

NIR and VIS over Vegetation and OceanNIR and VIS over Vegetation and Ocean

VegetationVegetation

Spectral Characteristics of Spectral Characteristics of Energy Sources and Sensing SystemsEnergy Sources and Sensing Systems

IRIRNIRNIR

Radiation is governed by Planck’s LawRadiation is governed by Planck’s Law

In wavelength:In wavelength: B(B(,T) = c,T) = c11

/{ /{ 55 [e [e c2 /c2 /TT -1] } -1] } (mW/m(mW/m22/ster/cm)/ster/cm)

wherewhere = wavelength (cm) = wavelength (cm)T = temperature of emitting surface (K)T = temperature of emitting surface (K)cc11 = 1.191044 x 10-8 (W/m = 1.191044 x 10-8 (W/m22/ster/cm/ster/cm-4-4))cc22 = 1.438769 (cm K) = 1.438769 (cm K)

In wavenumber:In wavenumber:B(B(,T) = c,T) = c113 3 / [e / [e c2c2/T/T -1] -1] (mW/m (mW/m22/ster/cm/ster/cm-1-1))

wherewhere = # wavelengths in one centimeter (cm-1) = # wavelengths in one centimeter (cm-1)T = temperature of emitting surface (K)T = temperature of emitting surface (K)cc11 = 1.191044 x 10-5 (mW/m = 1.191044 x 10-5 (mW/m22/ster/cm/ster/cm-4-4))cc22 = 1.438769 (cm K) = 1.438769 (cm K)

B(B(max,T)~Tmax,T)~T55 B(B(max,T)~Tmax,T)~T33

Planck Radiances

0

20

40

60

80

100

120

140

160

180

0 5 10 15 20 25 30

wavenumber (in hundreds)

mW

/m2/

ster

/cm

(cm

-1)

B(B(,T),T)

B(B(,T),T)

B(B(,T) versus B(,T) versus B(,T),T)

2020 1010 55 44 3.3.33

6.6.66

100100

wavelength [µm]wavelength [µm]

max max ≠(1/≠(1/ maxmax))

wavelength wavelength : distance between peaks (µm) : distance between peaks (µm)

Slide 4

wavenumber wavenumber : number of waves per unit : number of waves per unit distance (cm)distance (cm)

=1/ =1/

dd=-1/ =-1/ 22 d d

Using wavenumbersUsing wavenumbers

Wien's Law Wien's Law dB(dB(maxmax,T) / dT = 0 ,T) / dT = 0 where where (max) = 1.95T(max) = 1.95Tindicates peak of Planck function curve shifts to shorter wavelengths indicates peak of Planck function curve shifts to shorter wavelengths (greater wavenumbers) with temperature increase. Note (greater wavenumbers) with temperature increase. Note B(B(maxmax,T) ~ T**3. ,T) ~ T**3.

Stefan-Boltzmann Law Stefan-Boltzmann Law E = E = B( B(,T) d,T) d = = TT44, , where where = 5.67 = 5.67

x 10-8 W/mx 10-8 W/m22/deg/deg44.. 00

states that irradiance of a black body (area under Planck curve) is states that irradiance of a black body (area under Planck curve) is proportional to Tproportional to T44 . .

Brightness TemperatureBrightness Temperature

cc1133

T = cT = c22/[ln(/[ln(____________ + 1)] + 1)] is determined by inverting Planck is determined by inverting Planck

functionfunction BB

Brightness temperature is uniquely related to radiance for a given Brightness temperature is uniquely related to radiance for a given wavelength by the Planck functionwavelength by the Planck function

Planck Function and MODIS BandsPlanck Function and MODIS Bands

MODISMODIS

MODIS BAND 20MODIS BAND 20

Window Channel:Window Channel:•little atmospheric absorptionlittle atmospheric absorption•surface features clearly visiblesurface features clearly visible

Clouds are coldClouds are cold

MODIS BAND 31MODIS BAND 31

Window Channel:Window Channel:•little atmospheric absorptionlittle atmospheric absorption•surface features clearly visiblesurface features clearly visible

Clouds are coldClouds are cold

Clouds at 11 µm look Clouds at 11 µm look bigger than at 4 µmbigger than at 4 µm

Temperature sensitivityTemperature sensitivity

dB/B = dB/B = dT/T dT/T

The Temperature Sensitivity The Temperature Sensitivity is the is the percentage change in radiance percentage change in radiance corresponding to a percentage change in corresponding to a percentage change in temperature temperature

Substituting the Planck Expression, the Substituting the Planck Expression, the equation can be solved in equation can be solved in ::

= c= c22/T/T

Planck’s function (review lecture 1 )

B (T) = c1-5

exp (c2 / T ) -1

Irridance:Blackbody radiative fluxfor a single wavelength at temperature T (W m-2)

Second radiation constantAbsolute temperature

First radiation constant Wavelength of radiation

Total amount of radiation emitted by a blackbody is a function of its temperaturec1 = 3.74x10-16 W m-2 c2 = 1.44x10-2 m °K

∆∆BB1111

∆∆BB44

∆∆BB1111> > ∆B∆B44

T=300 KT=300 K

TTrefref=220 K=220 K

∆∆BB1111/B/B11 11 = = 1111 ∆T/T ∆T/T

∆∆BB44/B/B44= = 44 ∆T/T ∆T/T

∆∆BB44/B/B44>∆B>∆B1111/B/B11 11 44 > > 1111

(values in plot are referred to wavelength)(values in plot are referred to wavelength)

∆∆B/B=B/B= ∆T/T ∆T/T

Integrating the Temperature Integrating the Temperature Sensitivity EquationSensitivity EquationBetween TBetween Trefref and T (B and T (Brefref and B): and B):

B=BB=Brefref(T/T(T/Trefref))

Where Where =c=c22/T (in wavenumber space)/T (in wavenumber space)

(Approximation of) B as f(Approximation of) B as function of unction of and T and T

B=BB=Brefref(T/T(T/Trefref))

B=(BB=(Brefref/ T/ Trefref

) T ) T

BB T T The temperature sensitivity indicates the power to which the Planck The temperature sensitivity indicates the power to which the Planck radiance depends on temperature, since B proportional to Tradiance depends on temperature, since B proportional to T satisfies satisfies the equation. For infrared wavelengths, the equation. For infrared wavelengths,

= c= c22/T = c/T = c22//T. T. __________________________________________________________________________________________________________________

WavenumberWavenumber Typical SceneTypical Scene Temperature Temperature TemperatureTemperature SensitivitySensitivity

900900 300300 4.324.3225002500 300300 11.9911.99

Non-Homogeneous FOVNon-Homogeneous FOV

NN

1-N1-N

TTcoldcold=220 K=220 K

TThothot=300 K=300 K

B=NB(TB=NB(Thothot)+(1-N)B(T)+(1-N)B(Tcoldcold))

BT=NBTBT=NBThothot+(1-N)BT+(1-N)BTcoldcold

For NON-UNIFORM FOVs:For NON-UNIFORM FOVs:

BBobsobs=NB=NBcoldcold+(1-N)B+(1-N)Bhothot

BBobsobs=N B=N Brefref(T(Tcoldcold/T/Trefref))+ (1-N) B+ (1-N) Brefref(T(Thothot/T/Trefref))

BBobsobs= B= Brefref(1/T(1/Trefref)) (N T (N Tcoldcold + (1-N)T + (1-N)Thothot

))

For N=.5For N=.5

BBobsobs= .5B= .5Brefref(1/T(1/Trefref)) ( T ( Tcoldcold + T + Thothot

))

BBobsobs= .5B= .5Brefref(1/T(1/TrefrefTTcoldcold)) (1+ (T (1+ (Thothot/ T/ Tcoldcold)) ))

The greater The greater the more predominant the hot term the more predominant the hot term

At 4 µm (At 4 µm (=12) the hot term more dominating than at 11 =12) the hot term more dominating than at 11 µm (µm (=4)=4)

NN1-N1-N TTcoldcold

TThothot

Consequences: Cloud & Fire Consequences: Cloud & Fire Detection Detection

• At 4 µm (At 4 µm (=12) clouds look smaller than =12) clouds look smaller than at 11 µm (at 11 µm (=4)=4)

• In presence of fires the difference BTIn presence of fires the difference BT44--

BTBT1111 is larger than the solar contribution is larger than the solar contribution

• The different response in these 2 The different response in these 2 windows allow for cloud detection and windows allow for cloud detection and for fire detectionfor fire detection

The algorithm uses these thresholds to determine ice cloud:Band 31 (11 m) Brightness Temperature < 238 K or Band 29 – Band 31 difference > .5 K

The water cloud algorithm thresholds are

Band 31 (11 m) Brightness Temperature > 238 K andBand 29 – Band 31 difference < -1.0 KOR: OrBand 31 (11 m) Brightness Temperature > 285 K andBand 29 – Band 31 difference < -0.5 K

Band 29 (8.6 m) Band 31 (11 m

MODIS clouds algorithm (As an example)

Conclusions: Vegetation Conclusions: Vegetation DetectionDetection

• VegetationVegetation: highly reflective in the : highly reflective in the Near InfraredNear Infrared and highly and highly absorptive in the absorptive in the visible redvisible red. The contrast between these . The contrast between these channels is a useful indicator of the status of the vegetation;channels is a useful indicator of the status of the vegetation;

• Planck FunctionPlanck Function: at any wavenumber/wavelength relates the : at any wavenumber/wavelength relates the temperature of the observed target to its radiance (for temperature of the observed target to its radiance (for Blackbodies) Blackbodies)

• Thermal SensitivityThermal Sensitivity: different emissive channels respond : different emissive channels respond differently to target temperature variations. Thermal Sensitivity differently to target temperature variations. Thermal Sensitivity helps in explaining why, and allows for cloud and fire detection.helps in explaining why, and allows for cloud and fire detection.