Remote sensing on land surface properties
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Remote Sensing on land Surface Properties. Menglin Jin. Modified from Paolo Antonelli CIMSS, University of Wisconsin-Madison, M. D. King UMCP lecture, and P. Mentzel. outline. Reflectance and albedo Vegetation retrieval Surface temperature retrieval Theory of clouds and fire retrieval.

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Remote Sensing on land Surface Properties

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Remote sensing on land surface properties

Remote Sensing on land Surface Properties

Menglin Jin

Modified from Paolo Antonelli CIMSS, University of Wisconsin-Madison,

M. D. King UMCP lecture, and P. Mentzel


Outline

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

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

0 Water

6 Closed Shrublands

12Croplands

1 Evergreen Needleleaf Forest

7 Open Shrublands

13 Urbanand Built-Up

2 Evergreen BroadleafForest

8 Woody Savannas

14 Cropland/NaturalVeg. Mosaic

3 Deciduous Needleleaf Forest

9 Savannas

15 Snowand Ice

4 Deciduous Broadleaf Forest

10 Grasslands

16 Barren or Sparsely Vegetated

5 Mixed Forests

11 Permanent Wetlands

17 Tundra


Reflectance

Reflectance

  • The physical quantity is the Reflectance i.e. the fraction of solar energy reflected by the observed target

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

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


Remote sensing on land surface properties

Soil

Vegetation

Snow

Ocean


Modis multi channels

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

Vegetated areas

Higher reflectance in

Non-vegetated land areas


Modis band 2 nir

MODIS BAND 2 (NIR)

Higher reflectance in

Vegetated areas

Lower reflectance in

Non-vegetated land areas


Remote sensing on land surface properties

RED

NIR

Dense Vegetation

Barren Soil


Vegetation ndvi

Vegetation: NDVI

The NDVI is calculated from these individual measurements as follows:

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

NIR-RED

NDVI =

NIR+RED

NDVI –Normalized Difference Vegetation Index


Remote sensing on land surface properties

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).


Remote sensing on land surface properties

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 represents greenness


Ndvi as an indicator of drought

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

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

Electromagnetic spectrum

Red

(0.7m)

Orange

(0.6m)

Yellow

Green

(0.5m)

Blue

Violet

(0.4m)

Visible

Ultraviolet (UV)

Radio waves

Microwave

Infrared (IR)

X rays

Gamma

1000m

1m

1000 m

1m

0.001m

Longer waves Shorter waves

1,000,000m = 1m


Spectral surface albedo e g moody m d king s platnick c b schaaf f gao gsfc bu

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

  • 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.govfor data access and further descriptions


Conditioned spectral albedo maps c b schaaf f gao a h strahler boston university

Conditioned Spectral Albedo Maps(C. B. Schaaf, F. Gao, A. H. Strahler - Boston University)

MOD43B3


Indian subcontinent during monsoon june 10 26 2002

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

Spatially Complete Spectral Albedo Maps(E. G. Moody, M. D. King, S. Platnick, C. B. Schaaf, F. Gao – GSFC, BU)


Spectral albedo of snow

Spectral Albedo of Snow

  • 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)


Albedo by igbp ecosystem northern hemisphere multiyear average 2000 2004

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

???

urban

cropland

???


Surface temperature skin temperature

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 temperature1

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 temperature2

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 temperature3

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 is

    Tskin = 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

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

MODIS SST


Accuracy of retrieved tskin

Accuracy of Retrieved Tskin

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

  • SST is more accurate than LST (land skin temperature)

  • 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.


Remote sensing on land surface properties

Lambertian surface properties


Remote sensing on land surface properties

MODIS 2000-2007 averaged monthly Tskin


Remote sensing on land surface properties

  • Modis land cover.

  • 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 Albedo


Land tskin vs water vapor

Land Tskin vs. Water Vapor


Remote sensing on land surface properties

snow

clouds

desert

sea

Band 4

(0.56 µm)

Transects of Reflectance

Band 1

Band 4

Band 3


Remote sensing on land surface properties

Band 26

1.38 micron

Strong H20


Remote sensing on land surface properties

1.38 μm

Only High Clouds

Are Visible


Remote sensing on land surface properties

Band 26

1.38 µm


Remote sensing on land surface properties

Visible

(Reflective Bands)

Infrared

(Emissive Bands)


Remote sensing on land surface properties

Visible

(Reflective Bands)

Infrared

(Emissive Bands)


Emissive bands

Emissive Bands

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

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

  • Only 1% is observed below 4 µm

  • At 4 µm the solar reflected energy can significantly affect the observations of the Earth emitted energy


Remote sensing on land surface properties

Spectral Characteristics of

Energy Sources and Sensing Systems

IR

4 µm

11 µm


Nir and vis over vegetation and ocean

NIR and VIS over Vegetation and Ocean

Vegetation

Ocean

NIR (0.86 µm)

Green (0.55 µm)

Red (0.67 µm)

RGB

NIR


Remote sensing on land surface properties

Spectral Characteristics of

Energy Sources and Sensing Systems

NIR

IR


Remote sensing on land surface properties

Radiation is governed by Planck’s Law

In wavelength:

B(,T) = c1/{ 5 [e c2/T -1] }(mW/m2/ster/cm)

where = wavelength (cm)

T = temperature of emitting surface (K)

c1 = 1.191044 x 10-8 (W/m2/ster/cm-4)

c2 = 1.438769 (cm K)

In wavenumber:

B(,T) = c13 / [e c2/T -1] (mW/m2/ster/cm-1)

where = # wavelengths in one centimeter (cm-1)

T = temperature of emitting surface (K)

c1 = 1.191044 x 10-5 (mW/m2/ster/cm-4)

c2 = 1.438769 (cm K)


Remote sensing on land surface properties

B(max,T)~T5

B(max,T)~T3

max ≠(1/max)

B(,T) versus B(,T)

B(,T)

B(,T)

6.6

3.3

100

20

10

5

4

wavelength [µm]


Remote sensing on land surface properties

wavelength  : distance between peaks (µm)

Slide 4

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

=1/ 

d=-1/ 2 d 


Remote sensing on land surface properties

Using wavenumbers

Wien's Law dB(max,T) / dT = 0 where (max) = 1.95T

indicates peak of Planck function curve shifts to shorter wavelengths (greater wavenumbers) with temperature increase. Note B(max,T) ~ T**3.

Stefan-Boltzmann Law E =  B(,T) d = T4, where  = 5.67 x 10-8 W/m2/deg4.

0

states that irradiance of a black body (area under Planck curve) is proportional to T4 .

Brightness Temperature

c13

T = c2/[ln(______ + 1)] is determined by inverting Planck functionB

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


Planck function and modis bands

MODIS

Planck Function and MODIS Bands


Modis band 20

MODIS BAND 20

  • Window Channel:

  • little atmospheric absorption

  • surface features clearly visible

Clouds are cold


Modis band 31

MODIS BAND 31

  • Window Channel:

  • little atmospheric absorption

  • surface features clearly visible

Clouds are cold


Remote sensing on land surface properties

Clouds at 11 µm look

bigger than at 4 µm


Temperature sensitivity

Temperature sensitivity

dB/B =  dT/T

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

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

 = c2/T


Planck s function review lecture 1

Planck’s function (review lecture 1 )

First radiation constant

Wavelength of radiation

c1-5

B  (T) =

exp (c2 / T ) -1

Absolute temperature

Second radiation constant

Irridance:

Blackbody radiative flux

for a single wavelength at temperature T (W m-2)

Total amount of radiation emitted by a blackbody is a function of

its temperature

c1 = 3.74x10-16 W m-2

c2 = 1.44x10-2 m °K

Class Participation: Why this is different from the Planck Function on page 55?


Remote sensing on land surface properties

∆B11> ∆B4

∆B11

T=300 K

Tref=220 K

∆B4


Remote sensing on land surface properties

∆B4/B4= 4∆T/T

∆B11/B11 = 11∆T/T

∆B4/B4>∆B11/B11  4 > 11

(values in plot are referred to wavelength)


Remote sensing on land surface properties

(Approximation of) B as f

unction of  and T

∆B/B= ∆T/T

Integrating the Temperature

Sensitivity Equation

Between Tref and T (Bref and B):

B=Bref(T/Tref)

Where =c2/T (in wavenumber space)


B b ref t t ref b b ref t ref t b t

B=Bref(T/Tref)B=(Bref/ Tref) T B T 

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

 = c2/T = c2/T.

_________________________________________________________

WavenumberTypical SceneTemperature TemperatureSensitivity

9003004.32

250030011.99


Non homogeneous fov

N

Tcold=220 K

1-N

Thot=300 K

Non-Homogeneous FOV

B=NB(Thot)+(1-N)B(Tcold)

BT=NBThot+(1-N)BTcold


Remote sensing on land surface properties

N

1-N

Tcold

Thot

For NON-UNIFORM FOVs:

Bobs=NBcold+(1-N)Bhot (1)

Bobs=N Bref(Tcold/Tref)+ (1-N) Bref(Thot/Tref) (2)

Bobs= Bref(1/Tref) (N Tcold + (1-N)Thot) (3)

For N=.5

Bobs= .5Bref(1/Tref) ( Tcold + Thot)(4)

Bobs= .5Bref(1/TrefTcold) (1+ (Thot/ Tcold))(5)

The greater  the more predominant the hot term

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


Consequences cloud fire detection

Consequences: Cloud & Fire Detection

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

  • In presence of fires the difference BT4-BT11 is larger than the solar contribution

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


Remote sensing on land surface properties

MODIS clouds algorithm (As an example)

Band 29 (8.6 m)

Band 31 (11 m

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 and

Band 29 – Band 31 difference < -1.0 K

OR:Or

Band 31 (11 m) Brightness Temperature > 285 K and

Band 29 – Band 31 difference < -0.5 K


Conclusions vegetation detection

Conclusions: Vegetation Detection

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

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

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


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