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
•   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)

                                6 Closed Shrublands
0 Water                                                 12 Croplands
                                7 Open Shrublands
1 Evergreen Needleleaf Forest                           13 Urban and Built-Up
                                8 Woody Savannas
2 Evergreen Broadleaf Forest                            14 Cropland/Natural Veg. Mosaic
                                9 Savannas
3 Deciduous Needleleaf Forest                           15 Snow and Ice
                                10 Grasslands           16 Barren or Sparsely Vegetated
4 Deciduous Broadleaf Forest
                                11 Permanent Wetlands   17 Tundra
5 Mixed Forests
• 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
      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
 – Band 20 (3.7 m) – thermal emission plus solar
 – Band 31 (11 m) – clouds colder than rest of scene
-- Band 35 (13.9 m) – only upper atmospheric thermal
emission detected
                  Vegetation: NDVI
  The NDVI is calculated from these individual measurements as follows:

               NDVI =

NDVI –Normalized Difference Vegetation Index

• Subsequent work has shown that the
  NDVI is directly related to the
  photosynthetic capacity and hence energy
  absorption of plant canopies.
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).
• 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
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
• 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
                           Red Orange              Green                          Violet
                                         Yellow                         Blue
                         (0.7m) (0.6m)          (0.5m)                        (0.4m)

                                                     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 albedo needed for retrievals over land
• Spatially complete surface albedo datasets have been generated
   – Uses high-quality operational MODIS surface albedo dataset
   – Imposes phenological curve and ecosystem-dependent
   – White- and black-sky albedos produced for 7 spectral bands
     and 3 broadbands
• See for data access and
  further descriptions
 Conditioned Spectral Albedo Maps
(C. B. Schaaf, F. Gao, A. H. Strahler
        - Boston University)

Indian Subcontinent during Monsoon
         June 10-26, 2002
 Spatially Complete Spectral Albedo Maps
(E. G. Moody, M. D. King, S. Platnick, C. B.
       Schaaf, F. Gao – GSFC, BU)
            Spectral Albedo of Snow
 Used near real-time ice and snow extent (NISE) dataset
   – Distinguishes land snow and sea ice (away from coastal
   – 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)


     Surface Temperature: Skin
• 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
• 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
• Split Window Algorith
•     Retrieving Tskin using the two channels (i.e., SWT)
    was first proposed in the 1970s (Anding and Kauth
    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
    Surface Temperature: Skin
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
• 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
• SST ≈ B31 + (B31 – B32) (just this simple!)
 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

       Land surface temperature



Modis land cover. 1     2      3   4     5     6   7     8   9      10   11   12   13   14   15   16
1. Evergreen Needleleaf Forest;                        Land cover
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
             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
NIR and VIS over Vegetation and Ocean
                      


                  

                                                            
                                              Planck Radiances                        


                                                                                  
                       140       
mW/m2/ster/cm (cm-1)



                       60                                        


                             0       5   10             15               20   25       30
                                              wavenumber (in hundreds)


           

              
                            

                                

Planck Function and MODIS Bands
         Temperature sensitivity

                dB/B =  dT/T

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

Substituting the Planck Expression, the
  equation can be solved in :
                     = c2/T
Planck’s function (review lecture 1 )

                First radiation constant       Wavelength of radiation

                   B  (T) =
                             exp (c2 / T ) -1
                                                      Absolute temperature
 Irridance:                   Second radiation constant
 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


           



       
                    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.

      Wavenumber          Typical Scene         Temperature
                          Temperature            Sensitivity

          900                   300                  4.32
          2500                  300                 11.99
Non-Homogeneous FOV
                                   

                              

                          

                               


                                       
   Consequences: Cloud & Fire
• 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
                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
• 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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