Remote Sensing on land Surface Properties

1
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
2
outline

• Reflectance and albedo
• Vegetation retrieval
• Surface temperature retrieval
• A quick look at clouds and fire retrieval

3
MODIS Land Cover Classification(M. A. Friedl, A.
H. Strahler et al. Boston University)
Moody, E. G., M. D. King, S., Platnick, C. B.
Schaaf, and F. Gao, 2005 Spatially complete
global spectral surface albedos Value-added
datasets derived from Terra MODIS land products.
IEEE Trans. Geosci. Remote Sens., 43, 144158.
0 Water
6 Closed Shrublands
12 Croplands
1 Evergreen Needleleaf Forest
7 Open Shrublands
13 Urban and Built-Up
2 Evergreen Broadleaf Forest
8 Woody Savannas
14 Cropland/Natural Veg. Mosaic
3 Deciduous Needleleaf Forest
9 Savannas
15 Snow and Ice
4 Deciduous Broadleaf Forest
10 Grasslands
16 Barren or Sparsely Vegetated
5 Mixed Forests
11 Permanent Wetlands
17 Tundra
4
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

5
Electromagnetic spectrum
Red (0.7?m)
Orange (0.6?m)
Yellow
Green (0.5?m)
Blue
Violet (0.4?m)
Visible
Ultraviolet (UV)
Gamma
X rays
Infrared (IR)
Microwave
Radio waves
0.001?m
1?m
1000 ?m
1m
1000m
Longer waves
Shorter waves
1,000,000 ?m 1m
6
(No Transcript)
7
Soil
Vegetation
Snow
Ocean
8
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

9
Planck Function and MODIS Bands
10
MODIS BAND 1 (RED)
Low reflectance in Vegetated areas
Higher reflectance in Non-vegetated land areas
11
MODIS BAND 2 (NIR)
Higher reflectance in Vegetated areas
Lower reflectance in Non-vegetated land areas
12
(No Transcript)
13
(No Transcript)
14
(No Transcript)
15
Vegetation NDVI
The NDVI is calculated from these individual
measurements as follows
NIR-RED
NDVI
NIRRED

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

NDVI Normalized Difference Vegetation Index
16
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).
17
(No Transcript)
18
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.

19
NDVI represents greenness
20
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
21
Enhanced Vegetation Index (EVI)

• In December 1999, NASA launched the Terra
  spacecraft, the flagship in the agencys 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

22
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.gov for data access and
  further descriptions

23
Conditioned Spectral Albedo Maps(C. B. Schaaf,
F. Gao, A. H. Strahler - Boston University)
MOD43B3
24
Indian Subcontinent during MonsoonJune 10-26,
2002
25
Spatially Complete Spectral Albedo Maps(E. G.
Moody, M. D. King, S. Platnick, C. B. Schaaf, F.
Gao GSFC, BU)
26
Albedo by IGBP EcosystemNorthern Hemisphere
Multiyear Average (2000-2004)
???
urban
cropland
???
27
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 gt90 is flagged as either wet or dry
  snow in any 16-day period
• Hemispherical multiyear statistics
• Separate spectral albedo by ecosystem (MOD12Q1)

28
(No Transcript)
29
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)

30
Surface Temperature Skin Temperature (Tskin)

• 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!!
31
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

32
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

33
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!)

34
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.

35
METR180. Land Skin Temperature Remote Sensing

• Class discussion

36
The important effects of____________,
___________and ___________________ are
considered and the current practice for
removing these effects is specified.
37
The important effects of the atmosphere, surface
emissivity and instrument noise are considered
and the current practice for removing these
effects is specified.
38
What are the differences between Tskin (LST) and
2m surface air temperature?
Satellite sensor
39
Why do we need to use Tskin in land surface model
development
1. Problems of conventional surface temperature
observations measured at 2m above surface from
WMO weather stations,
Insufficient spatial coverage Sites are
irregularly distributed, Political boundary ..
2. Advances of satellite observations Global
coverage High resolution High quality ..
Jin et al. 1997 J. of Climate
40
Since the AVHRR has been used operationally with
some success to derive sea surface temperature
(SST) it is natural to attempt to use the data
over the land to derive land surface temperature
41
How to Use Satellite Data?

• good science is about identifying a good
• question and designing doable approach to solve
  the question, not about getting data and plotting
  them. This makes the
• difference between a scintist and a technician.
• -Robert E. Dickinson

42
http//gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cg
i?instance_idneespi

• Global Tskin from Terra for January 2009, July
  2009 where are the
• hottest regions?
• Time series of Tskin from Terra MODIS from July
  2000 to December 2009 over
• 10-20N, 20-40W (Sahara Desert)
• When the maximum occurred? How much was
  the maximum?
• When was the minimum? How much was the minimum?
• 3. Using daily data, examing Daytime andnighttime
  Tskin for
• July 2009
• SF
• Sahara
• Greenland
• 0, 20W (tropical forest)
• 4. Select both NDVI and Tskin, on monthly Terra
  MODIS,
• calculate the correlation coefficient over Tibet