Transcript 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
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Reflectance and albedo
Vegetation retrieval
Surface temperature retrieval
A quick look at clouds and fire retrieval
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, 144–158.
0 Water
1 Evergreen Needleleaf Forest
2 Evergreen Broadleaf Forest
3 Deciduous Needleleaf Forest
4 Deciduous Broadleaf Forest
5 Mixed Forests
6 Closed Shrublands
7 Open Shrublands
8 Woody Savannas
9 Savannas
12 Croplands
13 Urban and Built-Up
14 Cropland/Natural Veg. Mosaic
10 Grasslands
15 Snow and Ice
16 Barren or Sparsely Vegetated
11 Permanent Wetlands
17 Tundra
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
Electromagnetic spectrum
Radio waves
1000m
Microwave
1m
Longer waves
Infrared (IR)
1000 m
1m
Ultraviolet (UV)
Red Orange
Green
Yellow
(0.7m) (0.6m)
(0.5m)
Visible
Blue
X rays
0.001m
Shorter waves
1,000,000 m = 1m
Violet
(0.4m)
Gamma
Soil
Vegetation
Snow
Ocean
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
Planck Function and MODIS Bands
MODIS
MODIS BAND 1 (RED)
Low reflectance in
Vegetated areas
Higher reflectance in
Non-vegetated land areas
MODIS BAND 2 (NIR)
Higher reflectance in
Vegetated areas
Lower reflectance in
Non-vegetated land areas
RED
NIR
Dense Vegetation
Barren Soil
Vegetation: NDVI
The NDVI is calculated from these individual measurements as follows:
NDVI =
NIR-RED
NIR+RED
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).
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
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
Conditioned Spectral Albedo Maps
(C. B. Schaaf, F. Gao, A. H. Strahler
- Boston University)
MOD43B3
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)
Albedo by IGBP Ecosystem
Northern Hemisphere Multiyear Average (2000-2004)
???
urban
cropland
???
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)
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 (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!!
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 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
temperatures:
• 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).
• 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.
METR180. Land Skin
Temperature Remote Sensing
Class discussion
The important effects
of____________, ___________and
___________________ are
considered and the current practice
for removing these effects is specified.
The important effects
of the atmosphere, surface emissivity and
instrument noise are
considered and the current practice
for removing these effects is specified.
What are the differences between Tskin (LST)
and 2m surface air temperature?
Satellite sensor
LW ↑ =εσΤskin4
LW↑
Tair at 2m
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
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…
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
http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=neespi
1. Global Tskin from Terra for January 2009, July 2009 – where are the
hottest regions?
1. 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