Green, Robert O.

Hook, Simon J.

Hulley, Glynn C.

Quattrochi, Dale A.

Roberts, Dar A.

NASA Technical Reports Server

Synergies between VSWIR and TIR data for 
the urban environment: An evaluation of the 
potential for the Hyperspectral Infrared 
Imager (HyspIRI)  
• Dar A. Roberts1, Dale A. Quattrochi2, Glynn C. Hulley3, 
Simon J. Hook3, and Robert O. Green3 
• 1 UC Santa Barbara Dept. of Geography 
• 2 NASA Marshall Space Flight Center 
• 3 NASA Jet Propulsion Laboratory 
 
 
 
 
 
HyspIRI  Symposium – NASA/GSFC – May 16-17, 2012 
 
 
 
 
Acknowledgements: NASA HyspIRI Preparatory Program 
https://ntrs.nasa.gov/search.jsp?R=20120014551 2019-08-30T22:08:57+00:00Z

Outline 
• Why Urban? 
• Potential HyspIRI Synergies 
• Santa Barbara Study Design 
• Research Results 
• What’s next? 
• Summary 
Why Urban? (Vol 1) 
• A majority of the human 
population lives in urban and 
sub-urban areas 
– How does urbanization and 
urban composition modify 
urban environments and 
human habitat?  
– How might this change in a 
warmer world? 
• 1995 Chicago Heat Wave (750 
heat related deaths) 
• 2003 European Heat Wave 
(>40,000, heat related Deaths) 
• 2010 Great Russian Heat Wave 
(11,000 heat related deaths in 
Moscow) 
 
Image: 
http://en.wikipedia.org/wiki/File:Canicule_Europe_2003.jpg 
Why Urban? (Vol 2) 
• Urban areas are major sources of airborne and 
waterborne pollutants and major sinks for 
materials and energy 
– How do the properties of urban environments modify 
the flow of water borne pollutants, urban water use and 
urban energy use? 
 
Image Source: http://www.guardian.co.uk/politics/2007/mar/06/greenpolitics.uknews 
Urban Heat Islands 
• Urban areas are warmer than natural areas 
– Low albedo surfaces 
– Lack of vegetation cover and shading 
• Urban areas can create their own weather 
1997 Day time TIR image of  
Atlanta Georgia 
Source: Quattrochi, “HyspIRI 
Thermal IR (TQ4) Science  
Questions ….” 
Image source: Atlas, May 1997 
Atlas: 15 channel system 
  6 TIR, 1 MIR,  8 VNIR-SWIR 
http://www.ghcc.msfc.nasa.gov/atlanta/ 
Potential HyspIRI Synergies 
• Improved Temperature Emissivity Separation 
– Water vapor largest error source 
– Replace regional column water vapor with VSWIR 
water vapor 
• Full characterization of surface energy properties 
– Surface albedo, vegetation cover, impervious cover, 
emissivity, Land Surface Temperature (LST) 
• Improved discrimination of materials 
• Multi-wavelength measures of plant stress 
– VSWIR biochemistry, physiology 
– TIR canopy temperature 
 
  
Figure 1: Example of land cover composition for the eastern US and the fractional average NDVI 
for classes represented in the grid cell for 2001. 
Exa le of land cover comp sition and fractional average NDVI  
MODIS albedo for late July, 2001 over Chicago for different land cover 
classes.  Urban areas (red) are less reflective than most vegetated 
surfaces in the photosynthetic region of the spectrum while they are 
most reflective at longer wavelengths. 
 
Albedo change for a freshly painted white elastomeric paint applied 
to an asphaltic roof membrane in New York City. This “do-it-yourself” 
method for raising urban rooftop albedo is the lowest-cost high-
albedo retrofit approach available (Gaffin et al, 2011). 
Study Site: Santa Barbara 
• Mixed urban-natural systems, ~ 150,000 people 
• AVIRIS-MASTER pair, June 19, 2008 
– 7.5 m AVIRIS, 15 m MASTER 
– Spatial degradation, 15 m AVIRIS, 60 m AVIRIS/MASTER 
 
Methods 
• AVIRIS Analysis 
– ACORN 5, applied to 7.5, 15 and 60 m radiance data 
• Reflectance, water vapor, liquid water, albedo (Modo 4.3 Irradiance) 
– Surface Composition 
• VIS Model (Vegetation, Impervious, Soil expanded to include NPV) 
• Multiple Endmember Spectral Mixture Analysis 
– Fused field and AVIRIS-derived spectral library (7.5 m) 
– Count-based Endmember Selection,  cover class (i.e., bark,  composite shingle, oak) 
– Multi-level fusion (2, 3, 4 em models, selected based on 0.007 RMS threshold) 
• Water screened by LST (< 297.15K) 
• MASTER Analysis 
– Temperature Emissivity Separation (TES) 
• Modtran 5.2 derived atmospheric correction 
• NCEP column water vapor, iteratively adjusted for water over Laguna Blanca 
• TES, using ASTER algorithm (Gillespie et al.) 
– Spectral emissivity, broad band emissivity, LST 
 
Land-Cover Analysis/Mixture Validation 
• Defined 14 dominant land-cover classes on Google 
Earth Imagery 
– Residential (high, medium, low), Commercial, Transportation, 
Roofs 
– Closed canopy forest, Forested park, Golf courses, Grass sports 
fields, Orchards, Annual crop 
– Bare soil, crop 
• Extracted GV, NPV, Impervious, Soil fractions 
and land surface energy properties 
• Developed Mixture Validation Data Sets from 
NAIP 
– 120x120 m polygons (ranges in surface fractions) 
– Large, mixed land-cover polygons 
Land-Cover Analysis: Examples 
Mixture Validation 
• Utilized 85 polygons 
– 64 land-cover 
– 21 designed  
• Calculated fractions 
from high-res imagery 
– Manually delineated 
polygons for GV, NPV, 
Soil and Impervious 
– Determination aided by 
AVIRIS Figure showing three validation polygons 
A: 44% NPV, 11.3% GV, 44.7% Soil 
B: 50.45%NPV, 1.5% GV, 48%Soil 
C: 4.8% NPV, 57.4% GV, 34.5% Soil, 3.3% Imp 
 
A 
B 
C 
Results 
AVIRIS-MASTER Products 
• Fine spatial scale variability in water 
vapor 
– Elevation gradients clear 
– AVIRIS 1.2 – 1.7 cm, MASTER 0.78 cm 
• Liquid water and emissivity positively 
correlated 
– Asphalt also high emissiivty 
• Albedo and LST poorly correlated 
  
Reflectance and Emissivity Spectra 
• Biotic Materials – Most Distinct in VSWIR (all unique) 
– NPV low emissivity in TIR (Differs by stature) 
• Abiotic Materials – Varies 
– AVIRIS: Painted roofs, red tile 
• Soils and some road surfaces are not distinct* 
– MASTER: Quartz beach sands, various roof types 
• Asphalt surfaces are near black bodies 
VSWIR-TIR improves 
discrimination of 
abiotic materials 
MESMA: 
Endmember 
Selections 
• Selected 41 non-water endmembers 
– 4 NPV, 8 GV, 9 soils, 20 Impervious 
• GV strictly AVIRIS, transportation field, 
roofs, soils, NPV mixed 
 VIS-NPV Fractions: 15 and 60 m 
• GV and NPV 
fractions scaled well 
between all spatial 
resolutions 
– 7.5, 15 and 60 m 
• Soil tended to be 
overmapped at the 
expense of 
Impervious at 
coarser scales 
Two error sources 
    Asphalt – soil 
    Red Tile Roof (so variable     
 requires 3 ems) 
a) GV b) NPV 
c) Impervious d) Soil 
y = 0.998x - 0.0947 
R² = 0.8085 
0 
0.2 
0.4 
0.6 
0.8 
1 
0 0.2 0.4 0.6 0.8 1 
N
A
IP
 F
ra
ct
io
n
s 
y = 1.05x + 0.041 
R² = 0.829 
0 
0.2 
0.4 
0.6 
0.8 
1 
0 0.2 0.4 0.6 0.8 1 
y = 1.443x + 0.11 
R² = 0.7184 
0 
0.2 
0.4 
0.6 
0.8 
1 
0 0.2 0.4 0.6 0.8 
N
A
IP
 F
ra
ct
io
n
s 
HyspIRI Fractions 
y = 0.690x - 0.068 
R² = 0.409 
0 
0.2 
0.4 
0.6 
0.8 
1 
0 0.2 0.4 0.6 0.8 1 
HyspIRI Fractions 
Fraction Validation: 60 m 
GV-NPV  Fractions 
good at HyspIRI scales 
Under- 
mapped 
Phenology 
Soil Overmapped 
 (red tile/Asphalt) 
Land-cover Composition 
• Cover fractions 
follow the expected 
pattern 
– High GV, low 
Temperature 
– High Impervious, 
high Temperature 
– Residential 
• Low density, high GV, 
Low T, low Imp 
• High density, low GV, 
High T, High Imp 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
NPV 
GV 
Soil 
Impervious 
F
ra
ct
io
n
s 
306K 325K 
y = -16.018x + 324.45
R
2
 = 0.679
295
300
305
310
315
320
325
330
335
0 0.5 1 1.5
GV Fraction
L
S
T
 (
K
)
Green Cover and LST 
• Standard inverse 
relationship between 
GV Cover and LST 
• Considerable scatter 
– The scatter is likely to 
be the most 
interesting part 
– Moist soils 
evapotranspiring 
– Closed canopies with 
variable ET 
What’s Next? 
• TES with AVIRIS derived column water 
vapor 
• Spatial variation in LST in closed 
canopies 
– VSWIR stress measures 
• Analysis of new AVIRIS-MASTER pair, 
2011 
– Greater areal coverage, including greater 
elevation range, Gap, Tea and Jesusita Fire 
scars and a wide diversity of natural 
vegetation 
• Seasonal VSWIR-TIR spectroscopy 
– JPL Perkin Elmer, ASD and Nicolet 
– 17 tree species, chlorophyll water 
– First measures late July 2011 
• Follow up, Nov/Dec 2011, Mar 2012, June 2012 Source: Mike Alonzo, Keely Roth 
Summary 
• Urban material Identification will be difficult with 
HyspIRI 
– Variation too fine scale 
• GV-NPV cover stable at multiple scales 
– Impervious can be improved 
• Improved Red Tile Roofs 
– Potential for VSWIR-TIR unmixing for abiotic 
materials 
• Improved soil-impervious discrimination 
• Energy patterns reasonable 
– GV-LST inversely correlated 
– Impervious-LST positively correlated 
– Considerable scatter – which is the interesting part 


Synergies Between VSWIR and TIR Data for the Urban Environment: An Evaluation of the Potential for the Hyperspectral Infrared Imager (HyspIRI)

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