Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
2. WHY TO CARE ABOUT VIS?
•
VIs are estimates of the APAR of plants hence
correlated with photosynthetic capacity (Sellers
1985, Myneni 1995)
•
•
•
Then
•
Better understanding of carbon cycle and sink
X
VI over time ~ GPP
Related to vegetation “greeness” and “health”
Allow time series analysis (growing season
analysis)
3. BIOPHYSICAL BASIS
•
VIs are a function of the
optical and physical
properties of leaves, canopy
structure, soil background,
and atmosphere load.
•
VIs are typically based on the
absorption of blue and red
light by chlorophyll, and
reflection of near-infrared
light by the leaves’s structure
4. CHALLENGES
•
Observing vegetation from
space has several challenges,
mainly due to atmospheric
and soil effects
•
Additional issues may be
introduced by aggregating
techniques (e.g. MVC) and
observation geometry (BRDF
effects)
5. VIS FORMULATION
•
Several forms of VIs have been proposed over
time. Original goals were to estimate vegetation
status and biomass (Kriegler 1969 , Miller 1972,
Rouse 1974, Tucker 1977). Now many applications
•
Each VI instance try to address a specific issue,
such as atmospheric or soil effects
•
Examples:
•
•
•
•
NDVI - Normalized Difference VI
ARVI - Atmospheric Resistant VI
SAVI - Soil Adjusted VI
EVI - Enhanced VI
N DV I =
N IR R
N IR + R
N IR RB
N IR + RB
RB = R
(B R)
ARV I =
EV I = G ·
N IR
N IR + C1 · R
R
C2 · B + 1
6. MODIS VI (MOD13)
•
The MODIS VI product (MOD13) is a chain of
VI products at varying spatial (250m, 1km, 0.05
degree) and temporal (16-day, monthly)
resolutions
•
It contains both NDVI and EVI indices:
NDVI as the continuity index (+30 yr data). Simple, wellknown, APAR-correlated. Shows “saturation” and
atmospheric and soil issues
•
•
•
EVI as an enhanced index, addressing some NDVI
concerns (although at some “costs”)
A 2-band EVI (no blue band) is used as backup
algorithm under anomalous areas
7. MODIS VI (MOD13)
Product Family
•MOD13Q1: 16 d, 250 m, tiled
•MOD13A1: 16 d, 500 m, tiled
•MOD13A2: 16 d, 1 km, tiled
•MOD13A3: monthly, 1 km, tiled
•MOD13C1: 16 d, 0.05 deg (CMG)
•MOD13C2: monthly, 0.05 deg (CMG)
MODIS Sinusoidal 10x10 deg tiles
Tile h09v05
8. MOD13 AGGREGATION
•
MODIS VI products rely on the upstream daily
Surface Reflectance (SR, MOD09) series
•
The VI algorithms temporally composite the SR to
generate the VI products.
•
The 1-km VI product first aggregates 250- and 500m pixel sizes to 1 km.
•
The CMG products are generated through spatial
averaging of the 1-km versions
•
Monthly products are time-weighed averages of
their 16-day versions
10. MOD13 AGG ALGORITHM
•
MODIS VI algorithm applies a filter to the data
based on quality, cloud, and viewing geometry
•
Contaminated and extreme off-nadir data are
considered lower quality. A cloud-free, nadir view
pixel with no residual atmospheric contamination
represents the best quality pixel.
•
Compositing VI technique uses a Constrained View
angle - Maximum Value Composite (CV-MVC)
•
It uses a simple MVC when no enough data exist
19. MOD13 MONTHLY ALGORITHM
•
This algorithm uses all 16-day VI products which
overlap within a calendar month.
•
Once all 16-day composites are collected, a weighing factor based on the degree of temporal overlap
is applied to each input.
•
In assigning the pixel QA, a worst case scenario is
used, whereby the pixel with the lowest quality
determines the final pixel QA.
20. product ready for incorporation in MODIS Data Collection 5,
scheduled for processing in June 2005. The VI CMG is a
seamless 3600x7200 pixel data product with 12 layers, at
approximately 544MB per composite period. This is a higher
quality climate product useful in time series analyses of earth
surface processes. It incorporates a QA (quality analyses) filter
scheme that removes lower quality, cloud contaminated pixels in
aggregating the 1 km pixels into the 0.05 degree CMG product. It
also incorporates a data fill strategy, based on historic data
records, to produce a continuous and reliable product for ready
entry into biogeochemical, carbon, and growth models. With its
very manageable size, the VI CMG can be used for many
purposes, some of which are presented here.
MOD13 CMG (0.05 DEG)
ALGORITHM
•
•
•
The VI CMG series is a
seamless global 3600x7200
pixel data product with 13
SDS
It incorporates a QA filter
scheme that removes lower
quality pixels in aggregating
the 1-km pixels into the
0.05-deg CMG product
•
If all pixels are cloudy, it uses
historical time series values
Processing Flow
At most 36, 1km pixels
depending on latitude.
Inverse mapping /
projection of input data
to geographical
Inv Map/Projection
coordinates.
Reflectances averaged
VIs recomputed
Dominant QA
Standard deviations
QA Filter
Spatial Calc.
>1 high quality pixel
retained from QA filter
Uses only clean pixels to
compute final value
Filters pixels that are
cloudy, mixed clouds,
fill, or missing in input.
“Climatology Record”
23 Avg. Composites
One clean average year
Complete CMG
Pixel at 0.05 deg
MODISFigure 5: MOD13 CMG Processing flow.
VI CMG Data Layers
21. MODIS VI PRODUCTION SCHEME
•
To ensure the best quality, the MODIS program
is periodically updated to integrate the latests
advances and user needs.
•
Upgrades are released as “Collections”. Current
is Collection 5 (C5), based on a 16-day
composite period (1 image each 16 days)
•
Terra and Aqua are interleaved, then a combined
8-day product is possible. Considerations
required.
22. MODIS VI C5 MAIN CHANGES
•
Improvement of the Constrained View angle - Maximum Value
Composite (CV - MVC) compositing method
•
•
CV-MVC was modified to favor smaller view angles.
MVC is used when all input days are cloudy
•
•
Update of the EVI backup algorithm from SAVI to a 2–band EVI
•
Added the 0.05 deg CMG product series
Added Composite day of the year and Pixel reliability output
parameters
23. MODIS VI C5 APPLICATIONS
Left: NDVI anomaly (Z units) for a given three-month period, computed from the
aggregate1-km 16-day MOD13 product (9 yr); Right: corresponding aerosol load
extracted from the companion QA metadata
25. MODIS VI C6 CONSIDERATIONS
•
Upgrades for next C6 include increasing time
resolution to 8 days
•
Also desired is using a standard SR parameter (derived
from the Atmosphere section) for VI
•
More studies on BRDF effects and the CV-MVC
aggregating approach
•
Impacts of these changes on the performance of the
MODIS VI product for C6 are unknown.
26. MODIS VI C6 STUDIES
•
We developed test data for studying proposed
changes impacts
•
A revised aggregating algorithm to reduce BRDF
effects was proposed (reducing large VZ angles)
27. MODIS VI C6 STUDIES WHY BRDF REVISITING?
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F IGURE B.5: VIs computed from the MODIS ASRVN data set. Left panel shows the VIs
VIs computed from the angle-corrected from
derived from the actual MODIS geometry. Right panel shows the VIs computed(zenith-the
normalized) ASRVN subset. Data correspond to the
angle-corrected (zenith-normalized) ASRVN subset. Data correspond to the241-256 site,
GSFC validation site, dates 2002 GSFC
dates 2002 241-256
28. positive angles (forward scattering direction). The NIR band exhibited the largest bias,
MODIS VI C6 STUDIES lastly the red band (0.0232 to 0.0418). In relative units, the NIR varied?
WHY BRDF REVISITING ±19% from the
with values ranging from 0.2511 to 0.3599, followed by the blue band (0 to 0.0374) and
estimated unbiased (zenith) value, while the blue and red bands diverged ±77% and ±29%
Should be constant independently
of the view angle!
respectively.
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F IGURE reflectance bands from the MODIS AS
F IGURE 2.4. Effective BRDF effects onDifferential effects on B.5. VIs computed(left figure)
BRDF effects on daily MODIS. individual MODISVI bands. Large impact
and corresponding sun zenith angles (right figure),derived from the2002ASRVN forgeometry. Right p
as derived from actual MODIS actual
on VIs. Data correspond to the GSFC validation site, dates the 241-256
angle-corrected (zenith-normalized) ASRVN sub
MODIS geometry. Data correspond to the GSFC site, dates 2002 241-256.
dates 2002 241-256
29. 0
2000
127
-60
-40
MODIS VI C6 STUDIES BRDF RESULTS
-20
0
20
40
60
20
40
60
6000
4000
0
2000
C6 NDVI (x10k)
8000
vz (deg)
60
-60
-40
-20
0
vz (deg)
F IGURE B.8: Distribution of NDVI vs. acquisition angle for two different MODIS tiles.
Distribution of NDVI vs. acquisition angle for an Amazonian MODIS tile (h12v09).
Upper panelsLeft: NDVI C5; to NDVI C5 andC6. Both tiles correspond to NDVI C6. Left column
correspond Right: proxy NDVI bottom panels to proxy date 2002-177.
correspond to tile h10v05 and right column correspond to tile h12v09. Both tiles correspond
to compositing date 2002-177.
30. 0
2000
127
-60
-40
MODIS VI C6 STUDIES BRDF RESULTS
-20
0
20
40
60
20
40
60
6000
4000
0
2000
C6 NDVI (x10k)
8000
vz (deg)
60
-60
-40
-20
0
vz (deg)
F IGURE B.8: Distribution of NDVI vs. acquisition angle for two different MODIS tiles.
Distribution of NDVI vs. acquisition angle for an Amazonian MODIS tile (h12v09).
Upper panelsLeft: NDVI C5; to NDVI C5 andC6. Both tiles correspond to NDVI C6. Left column
correspond Right: proxy NDVI bottom panels to proxy date 2002-177.
correspond to tile h10v05 and right column correspond to tile h12v09. Both tiles correspond
to compositing date 2002-177.
31. 2000
125
0
MODIS VI C6 STUDIES BRDF RESULTS
-60
-40
-20
0
20
40
60
20
40
60
-40
-20
0
20
40
8000
4000
2000
-40
-20
0
-60
-4
vz (deg)
6000
8000
F IGURE B.6: Distribution of EVI vs. acquisition angle f
Upper angle for MODIS tile h10v05.
Distribution of EVI vs. acquisition panels show EVI C5 and bottom panels show proxy
tile h10v05, located in 2002-177.
Left: EVI C5; Right: proxy EVI C6. Compositing date isSE US, and right column shows ti
Amazonia. Both tiles correspond to compositing date 2002x10k)
8000
0
-60
60
vz (deg)
6000
C6 EVI (x10k)
2000
0
0
-60
x10k)
6000
8000
6000
4000
C6 EVI (x10k)
6000
4000
2000
C5 EVI (x10k)
8000
vz (deg)
32. 2000
125
0
MODIS VI C6 STUDIES BRDF RESULTS
-60
-40
-20
0
20
40
60
20
40
60
-40
-20
0
20
40
8000
4000
2000
-40
-20
0
-60
-4
vz (deg)
6000
8000
F IGURE B.6: Distribution of EVI vs. acquisition angle f
Upper angle for MODIS tile h10v05.
Distribution of EVI vs. acquisition panels show EVI C5 and bottom panels show proxy
tile h10v05, located in 2002-177.
Left: EVI C5; Right: proxy EVI C6. Compositing date isSE US, and right column shows ti
Amazonia. Both tiles correspond to compositing date 2002x10k)
8000
0
-60
60
vz (deg)
6000
C6 EVI (x10k)
2000
0
0
-60
x10k)
6000
8000
6000
4000
C6 EVI (x10k)
6000
4000
2000
C5 EVI (x10k)
8000
vz (deg)
33. MODIS VI C6 STUDIES FLUX GPP CORRELATION
10
16-day “C6”
40
50
16-day C5
R2 GPP vs EVI
0.70
0.65
4
8-day “C6”
1500
2000
2500
3000
3500
4000
0.55
0
GPP vs EVI C5
fit line
R2 EVI C5 (16d)
R2 EVI C6 (8d)
R2 EVI C6 (16d)
0.60
2
GPP (gC m−2d−1)
0.75
6
0.80
8
0.85
0
MODIS footprint (km2)
20
30
4500
EVI C5 (x10k)
F IGURE A.15. As in Figure A.13, but showing a better correlation for proxy 16-day C6
relative 16-day C5. Data Correlation - Kruger National Park, ZA (ZA-Kru) site.
from Skukuzq recovers for 16-day “C6” re-
composites
34. values, meaning that the growing season was detected a little sooner in C6 than in C5. The
MODIS VI C6 STUDIES SEASONALITY EFFECTS
length of the growing season consequently showed reduced values for the majority of sites,
indicating slightly smaller season lengths for C6 than for C5. The EVI values at the peak of
●
●
●
●
Start
End
Length
Peak time
EVI (10k−scaled)
−600 −400 −200
−0.5
0
0.0
0.5
200
●
●
−1.5
−1.0
16−day period
●
400
1.0
●
●
SOS = Start of Season
EOS = End of Season
LOS = Length of Season
600
1.5
the season were smaller for C6 than for C5 for the majority of sites (Figure A.26)
Peak val
Green season
F IGURE A.26. Distribution of mean difference values in seasonality parameters as derived
from MOD13 C5 and proxy MOD13 C6 (differences were computed as C6 - C5). Parameters
included are: date of start of green season (Start), end of season (End), length of season
Small increase in SOS
(Length), peak of season (Peak time), and EVI value at the peak of season (Peak val). Dates
Small Peak values
correspond to 16-day units as defined by the MOD13 compositing scheme. reduction on EOS and
LOS and on peak EVI
correspond to difference in 10k-scaled EVI units.
35. MODIS VI C6 STUDIES SOME CONCLUDING REMARKS
•
8-day “C6” EVI showed reduced EVI-GPP correlation.
Most likely cause is the reduction in the compositing
period (i.e. less probability of clear days).
•
16-day “C6” EVI algorithm restored EVI-GPP
correlation to C5 levels
•
Some impact on seasonality parameters: slight to
moderate reduction on EOS, LOS and peak EVI value
36. MODIS VI C6 STUDIES SOME CONCLUDING REMARKS
•
BRDF effects were evidenced on MODIS data using
daily ASRVN reference data
•
BRDF effects act different on individual B, R and NIR
bands, hence affecting VIs
•
EVI and NDVI are affected differently: EVI is more
biased and in the opposite direction than NDVI
•
BRDF effects are also present on the standard MODIS
VI product
•
Proxy C6 results showed reduced VZ angles than
current C5, but it is also affected. Lack of good data?
37. MODIS VI C6 STUDIES SOME CONCLUDING REMARKS
IMPORTANT
•
These studies were conducted using a proposed new
aggregating algorithm for further reducing BRDF
effects
•
Actual C6 algorithm to be implemented may be
different for a number of reasons