SPOT Resampling
•Spatial resolution for SPOT is 1.0 km and for MODIS is 5.6 km,thus in order to combine the data, they must have the sameresolution. First of all we have to inspect 6x6 pixels on SPOTimage, then filter the data and finally determine the average ofthe retained pixels (see the figure above). This procedure willachieve a 6 km pixel which is good enough to combine with5.6km pixel from MODIS.
VIS Estimation
•Vegetation indices (VI) are empirical measures thatquantities vegetation biomass of the vegetation at the landsurface. They often are function of the red and nearinfrared spectral functions.
•VIS Estimation: NDVI and EVI2 sds’s are estimated andadded to the downloaded data. In addition a Rank layer,describing the quality of the data, based on QA informationis added to each file.
•NDVI & EVI2: As a ratio, the NDVI has the advantage of minimizingcertain types of band-correlated noise (positively-correlated) andinfluences attributed to variations in direct/diffuse irradiance,clouds and cloud shadows, sun and view angles, topography, andatmospheric attenuation. On the other hand, EVI (EnhanceVegetation Index) was developed to minimize the atmosphericeffect by using the difference in blue and red reflectances as anestimator of the atmosphere influence level.
Long Term Average Estimation:
Long term data is necessary for estimate gaps in the timevegetation index series. The long term average can beused for either constrain the data for the year of interestand/or fill the gap for a particular day using the averagefor that day. It has been calculated for different periodsand situations. In order to determine the long termaverage, the AVHRR data were divided in:
•5-year (1980-1985, 1985-1990, 1990-1995, 1995-2000, 1983-1988,1988-1993, and 1993-1998)
•10-years (1980-1990, and 1990-2000) and 20-years (1980-2000).
The MODIS data were divided in
•5-years (2000-2005 and 2005-2010) and
•10-year (2000-2010).
Spectral Transformation Equations to MODIS-equivalents (TOC, CMG)
By Tomoaki Miura and Javzan Tsend-Ayush
NDVI (x variable)
Equation
Uncertainty(95% PI)
N-7 AVHRR, ROW, GAC
y = -0.0646111 + 1.2409713x - 0.0304219x2
±0.0138
N-9 AVHRR, ROW, GAC
y = -0.0621082 + 1.2487272x - 0.0307315x2
±0.0138
N-11 AVHRR, ROW, GAC
y = -0.0606805 + 1.2456808x - 0.0335204x2
±0.0138
N-14 AVHRR, ROW, GAC
y = -0.0571829 + 1.2372178x
±0.0138
S-4 VEGETATION, TOC, CMGV
y = 0.0156834 + 1.0610148x
±0.061
EVI2 (x variable)
Equation
Uncertainty(95% PI)
N-7 AVHRR, ROW, GAC
y = -0.0403338 + 1.2400319x
±0.088
N-9 AVHRR, ROW, GAC
y = -0.0403338 + 1.2400319x
±0.088
N-11 AVHRR, ROW, GAC
y = -0.0403338 + 1.2400319x
±0.088
N-14 AVHRR, ROW, GAC
y = -0.0403338 + 1.2400319x
±0.088
S-4 VEGETATION, TOC, CMGV
y = 0.0085842 + 1.1557716x
±0.037
Top-down, Direct Image Comparison ( for LTDR v.3)
Spectral Transformation Equations to MODIS-equivalents (TOC, CMG)
By Tomoaki Miura and Javzan Tsend-Ayush
NDVI (x variable)
Equation
Uncertainty(95% PI)
N-7 AVHRR, ROW, GAC
y = 0.0105080 + 1.1144501x
±0.033
N-9 AVHRR, ROW, GAC
y = 0.0127476 + 1.1215841x
±0.032
N-11 AVHRR, ROW, GAC
y = 0.0143102 + 1.1167148x
±0.032
N-14 AVHRR, ROW, GAC
y = 0.0143951 + 1.1336442x
±0.030
S-4 VEGETATION, TOC, CMGV
y = 0.0381324 + 1.0064999x
±0.013
EVI2 (x variable)
Equation
Uncertainty(95% PI)
N-7 AVHRR, ROW, GAC
y = -0.000084 + 1.2339542x
±0.023
N-9 AVHRR, ROW, GAC
y = 0.0023720 + 1.2298151x
±0.022
N-11 AVHRR, ROW, GAC
y = 0.0033594 + 1.2256970x
±0.022
N-14 AVHRR, ROW, GAC
y = 0.0044528 + 1.2244740x
±0.022
S-4 VEGETATION, TOC, CMGV
y = 0.0232545 + 1.0324644x
±0.006
Bottom-up, Hyperspectral Analysis
GAP Filling
Gaps are filled using
1.Linear Interpolation
2.Inverse Distance Weighting.
3. Values are constrained by the long term averagemoving window of 5 years. One standard deviation isused to restrict the boundaries of the values. Valuesoutside of boundaries are replace with a long termaverage value and labeled within the Rank sds.
VIi is the vegetation index value of the known points
dij is the distance to the known point
VIj is the vegetation index value of the unknown point
n is a power parameter , user selects the exponent (often1, 2 or 3)
Compositing
•Compositing is a procedures to improve the quality of landproducts. It combines multiple daily images to generate a singlecloud and problem free image over a predefined temporal intervals.This method reduces the noise due to the clouds and atmosphericconstituents [Jonsson et. al. 2004]. The compositing can be the firstfilter to get a better and more accurate time series data. One typeof composting is the maximum value composite (MVC). MVCcompares all the images taken by a satellite, such as MODIS, duringa pre-defined period of time and selects the pixels with the highestvegetation index value since it is assume that contaminationreduces the VI values [Viovy et. al. 1992].
•Daily data is used to generate composed images. A 15-days andMonthly datasets are generated. Each one based on the followingapproaches
a) CV-MVC (Constrain View-Maximum Value Compositing): itminimizes the off-nadir tendencies of MVC.
b) Average of All values
c) Average of N max values
Phenology
•Vegetation phenology can be defined as theplants study of the biological cycle eventsthroughout the year and the seasonal andinterannual response by climate variations.Phenology products, produced daily or on 16-daycompositing period, provided differentparameters which describe the seasonal behaviorof the vegetation.
•Phenology metrics (Start of the Season, End ofSeason, Length of Season, Peak of the Season,etc) are estimated for every dataset.
AVHRR missing days
Year
Missing Days
1981
177, 178, 182-201
1982
22, 88, 104-107, 114, 119-121, 187, 202, 237, 268, 269
1983
218
1984
14, 15, 51, 53, 62, 82, 100, 107, 205, 341, 342
1985
1, 2, 18, 19, 39, 40, 41, 42, 70, 310
1986
38, 73, 74
1987
1988
4, 72, 73, 81, 90, 135, 136, 170, 197-199, 206-208, 235, 262, 281, 313-315, 335
1989
80, 81, 96
1990
1, 3, 59, 201-205, 210-213, 307, 321
1991
1-4, 10-14, 41-43
1992
50
1993
90, 257-365
1994
11
1995
1996
121-128
1997
285-365
1998
1999
1, 287, 288
