Pushing MODIS to the edge: high-resolution applications of moderate-resolution data
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Pushing MODIS to the edge:High-resolution applications of moderate-resolution data
Dániel Kristóf – Róbert Pataki – András Kolesár – Tamás Kovács
MultiTemp 2013 | Banff, AB, Canada, 25-27 June 2013
Institute of Geodesy, Cartography and Remote Sensing
Budapest, Hungary
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Outline MODIS at a glance Beginning
Expectations and observations Particularities of MODIS dataNew preprocessing approach & first results
Current stateTime series construction and quality measuresResults achieved so far
Conclusions and outlook
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MODIS Moderate Resolution Imaging Spectroradiometer Onboard NASA’s Terra&Aqua satellites 36 spectral bands between 0.405 and 14.385 micrometers Wide field of view, daily (or even more frequent) coverage
with nominal nadir resolutions of 250, 500 and 1000 meters Sophisticated operational data processing (MODAPS) A large number of preprocessed data & scientific products
available (for free…), timeliness Well-published algorithms, systematic revision and
reprocessing (also backwards processing; Currently: „Collection 5”)
An archive continuous in time: (almost) gapless data since 1999!
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Band 2
Band 2, QC 4096(OK)
A user wants to detect the date of interventions on agricultural fields, size comparable to a 250-m MODIS pixel
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Beginning: initial motivation…
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Beginning: initial motivation… Aqua MODIS land
surface reflectance
Day x
Day
x+
1
Day
x+
1
Day x
Terra MODIS land surface reflectance
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• Terra & Aqua MODIS, the same day
Terra MODIS
Aqu
a M
OD
IS
Beginning: initial motivation…
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– Gridding artifacts: • Data stored in a predefined grid, resampled• Average overlap between observations and
their respective grid cells less than 30% [Tan et al., RSE 105(2006):98-114]
– Problems with the raster data model itself:• Fixed size and orientation of the cells although
the observation dimensions vary across the scene due to the wide field of view (+/- 55 degrees), orientation definitely not N-S
• The grid cells / pixels do not represent the area where the signal is originated from
Why…? MODIS particularities:
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8Close to nadir Close to swath edge
MODIS particularities
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Proposed solution• A possible solution would be the spatial and/or temporal
compositing and/or filtering, but: loss of resolution and information…
• Our approach: Change data representation– Calculate and store observation footprints in polygon format– Each polygon represents the respective observation footprint (“real
pixel”) sensed during image acquisition• Geolocation datasets (MOD/MYD03) contain all necessary
info to do this– Ground location, dimensions and orientation of each MODIS pixel
footprint can be determined from: Latitude, Longitude, Height, Sensor Zenith Angle, Sensor Azimuth Angle, Slant Range
– Geolocation accuracy: 50 m at 1 sigma at nadir– Swath images (MOD/MYD02) or „backsampled” Surface
Reflectance
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Proposed solution
What does itlook like?
1 km250 m1 km
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First results: correlation with SPOT data (NIR band)
MODIS at 1 km resolution MODIS at 250 m resolution
R2 = 0.4702 R2 = 0.6117
R2 = 0.7910 R2 = 0.7918
R2 = 0.4702 R2 = 0.6117
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First results• Possible application: one-step radiometric
normalization of high-resolution imagery by using same-day MODIS surface reflectance.
• What next? How to handle ever-changing observation geometries as a time series?
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TIME SERIES CONSTRUCTIONAnd then…?
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So we selected a study area in SE Hungary…
SPOT4 image, 06/08/2003
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So we selected a study area in SE Hungary…
SPOT4 image NIR band, 06/08/2003
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…downloaded some MODIS geolocation files and calculated 250-m geometries…
MODIS geometry, 2003216_0945
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MODIS geometry, 2003217_0850
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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MODIS geometry, 2003217_1030
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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MODIS geometry, 2003218_0935
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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MODIS geometry, 2003219_1020
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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MODIS geometry, 2003220_0925
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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MODIS geometry, 2003221_1005
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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MODIS geometry, 2003222_0910
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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MODIS geometry, 2003223_0955
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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MODIS geometry, 2003224_0900
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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MODIS geometry, 2003224_1035
…downloaded some MODIS geolocation files and calculated 250-m geometries…
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…then simulated „pure” MODIS data as the zonal mean of SPOT NIR pixels…
MODIS footprints on SPOT background, 2003216_0945
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…then simulated „pure” MODIS data as the zonal mean of SPOT NIR pixels…
MODIS footprints with SPOT-derived values, 2003216_0945
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MODIS footprints on SPOT background, 2003217_0850
…then simulated „pure” MODIS data as the zonal mean of SPOT NIR pixels…
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MODIS footprints with SPOT-derived values, 2003217_0850
…then simulated „pure” MODIS data as the zonal mean of SPOT NIR pixels…
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THE GRIDDING PROCESSSetting the baseline:
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SPOT4 image NIR band, 06/08/2003
Simulated MODIS data was then resampled according to „simple gridding” (NN)
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SPOT4 image & predefined sinusoidal MODIS grid
Simulated MODIS data was then resampled according to „simple gridding” (NN)
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Simulated MODIS data was then resampled according to „simple gridding” (NN)
Simulated MODIS observations, geometry: 2003216_0945
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Simulated MODIS data was then resampled according to „simple gridding” (NN)
Simulated MODIS observations, geometry: 2003217_0850
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Zoom-in: grid cell vs. observation geometry
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Zoom-in: grid cell vs. observation geometry
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Zoom-in: grid cell vs. observation geometry
MODIS observation from 2003216_0945 and the corresponding grid cell
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Zoom-in: grid cell vs. observation geometry
MODIS observation from 2003217_0850 and the corresponding grid cell
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Zoom-in: grid cell vs. observation geometry
MODIS observation from 2003218_0935 and the corresponding grid cell
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Zoom-in: grid cell vs. observation geometry
MODIS observation from 2003219_1020 and the corresponding grid cell
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Zoom-in: grid cell vs. observation geometry
MODIS observation from 2003220_0925 and the corresponding grid cell
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Zoom-in: grid cell vs. observation geometry
MODIS observation from 2003221_1005 and the corresponding grid cell
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Zoom-in: grid cell vs. observation geometry
MODIS observation from 2003222_0910 and the corresponding grid cell
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Zoom-in: grid cell vs. observation geometry
MODIS observation from 2003223_0955 and the corresponding grid cell
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Zoom-in: grid cell vs. observation geometry
MODIS observation from 2003224_0900 and the corresponding grid cell
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Zoom-in: grid cell vs. observation geometry
All MODIS observation geometries corresponding to the selected grid cell
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SURFACE OBJECTS AS OBSERVATION UNITS
The other approach:
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Land surface objects of interest:observation units defined a priori
Crop map: 512 crop parcels delineated on HR data
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Land surface objects of interest:observation units defined a priori
Crop map: 512 crop parcels delineated on HR data
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Relevance („purity”) of each MODIS observation (~pixel) to each observation unit
Portion of observation belonging to the given parcel
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Relevance („purity”) of each MODIS observation (~pixel) to each observation unit
Portion of observation belonging to the given parcel
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Only „pure” observations (above threshold) are selected
Green: retained, Red: rejected
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Only „pure” observations (above threshold) are selected
Green: retained, Red: rejected
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Then, radiometric values are assigned to each observation unit (parcel) for each MODIS overpass
Calculation based on area-weigthed mean of retained pixels
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QUALITY ASSESSMENT
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0
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mean_spot
mean_grid_simple
mean_modis_limit
mean_modis
0102030405060708090
100
mean_spot
mean_grid_simple
mean_modis
mean_modis_limit
105
110
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125
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mean_spot
mean_grid_simple
mean_modis
mean_modis_limit
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mean_spot
mean_grid_simple
mean_modis
mean_modis_limit
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mean_spot
mean_grid_simple
mean_modis
mean_modis_limit
0
20
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mean_spot
mean_grid_simple
atlag_gridbol
mean_modis
mean_modis_limit0
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mean_grid_simple
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mean_modis
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mean_spot
mean_grid_simple
mean_modis
mean_modis_limit
0102030405060708090
100
mean_spot
mean_grid_simple
atlag_gridbol
mean_modis
mean_modis_limit
Quality assessment: time-series comparison
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Observation time
Number of parcels
Parcels with valid obs.
Correlation gridded vs. reference
Correlation all MODIS vs. reference
Correlation filtered MODIS vs. reference
Mean diff (~RMSE) gridded
Mean diff (~RMSE) all MODIS
Mean diff (~RMSE) filtered MODIS
Max diff (~RMSE) gridded
Max diff (~RMSE) all MODIS
Max diff (~RMSE) filtered MODIS
2003216_0945 512 466 0,85 0,88 0,94 6,83 7,60 5,14 75,66 37,13 29,852003217_0850 512 333 0,73 0,74 0,89 10,60 10,71 7,72 51,57 40,87 40,182003217_1030 512 207 0,70 0,69 0,94 10,86 11,28 6,26 58,88 44,75 29,972003218_0935 512 463 0,86 0,89 0,95 6,57 7,42 4,74 63,79 41,14 36,522003219_1020 512 310 0,79 0,78 0,94 9,26 9,74 5,45 41,75 43,33 33,022003220_0925 512 460 0,85 0,87 0,93 7,39 8,01 5,59 41,89 38,60 35,212003221_1005 512 381 0,80 0,84 0,95 8,45 8,83 5,11 58,21 41,08 26,442003222_0910 512 419 0,82 0,84 0,92 8,19 8,87 6,22 58,66 43,31 31,572003223_0955 512 444 0,88 0,88 0,95 6,97 7,84 5,09 41,22 35,14 30,652003224_0900 512 363 0,74 0,77 0,91 9,83 10,05 6,86 69,21 49,45 29,012003224_1035 512 142 0,64 0,63 0,93 11,89 12,01 6,09 55,05 43,72 28,71
Quality assessment: statistics
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Quality measure: number of retained pixels
Green: high, Yellow: medium, Orange: low, Red: zero
2003216_0945
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Green: high, Yellow: medium, Red: low, White: N/A
Quality measure: average pixel proportion
2003216_0945
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Quality measure: number of retained pixels
Green: high, Yellow: medium, Orange: low, Red: zero
2003224_1035
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Quality measure: average pixel proportion
Green: high, Yellow: medium, Red: low, White: N/A
2003224_1035
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0 500000 1000000 1500000 2000000 2500000 3000000
010
2030
40
area
diff_
spot
_mod
is
2003224_1035
0 500000 1000000 1500000 2000000 2500000 3000000
010
2030
area
diff
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t_m
odis
2003226_0945
Area vs. accuracy
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2003224_1035
40 60 80 100 120 140
6080
100
120
mean_spot
mea
n_gr
id_s
impl
e
40 60 80 100 120 140
4060
8010
012
0
mean_spot
mea
n_m
odis
_lim
it
N = 512r = 0,638RMSE = 11,89
N = 142r = 0,934RMSE = 6,09
Correlation and RMS errors
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2003216_0945
N = 512r = 0,855RMSE = 6,82
N = 466r = 0,943RMSE = 5,13
40 60 80 100 120 140
6080
100
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140
mean_spot
mea
n_m
odis
_lim
it
40 60 80 100 120 140
4060
8010
012
014
0
mean_spot
mea
n_gr
id_s
impl
e
Correlation and RMS errors
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0.5 0.6 0.7 0.8 0.9
05
1015
2025
mean_pix_prop_limit
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_spo
t_m
odis
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it
2003224_1035
5 10 15 20
05
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pixel_count_limit
diff
_spo
t_m
odis
_lim
it
Pixel proportion, number of pixels, and RMSE
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0 10 20 30 40 50 60
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pixel_count_limit
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0.5 0.6 0.7 0.8 0.9 1.0
05
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mean_pix_prop_limit
diff
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odis
_lim
it
Pixel proportion, number of pixels and RMSE
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CONCLUSIONS AND OUTLOOK
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Conclusions• Gridding is a major source of inaccuracy and noise• New polygon representation of MODIS observations has
significantly increased correlation with same-day high-resolution data: better representation of observation geometry
• Landscape objects delineated a priori can be efficiently used to construct time series
• Data processing requires more computing power, but the user has full control over the process (thresholds, quality measures, etc.) and can obtain more accurate data with maximal spatial and temporal coverage
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Outlook• Currently: „proof of concept” level - further work
on real case studies needed• Point Spread Function is another source of
inaccuracy – could be integrated• Other data representations (Spline surface,
SensorML, etc.) are also to be assessed• Data processing speed is already good
(Python/PostgreSQL/PostGIS), but can be further improved by e.g. parallel computing in cloud architecture: implementation of IQmulus project service
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Thank you!
Questions?Dániel Kristóf
A High-volume Fusion and Analysis Platform for Geospatial Point
Clouds, Coverages and Volumetric Data Sets
www.iqmulus.eu
IQmulus (FP7-ICT-2011-318787) is a 4-year Integrating Project (IP) in the area of Intelligent Information Management within ICT 2011.4.4 Challenge 4: Technologies for Digital Content and Languages. IQmulus started on November 1, 2012, and will finish October 31, 2016.