pushing modis to the edge: high-resolution applications of moderate-resolution data

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!

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

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Day

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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:

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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…then simulated „pure” MODIS data as the zonal mean of SPOT NIR pixels…

MODIS footprints on SPOT background, 2003216_0945

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MODIS footprints with SPOT-derived values, 2003216_0945

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MODIS footprints on SPOT background, 2003217_0850


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MODIS footprints with SPOT-derived values, 2003217_0850


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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


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Simulated MODIS observations, geometry: 2003216_0945

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Simulated MODIS observations, geometry: 2003217_0850

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Zoom-in: grid cell vs. observation geometry

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MODIS observation from 2003216_0945 and the corresponding grid cell

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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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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

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0 500000 1000000 1500000 2000000 2500000 3000000

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Area vs. accuracy

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2003224_1035

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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

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Correlation and RMS errors

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0.5 0.6 0.7 0.8 0.9

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Pixel proportion, number of pixels, and RMSE

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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

[email protected]

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.

pushing modis to the edge: high-resolution applications of moderate-resolution data

Technology

modis geolocation files

pure modis data

modis particularitiesclose

modis pixelband

modis pixel footprint

day modis surface reflectance

data nir band

gapless data