A Data Harmonization Methodology Based On Simultaneous ... · Simultaneous Nadir Overpasses Methodology 18 1. SNO identification and acquisition 2. Gather reference data 3. Orthorectify

A Data Harmonization Methodology

Based On Simultaneous Nadir OverpassesJorge Gil - Juan Fernando Rodrigo - Alberto García - Sergio Gil - Cristina Moclán - Alfredo Romo - Fabrizio Pirondini

17th Joint Agency Commercial Imagery Evaluation (JACIE) Workshop. NOAA Center For Weather And Climate Prediction, MD, USA. September 17th -19th , 2018

• Launched in July 2009, operational since March 2010

• Sun-Synchronous ascending orbit at 650 km

• Mass: 100 Kg; Nadir-pointing platform

• Dual-bank pushbroom CCD, 3 cameras per bank

• Spatial resolution of 22m GSD at 10 bits

• Red, Green and NIR bands similar to Landsat to assure

continuity with existing tools and harmonization with

historical data

• DEIMOS-1 wide swath (625 km) combined with data

download at each orbit assures a high revisit frequency

for any given point on Earth

• No on-board calibration devices. Relies on reference

sensors

The Deimos-1 Earth Observation Satellite

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• A Pseudo-Invariant Calibration Site (PICS) is a place on

the Earth’s surface that has a high spatial uniformity and

a significant radiometric stability in time.

• PICS are being used for on-orbit radiometric trending,

cross calibration and absolute calibration of optical

satellite sensors.

• PICS-Based methodologies:

• Require long series of data

• Have a high latency

• Need BRDF modellization

Radiometry: PICS-Based Methodologies I

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CEOS calibration sites


Radiometry: PICS-Based Methodologies II

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Libya-4 PICS Landsat-7 Trend

Band 3 (red)

Collection-1

Red

• Long series of data: To build the trend minimizing the

uncertainty mainly created by the atmosphere

• High latency: Several cloud-free observations are

needed to create a noticeable change in the trend.

• BRDF modelling: To take into account the viewing

and illumination conditions (seasonal variations)


Simultaneous Nadir Overpasses

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When Landsat 8 was launched in 2013 Landsat 7

had been the radiometric reference for Deimos-1

during 5 years.

Due to the different RSRs in Landsat 8 we needed

to compare Deimos-1 with it immediately. There

was no time to create a trend.

Back in 2014 our terminology was “close

approach”. We acquired a Deimos-1 image over

Libya-4 4.5 minutes before Landsat 8.

It was a “simultaneous nadir overpass” over a

PICS.

The results were presented on JACIE 2014. Libya-4 2014-02-14 “close approach” acquisition.

Deimos-1 08:51:28 UTC – Landsat 8 08:56:00 UTC,


Two acquisitions from different platforms in a short time period over an area which is seen by both sensors nadir-looking.

Simultaneous Nadir OverpassesOur Definition I

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Whereas we want to compare measurements, we

have:

• Minimized uncertainties created by the

atmosphere

• Negligible need of a BRDF modelling

While we have left differences in:

• Relative spectral responses

• Spatial resolution

If we succeed we can focus on the sensor’s

properties since atmospheric and ground features

could be considered the same.


How short is a short time period?

Simultaneous Nadir OverpassesOur Definition II

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The limitation is empirical. Our limit is set to 10 minutes for acquisition planning. After analysis most valid SNOs are less

than 90 seconds.

NOAA National Calibration Center Simultaneous Nadir Overpass (SNO) Predictions are 80 seconds.

SNO over the Bhadi

area in India with a

time gap of ~46

minutes.

Changes caused by

the atmosphere are

observable.

Deimos-1 2018-05-15 04:49:11 Sentinel-2A 2018-05-15 05:35:53 Enhanced TOA reflectance change


What is a SNO area?

Simultaneous Nadir OverpassesOur Definition III

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A circular surface whose center is the ground track crossing. The radius ranges from 15km to 50km depending on the

resolutions being compared, solar elevation, relief, surface features and cloudiness.

Deimos-1 & Sentinel-2B SNO.

Australia. Gap 37s

Deimos-1 & Landsat 8 SNO. Gap 55s Deimos-1 & Sentinel-2A SNO. Gap 83s


The reference sensors we compare

Deimos-1 with are:

• Landsat 7 (bands 2, 3 & 4)

• Landsat 8 (bands 3, 4 & 5)

• Sentinel-2A (bands 3, 4, 8 & 8A)

• Sentinel-2B (bands 3, 4, 8 & 8A)

Blue, green and NIR (narrow and wide)

All polar sun-synchronous with very similar

orbital periods.

Landsats and Sentinels are descending.

Deimos-1 is ascending => Plenty of SNO

opportunities.

Simultaneous Nadir OverpassesOpportunities

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Maximum gap set to

10 minutes.

SNO gaps:

• Australia 7’ 25’’

• Africa 4’ 6’’

• Brazil 2’ 53’’

• Peru 2’ 8’’

An example of Landsat 8 and Deimos-1 automatic SNO prediction over landmasses on the Deimos’ PlanEO planning tool on

2018-07-27.


A double SNO on the same

Deimos-1 orbit highlights the

number of available

opportunities for SNO

acquisitions with Landsat 8 and

Sentinel 2A/B of an ascending

satellite

Deimos-1 (ascending) SNOs

with Landsat-7 and Sentinel-2B

(descending) on Jun the 27th

2018.


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The video shows a SNO

acquisition between Deimos-

1 and Landsat 7 in real time

to illustrate how short the

time gap can be and to

emphasize how ground,

atmospheric and illumination

differences between

samples can be neglected.

Simultaneous Nadir OverpassesAcquisition

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Simultaneous Nadir OverpassesMethodology

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1. SNO identification and acquisition



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2. Gather reference data



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3. Orthorectify Deimos-1 using reference



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4. Locate an accurate nadir position and

extract data over the AoI



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5. Find spatially homogeneous areas for

each band

• NIR



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each band

• NIR

• Red



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each band

• NIR

• Red

• Green



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each band

• NIR

• Red

• Green

6. Calculate an accurate acquisition time at

the scene center for each satellite



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3. Orthorectify Deimos-1 using

reference

4. Locate an accurate nadir position

and extract data over the AoI

5. Find spatially homogeneous areas

for each band

• NIR

• Red

• Green

6. Calculate an accurate acquisition

time at the scene center for each

satellite

7. Extract statistics for each polygon in

both samples


Data Harmonization Based on SNOsA note about homogeneous areas

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• The purpose of the comparison based on

homogeneous areas is to reduce the influence of the

different resolutions and small geometric errors of

the samples being compared

• They are calculated using the highest resolution

sample under the assumption that it will be also

homogeneous in the lowest resolution sample

• They have a minimum size in pixels of the lowest

resolution image


Data Harmonization Based on SNOsA note about robustness

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• The variation of the radius between

15km and 50km don’t change the

results significatively if the scene is

clear and the sun elevation is above

30º

• If random areas are chosen instead

of the homogeneous we obtain

similar results, but with reduced

correlation. No outliers were

removed.

• There is not a noticeable

dependence on the ground type

(rainforest, desert, agricultural) when

random areas are used

• There is a noticeable sensitivity to off-

nadir viewing

Random areas (red) and

homogeneous areas (green) in

a Landsat-7 NIR image (band

4) over a SNO AoI.


Data Harmonization Based on SNOsDifferent Sensors. Same Bands

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• Three representative cases: Australia is semi-arid, Brazil is

rainforest and Congo contains a mixture of vegetation and

bare soil with burned areas

• There can be seen differences between samples, however

the correlation remains high.

Congo Brazil Australia


Data Harmonization Based on SNOsProof of Concept With Different Bands

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• Sentinel-2A and B MSIs have two different

NIR bands

• The wide band 8 is similar to Deimos-1 band

1

• The band 8A is significatively narrower

• Both bands are acquired simultaneously

• What would happen if we compared the

correlation between Deimos-1 band 1 with

Sentinel-2 band 8A for a SNO and Sentinel-2

band 8 with 8A using the described

methodology?


Data Harmonization Based on SNOsProof of Concept With Different Bands

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The ground types in Australia are

desert, mid-latitude forest and

farmland (winter) while in Brazil it

is rainforest.

For the sake of clarity Deimos-1

data is not represented in the

figure.

Slopes Correlation coefficients

SNO Sentinel-2 Deimos-1 Sentinel-2 Deimos-1

2018-06-14-Australia-S2A 0.96426 0.96529 0.98884 0.99168

2018-06-20-Australia-S2B 0.92577 0.91368 0.99703 0.99753

2018-06-25-Australia-S2B 0.91182 0.89418 0.99453 0.96860

2018-06-27-Brazil-S2B 0.81850 0.80149 0.98860 0.96980

Each SNO has a different ground type and atmosphere. As a

consequence the slopes of the linear regression are different

as well.

Nevertheless the slopes of the Sentinel-2 wide NIR and the

Deimos-1 (wide) NIR vs. Sentinel-2 narrow NIR are similar.


Data Harmonization Based on SNOsResults with S2A & S2B

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The figures show the results obtained

from 18 SNOs of Deimos-1 with Sentinel-

2 (9 for each one) over a diversity of

scenes.

Despite the diversity, the correlation

coefficients remain high, which make the

linear adjustments shown suitable for

general use with the proper caution.

Note that the lowest correlation appears

on the DE1 (wide) NIR vs. S2 narrow NIR.


Data Harmonization Based on SNOsResults with Landsat 8

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The figures show the results obtained

from 9 SNOs of Deimos-1 Landsat 8 over

a diversity of scenes.

As it happens with the Sentinel-2 case,

the correlation coefficients remain high,

which make the linear adjustments shown

suitable for general use if special care is

taken when using the NIR band

adjustment.

The Deimos-1 satellite is now routinely

being tasked to acquire under SNO

conditions with Landsat 7/8 and Sentinel-

2A/B.


Data Harmonization Based on SNOsConclusions

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• Being the orbits of Landsat 7, Landsat 8, Sentinel-2A and Sentinel-2B descending, the frequency of the

acquisition opportunities with the ascending Deimos-1 under SNO conditions is high

• When the relative spectral responses of two different sensors are similar, a SNO measurement is virtually the

same in terms of ground, atmospheric features and viewing, illumination geometries; allowing this way the cross-

calibration, harmonization and trend monitoring of the radiometry with reduced latency

• Dissimilar relative spectral responses yield results that are scene-dependent. Grouping a significative number of

different scenes allows us to harmonize the TOA reflectance of different sensors, with limitations

• The harmonization accuracy is limited by the fact that the ground and atmospheric spectral features of a specific

acquisition are neglected. This limitation could be mitigated if the ground type is identified to the maximum

possible granularity and the proper parameters applied, maybe using machine learning techniques


Thank you.Fancy Animations

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Clouds displacement and a smoke plume

moving in a SNO with Deimos-1 and

Landsat-7.

The time gap was 15s

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A Data Harmonization Methodology Based On Simultaneous ... · Simultaneous Nadir Overpasses Methodology 18 1. SNO identification and acquisition 2. Gather reference data 3. Orthorectify