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Department of Geography
Recent achievements in HyPlant data processing
and SIF retrieval
A. Damm, A. Schickling & U. Rascher
OPTIMIZE Annual Meeting 2017
Limassol, Cyprus
22-24 February 2017
Department of Geography
Content
Overview HyPlant
HyPlant data processing
Overview of Hyper processing chain
Specific elements
Sensor calibration - (cf. Luis Alonso’s talk)
Destriping
Shift/band broadening
Strategy of atmospheric correction
SIF retrieval
SIF retrievals – achievements and challenges (iFLD, for SFM cf. Sergio Cogliati 's talk)
SIF retrieval examples
Virtual Cloud experiment
Soybean mutants
Herbicide experiment in Latisana
Concluding remarksPage 2
Department of Geography
Overview HyPlant
HyPlant:
• a high performance imaging spectrometer
• dedicated to measure sun-induced chlorophyll fluorescence
• developed as airborne demonstrator of ESA’s 8th Earth Explorer
Fluorescence Explorer (FLEX)
HyPlant comprises two modules
• DUAL
• FLUO
Page 3
HyFLEX- Final report 2012, ESA
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HyPlant characteristics
DUAL: 380 – 2500 nm
VIS 3 nm; SWIR 10 nm FWHM;
1-3 m spatial resolution
FLUO: 670 – 780 nm
0.25 nm (FWHM), 0.11 nm (SSI)
1-3 m spatial resolution
Rascher et al. 2015, GCB
Department of Geography
Overview spec’s
Rascher et al. 2015, GCB
Department of Geography
Content
Overview HyPlant
HyPlant data processing
Overview of Hyper processing chain
Specific elements
Sensor calibration - (cf. Luis Alonso’s talk)
Destriping
Shift/band broadening
Strategy of atmospheric correction
SIF retrieval
SIF retrievals – achievements and challenges (iFLD, for SFM cf. Sergio Cogliati 's talk)
SIF retrieval examples
Virtual Cloud experiment
Soybean mutants
Herbicide experiment in Latisana
Concluding remarksPage 6
Department of Geography
HyPlant processing chain
HyPlant s processing infrastructure has been developed in the project
"HyPlant Processing Experiment” (HYPER)
Page 7
Department of Geography
Page 8
Hyper-PMP, ESA
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L0-L1 processing
Page 9
Destriping tool for
2012 data
Hyper-PMP, ESA
cf. Luis’ talk
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Destriping
First SIF retrieval result showed a severe striping
Department of Geography
Destriping
Flickering pixels are well known for sCMOS detectors (1-2% randomly
affected pixels per frame)
The data from 2012 suffered from flickering pixels as the on-chip
correction failed
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Destriping
A dedicated destriping algorithm could significantly improve image
quality
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Destriping
Spectral signatures before and after correction
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Destriping
SIF maps before and after correction
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Detection of shift and band broadening
Imaging spectrometers are commonly affected by spectral mis-
registrations caused by
• imperfect laboratory calibrations
• instabilities of the system during flight
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Page 16
Detection of shift and band broadening
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… to come for Hyplant: Pixel wise detection of
shift and band broadening
Kuhlman, Hueni, Damm, Brunner (2016), An Algorithm for In-Flight Spectral Calibration of
Imaging Spectrometers. Remote Sensing 8
Department of Geography
Page 18
Hyper-PMP, ESA
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Atmospheric correction
Page 19
Hyper-PMP, ESA
Department of Geography
Page 20
Hyper-PMP, ESA
Department of Geography
SIF retrieval
Page 21
Hyper-PMP, ESA
Department of Geography
Content
Overview HyPlant
HyPlant data processing
Overview of Hyper processing chain
Specific elements
Sensor calibration - (cf. Luis Alonso’s talk)
Destriping
Shift/band broadening
Strategy of atmospheric correction
SIF retrieval
SIF retrievals – achievements and challenges (iFLD, for SFM cf. Sergio Cogliati 's talk)
SIF retrieval examples
Virtual Cloud experiment
Soybean mutants
Herbicide experiment in Latisana
Concluding remarksPage 22
Department of Geography
Accuracy of SIF retrievals (iFLD)
Rascher et al. 2015, GCB
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SIF687 and SIF760 retrieval results
Page 24
SIF687 SIF760 SIF687 SIF760
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SIF retrieval challenges – noise
Page 25
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SIF retrieval challenges – sensor non-uniformities
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SIF687 SIF760 SIF687 SIF760
Stray lightStriping due
to missing
reference surfaces
PSF non-uniformity
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Page 27
Topography effects
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Page 28
Topography effects
Department of Geography
Content
Overview HyPlant
HyPlant data processing
Overview of Hyper processing chain
Specific elements
Sensor calibration - (cf. Luis Alonso’s talk)
Destriping
Shift/band broadening
Strategy of atmospheric correction
SIF retrieval
SIF retrievals – achievements and challenges (iFLD, for SFM cf. Sergio Cogliati 's talk)
SIF retrieval examples
Virtual Cloud experiment
Soybean mutants
Herbicide experiment in Latisana
Concluding remarksPage 29
Department of Geography
Virtual cloud experiment (SoyFlex 2015)
Artificial net reduced 50% of PAR & caused a dark adaptation of plants
Net was removed 20min (tx) - 1s (t0) before flight
t0
t20
t10
Page 30
Campaign report (2015)
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SIF687 and SIF760 retrievals are sensitive for Kautsky
effect
Page 31
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Soybean Mutants – SoyFLEX 2016
Page 32
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Latisana Herbicide experiment
Page 33
Campaign report (2014)
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Latisana Herbicide experiment
Page 34
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Concluding remarks
Four SIF retrieval algorithm available, evaluation and fine tuning ongoing.
As soon as SIF retrievals are reliable, sensor artefacts, non-uniformities,
and other issues causing an in-filling and affect in SIF retrievals.
Proper knowledge of potential effects and strategies for compensation
are treatment.
Atmospheric compensation including topography correction is mandatory for
SIF retrievals. Soon to be implemented in the Hyper processing chain.
HyPlant SIF retrievals show sensitivity for plant functioning:
• Kautsky effect
• Plant stress due to herbicide treatment Page 35
Department of Geography
Many thanks to all involved
groups and persons!!
© Alexander Damm, RSL, 2017
Page 36
