Uncertainty Propagation of Atmospheric Correction Parameters

N. Bhatia

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

Abstract

The main objective of this thesis was to quantify the propagation of
uncertainty in a layered processing system as applied in Processing and
Archiving Facilities (PAF). A framework is presented that is suited for the
purpose. Practical uncertainty estimates are then obtained in estimating a
re
ectance product using three parameters: Column Water Vapour (CWV),
Aerosol Optical Depth (AOD), and the adjacency range. We demonstrate
the propagation of uncertainty from the re
ectance product to application
products, by focusing on unmixing i.e. retrieving materials and their
proportional abundances present in each pixel. The thesis is divided into six
chapters.
After the rst introductory chapter, the second chapter introduces uncertainty
and the framework.
The third chapter presents a generic method to quantify the sensitivity of
re
ectance to CWV concentration, AOD, and adjacency range parameters
via the atmospheric correction modelling (AC). The approximate dispersion
in re
ectance estimates was related to the contribution of each parameter by
performing a Sensitivity Analysis (SA) using a Fourier Amplitude Sensitivity
Test (FAST). We studied the eects of surface albedo on Sensitivity Indices
(SI) for three target surfaces in the spectral range 0.42{0.96 µm: a dark target
(water), a bright target (bare soil), and a target with a low albedo in the
visible and a high albedo in the near infrared range (vegetation). For AOD,
high ( 0.9) SI values were observed at the non-water absorption wavelengths.
For CWV concentration, high SI values were observed at wavelengths with
strong absorption features and if the surface albedo was high. For the dark
target, the eect of AOD was prominent throughout the spectral range. We
found that the sensitivity of re
ectance to CWV concentration and AOD is a
function of the wavelength, strength of the absorption features, and surface
albedo. Such information provided a greater insight into how to deal with
absorption, scattering, and adjacency range type parameters.
The fourth chapter presents a generic method for a qualitative and
quantitative analysis of uncertainty propagation from values of the CWV
concentration and AOD to the fractional abundances derived from unmixing.
Both Fully Constrained Least Squares (FCLS) and FCLS with Total
Variation (FCLS-TV) were applied to estimate abundance maps. We used
ve simulated datasets contaminated by various noise levels. Three datasets
cover two spectral scenarios with dierent endmembers. On those a univariate and a bivariate analysis were carried out on CWV concentration
and AOD. The other two datasets were used to analyse the eect of surface
albedo. The analysis identied trends in performance degradation caused by
the gradual shift in parameter values from their true value. The maximum
achievable performance depends upon spectral characteristics of the datasets,
noise level, and surface albedo. As expected, under noisy conditions
FCLS-TV performs better than FCLS. This experiment helped in addressing
various concerns pertaining to quantifying the propagation of uncertainty
like identifying the best ways to report the propagation of uncertainty.
Propagation of uncertainty was expressed both by measuring various
quantities at pixel level and at scene level. In addition, we addressed the
question on how to incorporate the eect of noise and surface albedo. We
found that unmixing provided a greater insight into how to incorporate a
wider range of applications to the propagation framework.
The fth chapter presents a generic method to estimate and calibrate
concentration of CWV under uncertainty. The method iteratively estimates
the concentration of CWV from the pre-estimates of target surface
re
ectances. The method was free from assumptions, in contrast to at-sensor
radiance based CWV concentration estimation methods. We considered two
cases: (a) CWV concentration was incorrectly estimated in a processing
chain; (b) CWV concentration was not estimated in a processing chain. To
solve (a) we used the incorrect estimations as initial values to the proposed
method during calibration, whereas for (b), CWV concentration was
estimated without initial information. Next, we combined the two scenarios,
resulting in a generic method to calibrate and estimate CWV concentration.
We utilised the Hyperspectral Mapper (HyMap) and Airborne Prism
EXperiment (APEX) instruments for the synthetic and real data
experiments, respectively. Noise levels were added to the synthetic data to
simulate real imaging conditions. For performance assessment, we compared
the proposed method with two state-of-the-art methods. The developed
method performed better than the two methods used for comparison. The
developed method minimised the absolute error close to zero, within only
8{10 iterations. It thus suits existing PAFs where the number of iterations is
an important consideration. Finally, the method is simple to implement and
can be extended to address other atmospheric trace gases.
The sixth chapter presents a generic method to estimate AOD under
uncertainty. AOD was estimated using the pre-estimates of surface
re
ectance. Assumptions concerning retrieval uncertainty and instrumental
errors were less in
uential than for methods based upon the at-sensor
radiance. Using simulated data from HyMap instruments and real data from
Apex instruments, this resulted in an iterative pixelwise estimation of AOD
from estimates of re
ectance. Noise levels were added to the simulated data
to simulate real imaging conditions. Results show that the proposed method
requires 6{8 iterations. It thus suits existing PAFs where the number of
iterations is an important consideration. Further, the method is free from
assumptions for the at-sensor radiance based estimation methods. Finally,
the method is simple to implement, it reduces the processing time in PAFs,
and it can be extended to address other AC parameters.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Faculty of Geo-Information Science and Earth Observation
Supervisors/Advisors
  • Stein, Alfred, Supervisor
  • Tolpekin, V.A., Supervisor
  • Reusen, I., Supervisor
Award date17 May 2018
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-4237-1
DOIs
Publication statusPublished - 2018

Fingerprint

atmospheric correction
water vapor
optical depth
aerosol
albedo
method
parameter
estimation method
wavelength
pixel
atmospheric gas
performance assessment
trend analysis
qualitative analysis
bare soil
trace gas
radiance
modeling
sensitivity analysis

Cite this

Bhatia, N. (2018). Uncertainty Propagation of Atmospheric Correction Parameters. Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/1.9789036542371
Bhatia, N.. / Uncertainty Propagation of Atmospheric Correction Parameters. Enschede : University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2018. 112 p.
@phdthesis{857cab1177aa4af393c2d071214ebcea,
title = "Uncertainty Propagation of Atmospheric Correction Parameters",
abstract = "The main objective of this thesis was to quantify the propagation ofuncertainty in a layered processing system as applied in Processing andArchiving Facilities (PAF). A framework is presented that is suited for thepurpose. Practical uncertainty estimates are then obtained in estimating areectance product using three parameters: Column Water Vapour (CWV),Aerosol Optical Depth (AOD), and the adjacency range. We demonstratethe propagation of uncertainty from the reectance product to applicationproducts, by focusing on unmixing i.e. retrieving materials and theirproportional abundances present in each pixel. The thesis is divided into sixchapters.After the rst introductory chapter, the second chapter introduces uncertaintyand the framework.The third chapter presents a generic method to quantify the sensitivity ofreectance to CWV concentration, AOD, and adjacency range parametersvia the atmospheric correction modelling (AC). The approximate dispersionin reectance estimates was related to the contribution of each parameter byperforming a Sensitivity Analysis (SA) using a Fourier Amplitude SensitivityTest (FAST). We studied the eects of surface albedo on Sensitivity Indices(SI) for three target surfaces in the spectral range 0.42{0.96 µm: a dark target(water), a bright target (bare soil), and a target with a low albedo in thevisible and a high albedo in the near infrared range (vegetation). For AOD,high ( 0.9) SI values were observed at the non-water absorption wavelengths.For CWV concentration, high SI values were observed at wavelengths withstrong absorption features and if the surface albedo was high. For the darktarget, the eect of AOD was prominent throughout the spectral range. Wefound that the sensitivity of reectance to CWV concentration and AOD is afunction of the wavelength, strength of the absorption features, and surfacealbedo. Such information provided a greater insight into how to deal withabsorption, scattering, and adjacency range type parameters.The fourth chapter presents a generic method for a qualitative andquantitative analysis of uncertainty propagation from values of the CWVconcentration and AOD to the fractional abundances derived from unmixing.Both Fully Constrained Least Squares (FCLS) and FCLS with TotalVariation (FCLS-TV) were applied to estimate abundance maps. We usedve simulated datasets contaminated by various noise levels. Three datasetscover two spectral scenarios with dierent endmembers. On those a univariate and a bivariate analysis were carried out on CWV concentrationand AOD. The other two datasets were used to analyse the eect of surfacealbedo. The analysis identied trends in performance degradation caused bythe gradual shift in parameter values from their true value. The maximumachievable performance depends upon spectral characteristics of the datasets,noise level, and surface albedo. As expected, under noisy conditionsFCLS-TV performs better than FCLS. This experiment helped in addressingvarious concerns pertaining to quantifying the propagation of uncertaintylike identifying the best ways to report the propagation of uncertainty.Propagation of uncertainty was expressed both by measuring variousquantities at pixel level and at scene level. In addition, we addressed thequestion on how to incorporate the eect of noise and surface albedo. Wefound that unmixing provided a greater insight into how to incorporate awider range of applications to the propagation framework.The fth chapter presents a generic method to estimate and calibrateconcentration of CWV under uncertainty. The method iteratively estimatesthe concentration of CWV from the pre-estimates of target surfacereectances. The method was free from assumptions, in contrast to at-sensorradiance based CWV concentration estimation methods. We considered twocases: (a) CWV concentration was incorrectly estimated in a processingchain; (b) CWV concentration was not estimated in a processing chain. Tosolve (a) we used the incorrect estimations as initial values to the proposedmethod during calibration, whereas for (b), CWV concentration wasestimated without initial information. Next, we combined the two scenarios,resulting in a generic method to calibrate and estimate CWV concentration.We utilised the Hyperspectral Mapper (HyMap) and Airborne PrismEXperiment (APEX) instruments for the synthetic and real dataexperiments, respectively. Noise levels were added to the synthetic data tosimulate real imaging conditions. For performance assessment, we comparedthe proposed method with two state-of-the-art methods. The developedmethod performed better than the two methods used for comparison. Thedeveloped method minimised the absolute error close to zero, within only8{10 iterations. It thus suits existing PAFs where the number of iterations isan important consideration. Finally, the method is simple to implement andcan be extended to address other atmospheric trace gases.The sixth chapter presents a generic method to estimate AOD underuncertainty. AOD was estimated using the pre-estimates of surfacereectance. Assumptions concerning retrieval uncertainty and instrumentalerrors were less inuential than for methods based upon the at-sensorradiance. Using simulated data from HyMap instruments and real data fromApex instruments, this resulted in an iterative pixelwise estimation of AODfrom estimates of reectance. Noise levels were added to the simulated datato simulate real imaging conditions. Results show that the proposed methodrequires 6{8 iterations. It thus suits existing PAFs where the number ofiterations is an important consideration. Further, the method is free fromassumptions for the at-sensor radiance based estimation methods. Finally,the method is simple to implement, it reduces the processing time in PAFs,and it can be extended to address other AC parameters.",
author = "N. Bhatia",
note = "ITC Dissertation; 292",
year = "2018",
doi = "10.3990/1.9789036542371",
language = "English",
isbn = "978-90-365-4237-1",
series = "ITC Dissertation",
publisher = "University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)",
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Bhatia, N 2018, 'Uncertainty Propagation of Atmospheric Correction Parameters', Doctor of Philosophy, Faculty of Geo-Information Science and Earth Observation, Enschede. https://doi.org/10.3990/1.9789036542371

Uncertainty Propagation of Atmospheric Correction Parameters. / Bhatia, N.

Enschede : University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2018. 112 p.

Research output: ThesisPhD Thesis - Research UT, graduation UTAcademic

TY - THES

T1 - Uncertainty Propagation of Atmospheric Correction Parameters

AU - Bhatia, N.

N1 - ITC Dissertation; 292

PY - 2018

Y1 - 2018

N2 - The main objective of this thesis was to quantify the propagation ofuncertainty in a layered processing system as applied in Processing andArchiving Facilities (PAF). A framework is presented that is suited for thepurpose. Practical uncertainty estimates are then obtained in estimating areectance product using three parameters: Column Water Vapour (CWV),Aerosol Optical Depth (AOD), and the adjacency range. We demonstratethe propagation of uncertainty from the reectance product to applicationproducts, by focusing on unmixing i.e. retrieving materials and theirproportional abundances present in each pixel. The thesis is divided into sixchapters.After the rst introductory chapter, the second chapter introduces uncertaintyand the framework.The third chapter presents a generic method to quantify the sensitivity ofreectance to CWV concentration, AOD, and adjacency range parametersvia the atmospheric correction modelling (AC). The approximate dispersionin reectance estimates was related to the contribution of each parameter byperforming a Sensitivity Analysis (SA) using a Fourier Amplitude SensitivityTest (FAST). We studied the eects of surface albedo on Sensitivity Indices(SI) for three target surfaces in the spectral range 0.42{0.96 µm: a dark target(water), a bright target (bare soil), and a target with a low albedo in thevisible and a high albedo in the near infrared range (vegetation). For AOD,high ( 0.9) SI values were observed at the non-water absorption wavelengths.For CWV concentration, high SI values were observed at wavelengths withstrong absorption features and if the surface albedo was high. For the darktarget, the eect of AOD was prominent throughout the spectral range. Wefound that the sensitivity of reectance to CWV concentration and AOD is afunction of the wavelength, strength of the absorption features, and surfacealbedo. Such information provided a greater insight into how to deal withabsorption, scattering, and adjacency range type parameters.The fourth chapter presents a generic method for a qualitative andquantitative analysis of uncertainty propagation from values of the CWVconcentration and AOD to the fractional abundances derived from unmixing.Both Fully Constrained Least Squares (FCLS) and FCLS with TotalVariation (FCLS-TV) were applied to estimate abundance maps. We usedve simulated datasets contaminated by various noise levels. Three datasetscover two spectral scenarios with dierent endmembers. On those a univariate and a bivariate analysis were carried out on CWV concentrationand AOD. The other two datasets were used to analyse the eect of surfacealbedo. The analysis identied trends in performance degradation caused bythe gradual shift in parameter values from their true value. The maximumachievable performance depends upon spectral characteristics of the datasets,noise level, and surface albedo. As expected, under noisy conditionsFCLS-TV performs better than FCLS. This experiment helped in addressingvarious concerns pertaining to quantifying the propagation of uncertaintylike identifying the best ways to report the propagation of uncertainty.Propagation of uncertainty was expressed both by measuring variousquantities at pixel level and at scene level. In addition, we addressed thequestion on how to incorporate the eect of noise and surface albedo. Wefound that unmixing provided a greater insight into how to incorporate awider range of applications to the propagation framework.The fth chapter presents a generic method to estimate and calibrateconcentration of CWV under uncertainty. The method iteratively estimatesthe concentration of CWV from the pre-estimates of target surfacereectances. The method was free from assumptions, in contrast to at-sensorradiance based CWV concentration estimation methods. We considered twocases: (a) CWV concentration was incorrectly estimated in a processingchain; (b) CWV concentration was not estimated in a processing chain. Tosolve (a) we used the incorrect estimations as initial values to the proposedmethod during calibration, whereas for (b), CWV concentration wasestimated without initial information. Next, we combined the two scenarios,resulting in a generic method to calibrate and estimate CWV concentration.We utilised the Hyperspectral Mapper (HyMap) and Airborne PrismEXperiment (APEX) instruments for the synthetic and real dataexperiments, respectively. Noise levels were added to the synthetic data tosimulate real imaging conditions. For performance assessment, we comparedthe proposed method with two state-of-the-art methods. The developedmethod performed better than the two methods used for comparison. Thedeveloped method minimised the absolute error close to zero, within only8{10 iterations. It thus suits existing PAFs where the number of iterations isan important consideration. Finally, the method is simple to implement andcan be extended to address other atmospheric trace gases.The sixth chapter presents a generic method to estimate AOD underuncertainty. AOD was estimated using the pre-estimates of surfacereectance. Assumptions concerning retrieval uncertainty and instrumentalerrors were less inuential than for methods based upon the at-sensorradiance. Using simulated data from HyMap instruments and real data fromApex instruments, this resulted in an iterative pixelwise estimation of AODfrom estimates of reectance. Noise levels were added to the simulated datato simulate real imaging conditions. Results show that the proposed methodrequires 6{8 iterations. It thus suits existing PAFs where the number ofiterations is an important consideration. Further, the method is free fromassumptions for the at-sensor radiance based estimation methods. Finally,the method is simple to implement, it reduces the processing time in PAFs,and it can be extended to address other AC parameters.

AB - The main objective of this thesis was to quantify the propagation ofuncertainty in a layered processing system as applied in Processing andArchiving Facilities (PAF). A framework is presented that is suited for thepurpose. Practical uncertainty estimates are then obtained in estimating areectance product using three parameters: Column Water Vapour (CWV),Aerosol Optical Depth (AOD), and the adjacency range. We demonstratethe propagation of uncertainty from the reectance product to applicationproducts, by focusing on unmixing i.e. retrieving materials and theirproportional abundances present in each pixel. The thesis is divided into sixchapters.After the rst introductory chapter, the second chapter introduces uncertaintyand the framework.The third chapter presents a generic method to quantify the sensitivity ofreectance to CWV concentration, AOD, and adjacency range parametersvia the atmospheric correction modelling (AC). The approximate dispersionin reectance estimates was related to the contribution of each parameter byperforming a Sensitivity Analysis (SA) using a Fourier Amplitude SensitivityTest (FAST). We studied the eects of surface albedo on Sensitivity Indices(SI) for three target surfaces in the spectral range 0.42{0.96 µm: a dark target(water), a bright target (bare soil), and a target with a low albedo in thevisible and a high albedo in the near infrared range (vegetation). For AOD,high ( 0.9) SI values were observed at the non-water absorption wavelengths.For CWV concentration, high SI values were observed at wavelengths withstrong absorption features and if the surface albedo was high. For the darktarget, the eect of AOD was prominent throughout the spectral range. Wefound that the sensitivity of reectance to CWV concentration and AOD is afunction of the wavelength, strength of the absorption features, and surfacealbedo. Such information provided a greater insight into how to deal withabsorption, scattering, and adjacency range type parameters.The fourth chapter presents a generic method for a qualitative andquantitative analysis of uncertainty propagation from values of the CWVconcentration and AOD to the fractional abundances derived from unmixing.Both Fully Constrained Least Squares (FCLS) and FCLS with TotalVariation (FCLS-TV) were applied to estimate abundance maps. We usedve simulated datasets contaminated by various noise levels. Three datasetscover two spectral scenarios with dierent endmembers. On those a univariate and a bivariate analysis were carried out on CWV concentrationand AOD. The other two datasets were used to analyse the eect of surfacealbedo. The analysis identied trends in performance degradation caused bythe gradual shift in parameter values from their true value. The maximumachievable performance depends upon spectral characteristics of the datasets,noise level, and surface albedo. As expected, under noisy conditionsFCLS-TV performs better than FCLS. This experiment helped in addressingvarious concerns pertaining to quantifying the propagation of uncertaintylike identifying the best ways to report the propagation of uncertainty.Propagation of uncertainty was expressed both by measuring variousquantities at pixel level and at scene level. In addition, we addressed thequestion on how to incorporate the eect of noise and surface albedo. Wefound that unmixing provided a greater insight into how to incorporate awider range of applications to the propagation framework.The fth chapter presents a generic method to estimate and calibrateconcentration of CWV under uncertainty. The method iteratively estimatesthe concentration of CWV from the pre-estimates of target surfacereectances. The method was free from assumptions, in contrast to at-sensorradiance based CWV concentration estimation methods. We considered twocases: (a) CWV concentration was incorrectly estimated in a processingchain; (b) CWV concentration was not estimated in a processing chain. Tosolve (a) we used the incorrect estimations as initial values to the proposedmethod during calibration, whereas for (b), CWV concentration wasestimated without initial information. Next, we combined the two scenarios,resulting in a generic method to calibrate and estimate CWV concentration.We utilised the Hyperspectral Mapper (HyMap) and Airborne PrismEXperiment (APEX) instruments for the synthetic and real dataexperiments, respectively. Noise levels were added to the synthetic data tosimulate real imaging conditions. For performance assessment, we comparedthe proposed method with two state-of-the-art methods. The developedmethod performed better than the two methods used for comparison. Thedeveloped method minimised the absolute error close to zero, within only8{10 iterations. It thus suits existing PAFs where the number of iterations isan important consideration. Finally, the method is simple to implement andcan be extended to address other atmospheric trace gases.The sixth chapter presents a generic method to estimate AOD underuncertainty. AOD was estimated using the pre-estimates of surfacereectance. Assumptions concerning retrieval uncertainty and instrumentalerrors were less inuential than for methods based upon the at-sensorradiance. Using simulated data from HyMap instruments and real data fromApex instruments, this resulted in an iterative pixelwise estimation of AODfrom estimates of reectance. Noise levels were added to the simulated datato simulate real imaging conditions. Results show that the proposed methodrequires 6{8 iterations. It thus suits existing PAFs where the number ofiterations is an important consideration. Further, the method is free fromassumptions for the at-sensor radiance based estimation methods. Finally,the method is simple to implement, it reduces the processing time in PAFs,and it can be extended to address other AC parameters.

U2 - 10.3990/1.9789036542371

DO - 10.3990/1.9789036542371

M3 - PhD Thesis - Research UT, graduation UT

SN - 978-90-365-4237-1

T3 - ITC Dissertation

PB - University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)

CY - Enschede

ER -

Bhatia N. Uncertainty Propagation of Atmospheric Correction Parameters. Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2018. 112 p. (ITC Dissertation). https://doi.org/10.3990/1.9789036542371