Polar-orbiting MODIS and VIIRS data ( LoVE-P) · Polar-orbiting MODIS and VIIRS data ( LoVE-P)...
Transcript of Polar-orbiting MODIS and VIIRS data ( LoVE-P) · Polar-orbiting MODIS and VIIRS data ( LoVE-P)...
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The Land continuous Variable Estimator for Polar-orbiting MODIS and VIIRS data (LoVE-P)
Shunlin LiangDepartment of Geographical Sciences
University of Maryland, USA
Nov. 12, 2019CISESS 2019
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Acknowledgements
Thank many students, colleagues and collaborators for their contributions to this talk
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Outline1. Why do we need a new inversion framework?2. Overview of the LoVE-P3. Case studies using MODIS and VIIRS data4. Concluding remarks
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NASA Earth Observing System(EOS)
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satellite images + high-level products
EOS is the first comprehensive mission for monitoring the Earth system
EOS started to convert satellite observations to high-level products
Earth System Science Decision support systems and much wider applications
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Click to edit Master title styleNASA’s approach for generating the EOS
high-level products
Each sensor has a Science Team
Each PI is responsible for one product algorithm development
PI passes the science code to the central facility for production
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Click to edit Master title style
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Generic issues of the EOS framework
♣ Same land product generated from different EOS instruments without using the multiple data sources effectively;
♣ Multiple products from the same sensor by different PIs using different algorithms indepedantly
♣ Looping requirements and conflicting assumptions; Aerosol retrieval Atmospheric correction surface BRDF
♣ Only one algorithm for one product;♣ Temporal signatures not used effectively;♣ Products often spatiotemporally discontinuous and
physically inconsistent
The EOS framework has been widely accepted by various space agencies. However, there are many issues:
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Two:Develop a NEW inversion framework: based on data assimilation
Two solutions
One:Continue to improve the existing framework
This talk
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Why is DA a better framework? Regularization methods
• Challenges: ill-posed inversion problem the number of bands of each pixel always smaller than the number of
unknowns spectral signatures highly correlated
• Solutions– A priori knowledge
• climatology– Spatial constraints– Temporal constraints– Multi-sources data
Data Assimilation
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Liang, S., J. Townshend and R. Dickinson, “Improving Land Surface Products from Multiple EOS Sensors by Developing a Prototype Data Assimilation System”, National Aeronautics and Space Administration(NASA), 4/2004 – 3/2008
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Click to edit Master title styleLand continuous Variable Estimator (LoVE)
The new inversion framework is based on data assimilation, characterized by multiple data sources, multiple algorithms, and multiple products
Data
Model
A priori knowledge & constraints
Products
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Click to edit Master title styleLand continuous Variable Estimator (LoVE)The new generation of the inversion method is based on data assimilation, characterized
by multiple data sources, multiple algorithms, and multiple products
Data Assimilation
Sensor 1 time
series observations
Sensor 3 time
series observations
…
Sensor 2 time
series observations
Indirect Estimation Methods
Direct inversion LAI Leaf angle distribution Leaf reflectance Leaf transmittance FVC AOD LST …
Indirect inversion Albedo FPAR Emissivity Insolation Long-wave radiation Net radiation …
Priori knowledge
Land surface products Statistical constraint Intrinsic Physical.
…
Radiative Transfer Modeling
Dynamic Model
LAI Retrieval Methods Ensemble
FPAR Retrieval Methods Ensemble
LST Retrieval Methods Ensemble
… ABD Retrieval Methods Ensemble
Data Assimilation
Dynamic Model
Radiative Transfer Modeling
Direct inversion
LAI
Leaf angle distribution
Leaf reflectance
Leaf transmittance
FVC
AOD
LST
…
Sensor 1 time series observations
Sensor 2 time series observations
Priori knowledge
Land surface products
Statistical constraint
Intrinsic Physical.
…
LAI Retrieval Methods Ensemble
FPAR Retrieval Methods Ensemble
LST Retrieval Methods Ensemble
Indirect inversion
Albedo
FPAR
Emissivity
Insolation
Long-wave radiation
Net radiation
…
…
ABD Retrieval Methods Ensemble
…
Sensor 3 time series observations
Indirect Estimation Methods
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Radiative transfer modeling
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梁顺林
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1.0
1.5
2.0
2.5
3.0
3.5
4.0 climatology fitting curve
LAI(e
verg
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dlel
eaf t
ree)
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0.7 climatology fitting curve
LAI(b
arre
n or
spa
rsel
y ve
geta
)
julian day
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2.8
3.0
3.2
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3.8
4.0 climatology fitting curve
julian day
LAI(e
verg
reen
bro
adle
af tr
ee)
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0
1
2
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5 climatology fitting curve
LAI(d
ecid
uous
nee
dlel
eaf t
ree)
julian day-50 0 50 100 150 200 250 300 350 400
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1.0
1.5
2.0
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3.0
3.5
4.0
4.5
5.0 climatology fitting curve
LAI(d
ecid
uous
bro
adle
af tr
ee)
julian day-50 0 50 100 150 200 250 300 350 400
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6 climatology fitting curve
LAI(s
hrub
)
julian day
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1.8 climatology fitting curve
LAI(u
rban
and
bui
lt-up
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2.4 climatology fitting curve
LAI(b
road
leaf
cro
p)
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2.2 climatology fitting curve
LAI(c
erea
l cro
p)
julian day
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Empirical dynamic model
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Generating products: direct + indirect
Direct: inversion(LAI, LST,soil reflectance, leaf optics…)
Indirect: model calculations(narrowband albedos, fAPAR, broadband albedos,
incident PAR/insolation)
Category variables(land cover, PFT)
DA
Other information
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梁顺林
First Guess First GuessKnowledge Database
Analysis (inversion)
Prediction Prediction
Analysis (inversion)
Observations Observations
Product generation (time)
Real-time inversionReanalysis
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Recent Publications• Case 1: MODIS surface reflectance -> LAI, FAPAR and albedo
– Xiao, Z.Q., Liang, S., Wang, J.D., Xie, D.H., Song, J.L., & Fensholt, R. (2015). A Framework for Consistent Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Surface Albedo from MODIS Time-Series Data. IEEE Transactions on Geoscience and Remote Sensing, 53, 3178-3197
• Case 2: MODIS+VEGETATION+MISR surface reflectance ->LAI, FAPAR, and albedo– Ma, H., Liu, Q., Liang, S., & Xiao, Z. (2017). Simultaneous Estimation of Leaf
Area Index, Fraction of Absorbed Photosynthetically Active Radiation and Surface Albedo from multiple-Satellite Data. IEEE Transactions on Geoscience and Remote Sensing, 55, 4334 - 4354
• Case 3: MODIS top-of-atmosphere (TOA) (clear-sky) reflectance->LAI, FAPAR, albedo, PAR/APAR– Shi, H., Xiao, Z., Liang, S., & Zhang, X. (2016). Consistent estimation of
multiple parameters from MODIS top of atmosphere reflectance data using a coupled soil-canopy-atmosphere radiative transfer model. Remote Sensing of Environment, 184, 40-57
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Recent Publications – Cont.
• Case 4:MODIS TOA (all-sky) reflectance->LAI, FAPAR, albedo, PAR/APAR– Shi, H., Xiao, Z., Liang, S., & Ma, H. (2017). A Method for Consistent
Estimation of Multiple Land Surface Parameters From MODIS Top-of-Atmosphere Time Series Data. IEEE Transactions on Geoscience and Remote Sensing, 55, doi:10.1109/TGRS.2017.2702609
• Case 5: MODIS surface reflectance + thermal TOA brightness temperature ->LST + …– Ma, H., Liang, S., Xiao, Z., & Shi, H. (2017). Simultaneous inversion of
multiple land surface parameters from MODIS optical–thermal observations. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 240-254
• Case 6: VIIRS surface reflectance + thermal TOA brightness temperature ->LST + …– Ma, H., Liang, S., Xiao, Z., & Wang, D. (2018). Simultaneous Estimation of
Multiple Land Surface Parameters from VIIRS Optical-Thermal data. IeeeGeoscience and Remote Sensing Letters, 15, 151-160
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Case 4: Coupled surface + atmosphere
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Goodwin Creek, 2002
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Case 5: MODIS (visible-thermal)
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The inverted results are better than the existing satellite products at Bondville, 2005
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Case 6: using VIIRS data
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Concluding remarks• The current inversion framework was developed in 1980s. It is
necessary to explore the new generation of the inversion scheme• We proposed a new inversion framework (LoVE-P) based on the
data assimilation method• Multiple case studies demonstrated that LoVE-P works very
well• Strengths of the LoVE-P
– Integration of various regularization methods effectively– Estimation of a group of variables simultaneously– Spatially and temporally continuous– Physically consistent – Better or comparable accuracies– Suitable for any satellite observations (single or multiple)
Slide Number 1AcknowledgementsOutlineSlide Number 4Slide Number 5NASA’s approach for generating the EOS high-level productsGeneric issues of the EOS frameworkTwo solutionsWhy is DA a better framework? Regularization methodsSlide Number 10Slide Number 11Slide Number 12Radiative transfer modelingSlide Number 14Slide Number 15Slide Number 16Slide Number 17Recent PublicationsRecent Publications – Cont.Slide Number 20Slide Number 21Slide Number 22Case 5: MODIS (visible-thermal)Slide Number 243Slide Number 26Slide Number 27Slide Number 28Slide Number 29Slide Number 30Slide Number 31Slide Number 32Slide Number 33Concluding remarks