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Xin Li (PI), Yong Ge, Rui Jin, Shaomin Liu, Mingguo Ma, Wenzhong Shi, Rongxing Li, Qinhuo Liu, Shuguo Wang
CAREERI/CAS, IGSNRR/CAS, IRSA/CAS, BNU, WHU, TJU
Presented by Tao Che
January 30, 2014LPVE, ESRIN
Development and Experimental Verification
of Key Techniques to Validate Remote
Sensing Products
Why new validation techniques ? Introduction of the project on the vali
dation of remote sensing products Validation experiment in HiWATER
Outline
2
1 Why new validation techniques ?
3
西西西 西西西西 西西 西西 西西 西西 西西0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.12
0.05
0.36
0.06 0.05
0.15
0.05
AMSR-E soil moisture products
de Jeu, et al., 2008; Su, et al., 2011
Considerable uncertainties in remote sensing products
Region Land typeUncertainties of MODIS
LAI products
IGBP Kalaharisampling strips
Woodland Fairly consistence
Savanna Fairly consistence
Finland Coniferous forest Accuracy is 48%
France Agriculture 20% error
Africa Savanna 2-15% overestimation
India AgricultureBhopal, RMSE 0.92-1.26;
Indore, RMSE 0.20-0.33
BOREAS Coniferous forest 33% overestimation
Hanjiang River Basin
Agriculture, broad-leaved forest, coniferous forest, grassland
10% underestimation
Heihe River Basin
Broad-leaved forest, shrub, coniferous forest
58% underestimation
RM
SE
(m3/
m3
)
Crop
Crop
GrasslandCrop Crop
Grassland
Grassland
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First example: LAI
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LAI is defined as the one-sided green leaf area per unit ground surface area (LAI = leaf area / ground area, m2 m-2) in broadleaf canopies.
Is sampling of LAI easy?(Hufkens et al., 2008, validation experiment in Ejin, northwest China)
Assume point measurement of LAI is reliable (unbiased).Quasi-homogeneous surface: LAI = 0.3
First sampling: FVC = ~0.40 LAI = ΣLAIi / (number of samples) = 0.069 LAIf = LAId × FVC = 0.12
Heterogeneous surface LAI = ΣLAIi / (number of samples) = 0.31if we know the coverage of vegetation (FVC) FVC = ~0.06 LAIf = LAId × FVC = 0.24
Second sampling: FVC = ~0.35 LAI = ΣLAIi / (number of samples) = 0.18 LAIf = LAId × FVC = 0.105
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What are the implications ?
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• Sampling and scaling strategies have great impacts on the estimation of ground truth.
• We were using existing sampling protocol such as VALERI, which could be wrong and ambiguous.
• For heterogeneous land surface, pattern plays an important role. It must be considered in designing sampling.
• More reliable, robust, and rigorously mathematically defined sampling protocol should be developed.
Sampling strategy used in VALERI
Second example: soil moisture
Volumetric water content, is defined mathematically as:
Vw: volume of water; Vs: soil volume; Va: air space.
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Is sampling of soil moisture easy?
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Tibetan Plateau
Yang et al., BAMS, 2013
What are the implications ?
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• Point observation is most likely not a
representative value of pixel/footprint scale truth.
• Sampling for a coarse-scale pixel is a very
challenging work. It may cost a lot of labor and
money so more efficient method is needed.
• Representativeness error should be quantified.
• More reliable, robust, and rigorously
mathematically defined sampling protocol should
be developed.
We need new specifications and techniques for the validation of remote sensing product
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2 The project on the val idation of remote sensing products
Validation project
• Ministry of Science and Technology of China
(MOST) launched a high-tech R&D Program
(863) named ‘Development and experimental
verification of key techniques to validate rem
ote sensing products’ in 2011.
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Objectives
• To build technical specifications for the validation o
f remote sensing products.
• To carry out comprehensive remote sensing experim
ents on ‘truth’ collecting and new validation techniq
ue testing and to verify the applicability of the techn
ical specifications.
• To establish a network in China for routine validatio
n of remote sensing products.
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Targeting variables/products
• Ecology and carbon cycle (9): vegetation type, vegetation index, LAI, FPAR,
fraction of vegetation coverage, leaf chlorophyll content, phenology, GPP,
NPP
• Water cycle (5): precipitation, ET, soil moisture, snow water equivalent, snow
cover area
• Radiation and energy balance (11): aerosol optical depth, albedo, land
surface temperature, reflectivity, downward shortwave radiation, downward
longwave radiation, PAR, net radiation, sensible heat flux, soil heat flux,
aerodynamic roughness.
• Remote sensing image: Geometric accuracy of ZY-3 and other super high
resolution images.
• Polar remote sensing products: grounding line, ice edge, mass balance of ice
sheet.
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Best unbiased prediction of ground truth at pixel scale
Objective function
ℜ
yi
ℜ
yhi
ℜh=1
ℜh=2
ℜh=3
To obtain best and unbiased pixel-scale ground truth in terms of spatial representativeness ba
sed on the mean of surface with non-homogeneity (MSN) spatial sampling optimization schem
e.
Key scientific question: sampling and estimation for heterogeneous surface
Weighted sample value
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∫ℜ
−ℜ ℜ= dssyY )(1
)()( st. ][)(min 2ℜℜℜℜℜ =−= YEyEYyEyv
∑=
ℜ =n
iii ywy
1
∑ ∫=
ℜ
−−
ℜℜℜ=
H
hhh
h
dssy1
11 )( ∑ ∑= =
=H
h
n
ihihih
h
ywa1 1
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Proposed network for the validation of remote sensing products
3 Testing new techniques and new specif ications in HiWATER
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Hi-WATER
An observation matrix to capture the land surface heterogeneity
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SoilNet
WATERNet
LAINet
Li et al. BAMS, 2013
More information: hiwater.westgis.ac.cn/english/Li et al., 2013. HiWATER: Scientific objectives and experimental desig
n. BAMS.
Summary
Currently most of the validation protocols are intuitional, lacking of theoretic foundation.
Obtaining pixel/footprint scale truth is a great challenge. True value is defined as a best unbiased estimation at footprint scale.
New specifications, new sampling and scaling transformation methods should be developed.
Large scale ground observation techniques such as WSN, LAS, eddy covariance system, cosmic ray probe are emerging new hard techniques in the validation of remote sensing products.
More reliable, robust, and rigorously mathematically defined sampling protocol should be developed.
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Thank you !
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