Estimation of leaf nitrogen content using combination of empirical model

and physically based model

Zhihui Wang a,b, Andrew K. Skidmore a, Roshanak Darvishzadeh a, Uta Heiden c, Marco Heurich d, Tiejun Wang a

a Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente b School of Mathematical and Geospatial Sciences, RMIT University

c Department of Land Surface, Earth Observation Center, German Aerospace Center d Bavarian Forest National Park

CONTENT

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Results and discussion

Materials and methods Background

I. BACKGROUND

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• Role of foliar nitrogen o a limiting factor for plant growth o a primary regulator of physiological

processes o related to canopy and stand-level traits o an important input variable required by

process models

• Hyperspectral remote sensing in estimation leaf nitrogen

(Cho, M., 2007)

• Empirical methods – dominated technique – i.e., spectral indices, stepwise multiple linear

regression, partial least squares regression, support vector regression, and artificial neural network, etc.

• Physically based models – advantage: Transferability, robustness – not successful in estimating nitrogen in fresh

leaves

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• Leaf traits – Nitrogen links with leaf traits – Chlorophyll, dry matter, water content (mass-

based vs. area based) – Area-based leaf traits are incorporated in

physically based models (i.e. PRSPECT)

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• Objectives: – to explore the relationship between mass-

based vs. area-based leaf nitrogen content and leaf traits, and

– apply it to retrieve leaf nitrogen content from fresh leaf spectra combined with physically based models.

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II. MATERIALS AND METHODS

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Study area

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Location of the study area in the Bavarian Forest National Park (BFNP), Germany.

Species composition

25%

68%

3% 1% 3%

European beech Norway spruce White firSycamore Maple Mountain ash

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Heurich, M. und M. Neufanger (2005).

• Fieldwork in Bavarian Forest National Park, Germany – mid-July to late-August, 2013

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Bavarian Dataset

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Leaf parameters

Leaf optical properties (directional hemispherical reflectance/transmittance)

Fresh leaf weight Dry leaf weight

Foliage area Leaf chlorophyll content

Foliar nitrogen concentration

Method

Leaf nitrogen

Regression models

Nitrogen=f(leaf traits)

Leaf traits retrieved from

PROSPECT inversion

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Correlation analysis between mass vs.

area based nitrogen and leaf traits

Leaf traits Equation Unit (1) Area-based leaf traits

Leaf mass per area (LMA) Wd/A g/cm2

Area-based leaf nitrogen content (Narea) Nmass×LMA g/cm2

Equivalent water thickness (EWT) ρw × (Wf –Wd)/A cm

(2) Mass-based leaf traits

Mass-based leaf chlorophyll content (CHLmass) CHLarea/LMA mg/g

Gravimetric water content (GWCf) 100×(Wf –Wd)/Wf %

Leaf dry matter content (LDMC) Wd/Wf mg/g

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The selected leaf traits in this study.

III. RESULTS AND DISCUSSION

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Result (1/4)

CHLarea CHLmass LMA LDMC EWT GWCf

Narea 0.597** -0.282 0.686** 0.066 0.841** -0.066

Nmass 0.637** 0.555** -0.346* -0.663** 0.200 0.633**

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Correlations between leaf nitrogen content and other leaf traits.

** correlations significant at p < 0.01, and * correlations significant at p < 0.05.

Result (2/4)

Independent variables

R2CV RMSECV NRMSECV

Standardized coefficients CHLarea LMA EWT

CHLarea 0.243 4.91E-05 0.130 0.597

LMA 0.376 4.46E-05 0.118 0.686

EWT 0.660 3.29E-05 0.087 0.841

LMA, EWT 0.663 3.28E-05 0.087 0.186 0.710

LMA, CHLarea 0.695 3.12E-05 0.083 0.532 0.631

EWT, CHLarea 0.648 3.35E-05 0.089 0.113 0.770

LMA, EWT, CHLarea 0.710 3.04E-05 0.080 0.350 0.422 0.324

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Performance of regression models for estimating leaf nitrogen content (Narea) using different combinations of independent variables.

Result (3/4)

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10 20 30 40 50 600

10

20

30

40

50

60

Measured CHLarea (µg/cm2)

Estim

ated

CH

L area

(µg/

cm2 )

(a)

0 0.005 0.01 0.0150

0.005

0.01

0.015

Measured EWT (cm)

Estim

ated

EW

T (c

m)

(b)

R2=0.54RMSE=7.72NRMSE=0.20

R2=0.66RMSE=0.0014NRMSE=0.13

0 0.005 0.01 0.0150

0.005

0.01

0.015

Measured LMA (g/cm2)

Estim

ated

LM

A (g

/cm2 )

(c)

R2=0.64RMSE=0.0022NRMSE=0.22

Measured versus estimated leaf traits obtained from PROSPECT model inversion: (a) CHLarea, (b) EWT and (c) LMA.

0 1 2 3 4 5

x 10-4

0

1

2

3

4

5 x 10-4

Measured

Estim

ated

(a)

0 1 2 3 4 5x 10-4

0

1

2

3

4

5 x 10-4

Measured

Estim

ated

(b)

0 1 2 3 4 5x 10-4

0

1

2

3

4

5 x 10-4

Measured

Estim

ated

(c)

0 1 2 3 4 5x 10-4

0

1

2

3

4

5 x 10-4

Measured

Estim

ated

(d)

0 1 2 3 4 5x 10-4

0

1

2

3

4

5 x 10-4

Measured

Estim

ated

(e)

0 1 2 3 4 5x 10-4

0

1

2

3

4

5 x 10-4

Measured

Estim

ated

(f)

0 1 2 3 4 5x 10-4

0

1

2

3

4

5 x 10-4

Measured

Estim

ated

(g)

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Comparison between measured and estimated Narea (g/cm2) using different regression models, coupled with their independent variables retrieved from the PROSPECT model inversion. The independent variables were (a) CHLarea, (b) LMA, (c) EWT, (d) LMA and EWT, (e) LMA and CHLarea, (f) EWT and CHLarea, (g) LMA, EWT and CHLarea.

R2=0.58

R2=0.60

R2=0.36 R2=0.21 R2=0.55

R2=0.44 R2=0.53

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Findings

High correlations were found between area-based leaf N with leaf traits.

Nitrogen links with chlorophyll, LMA and EWT can be used for nitrogen estimation.

Leaf N was moderately well estimated by combining empirical and physically based models.

EWT serves as the best indicator for estimating leaf nitrogen content.

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