RESEARCH ON FPAR VERTICAL DISTRIBUTION IN DIFFERENT GEOMETRY MAIZE CANOPY

Dr.Liu Rongyuan liurongyuan@gmail.com

Pro. Huang Wenjiang huangwj@nercita.org.cn

Beijing Normal UniversityBeijing Agriculture Information Technology Research Center

July 27, 2011

Outline

INTRODUCTION METHODS VALIDATION CONCLUSION and DISCUSION

Introduction

FPAR (Fraction of absorbed Photosynthetically Active Radiation)

FPAR=APRA/PAR To study on the vertical distribution of FPA

R in the canopy is important to quantitatively simulate crop photosynthesis, crop NPP&GPP, and crop yield prediction in agricultural application.

PAR is the radiation (400-700nm), FPAR is the fraction of absorbed PAR captured by canopy.

Why we do this research?

The leaf vertical distribution caused the FPAR vertical distribution! (more than 10 leaves for maize)

The leaf angle distribution (LAD) affect the FPAR

distribution

It is important to establish the model to retrieve canopy structure parameters based on remote sensing data.

Effect parameters canopy structure(Flenet,1996; Stoekle,1992)

solar elevation angle(Zhang,1999) incident light intensity(Qi,2008)

skyratioLADLAIhfhFPAR i ,,,,

But the research on FPAR vertical distribution is slim. Our research is to establish a quantitative model taking these effective factors to simulate the FPAR vertical distribution.

Methods

, , 1 , 1 , 1 , 1 1 1, ,

, 1 , 1 1 , 1 , 1 1, , , ,

1

(1 ) (1 )

u i u i d i u i d i d d

dd i d i db i b ib b b b

S S S f f

S f f S f f

（ ）

Upward Flux

Downward Flux

dd, , 1 , , 1 , 1 1, ,

, 1 , 1 1 , -1 , 1 1, , , ,

S (1 )

(1 ) (1 )

i dd i d i dd i d i d d

db i b i u i d ib b d d

S S f f

S f f S f f

（ SHAW model)(Flerchinger ， 2007 ）

db, , 1 , db,0 bS S Ii db i b iS ， (beam radiation)

(scattering radiation)

How to establish models to describe the flux upward and downward based on the radiation transmission equations of SHAW (Simultaneous Heat and Water)) model?

Methods

we derived the Downward flux of short-wave radiation between canopy layer i and the next layer i+1, which contain two parts, the first part is the downward flux of beam radiation , and the other one is the downward flux of scattering radiation.represent the total flux of beam radiation penetrating through the canopy, is the albedo of the canopy leaves by leaf transmissivity, and represent the fraction of beam radiation and scattering radiation passing through the layer i unimpeded by vegetation, respectively is the fraction of reflected upward diffuse radiation that is scattering downward. is the fraction of reflected downward diffuse radiation that is scattering downward is the fraction of reflected upward direct radiation that is scattering downward

MethodsBoundary Condition

sddbbsoilu IIS ,

Layer’s FPAR

700

400 ,,

,,,1,,,,1,,,,1,700

400

,

301

1

301

1

db

iuiuiddiddidbidbii II

SSSSSS

PAR

APARFPAR

Due to the bottom is soil and the boundary condition was set as the equation ( red underline). !! we revised a minor error of the original SHAW model (Flerchinger ， 2007 ） . Finally we established a model to calculate Layer’s FPAR when obtained the upward flux and downward flux of each layer.

Validation Testing ground China National Experimental Station for Precision Agriculture

(40º10´N, 116º26´E)

Maize type/seeding time/experimental period

Name Variety Seeding time experimental period

Jingke25 erectophile leaf angle distribution (ELAD)

July 6th Little coiled stage

JingDan28

Horizontal leaf angle distribution (HLAD)

July 23rd Jointing stage

In order to validate our model ,we developed a test in China National Experimental Station for Precision Agriculture in the summer of 2010. Two different geometry varieties.

leaf orientation value (LOV): LOV ≥ 45° were treated as erectophile variety; 25° < LOV < 45° were treated as horizontal variety,

Beijing city

Study site

Soil sample points Location of the test site

Location of Beijing Precision Agriculture Experimental Station

Study area

Map of Beijing area

Precision Agriculture Research and Demonstration station (167ha)

Validation Measuring parameters Spectrum character of leaf/soil by ASD Plant Features LAI 、 LAD… Vertical Distribution of PAR ---by SUNSCAN Layer setting : each 20cm from top to bottom Measuring time ： every hour from 10 am to 3pm

2 30 1 2 3W W b a b a b a

1

i

i

a

aS Wda

Equation of leaf shape

Plant geometry measurement

（ Stewart， 1993）

LAI/LAD of arbitrary space

90

0tLAD S S

1 1arctan(( ) / ( ))i i i i iy y x x

2 2i i ia x y 1i

i

a

i aS Wda

The plant features could obtained by measuring the relationship among leaf’s shape, area, and position. The layer space is 20 cm

2

2

,

,m

m

x xy ax bx c

x xy dx ex f

Maize leaf and LAD simulation!

Validation

The results show that the model could simulate the FPAR vertical distribution in maize canopy well.

The result of simulation FPAR was validated by SUNSCAN measurement

The model simulation fit measurement in situ well in two different stages. The maximum RMSE is 0.168. In the figure P and V stands for measuring parallel and perpendicular to the row, respectively.

LAI/Leaf Angle and Canopy Spectra

Asner, 1998

MLA is Mean Leaf Angle

Model sensitivity analysis LAI

The increase of LAI caused the increase of FPAR in upper layer canopy, until the FPAR becomes saturation with LAI about seven.

0

5

10

15

20

25

30

35

40

350 850 1350 1850 2350

9428 , LAI =2.35披散 411, LAI =2.67京

LAI=2.4 horizontal leaf varietiesLAI=2.4 horizontal leaf varieties

LAI=2.6 erective leaf varieties

Effect of LAD on Canopy SpectrumCanopy Reflectance was different for about the same LAI with different LAD

Model sensitivity analysis ALA (average leaf angle)-LAD

The increase of ALA caused the decrease of FPAR in upper layer canopy, which indicated that the canopy will intercept more incident light flux if the distribution of its leaf angles is close to horizontal geometry.

Model sensitivity analysis Solar elevation angle

The increase of solar elevation angle caused the decrease of FPAR in upper layer canopy. However, this result did not mean that solar elevation angle will make the absorbed incident flux decrease, because it was also determined by the total amount of incident solar flux.

Model sensitivity analysis

Sky scattering light ratio

0.80

0.81

0.82

0.83

0.84

0.85

0.86

0.87

0.88

0.89

0.90

400 450 500 550 600 650 700Wavelength(nm)

Tot

al F

PA

R0. 2*SkyR

0. 4*SkyR

0. 6*SkyR

0. 8*SkyR

SkyR

The increase of the ratio of sky scattering light caused the increase of FPAR in upper layer canopy

Leaf Angle Distribution (LAD) by beta distribution function and radiative transfer SAILH model for different LAD varieties.

erectophi l e LAD var i et i es

0

0. 04

0. 08

0. 12

0. 16

0. 2

5°

10°

15°

20°

25°

30°

35°

40°

45°

50°

55°

60°

65°

70°

75°

80°

85°

90°

Leaf angl e(° )

Proportion of leaf angle

in 5°

angle classes

pl anophi l e LAD var i et i es

0

0. 04

0. 08

0. 12

0. 16

0. 2

5°

10°

15°

20°

25°

30°

35°

40°

45°

50°

55°

60°

65°

70°

75°

80°

85°

90°

Leaf angl e(° )

Proportion of leaf angle

in 5°

angle classes

hor i zontal LAD var i et i es

0

0. 04

0. 08

0. 12

0. 16

0. 2

5°

10°

15°

20°

25°

30°

35°

40°

45°

50°

55°

60°

65°

70°

75°

80°

85°

90°

Leaf angl e(° )

Proportion of leaf angle

in 5°

angle classes

Leaf Angle Distribution (LAD) by beta distribution function and radiative transfer SAILH model for different LAD varieties.

The proportion of leaf angle in 5° angle classes ( 5°-90°) erectophile varieties is dominated by about 75°, planophile varieties is dominated by about 55° , horizontal varieties is dominated by about 35°

I tem

Erectophile

varieties

Planophile

varieties

Horizontal

varieties

Mean l eaf angl e(°) 73. 8 53. 5 32. 7

Eccentri c rate 0. 9945 0. 9927 0. 9956

Identification of crop canopy geometry by bidirectional canopy reflected spectrum

Gao et al (2003) calculated structural scattering index (SSI) as follows

SSI=ln( nirvolf / red

o gef )

where nirvolf is the volumetric kernel weight at the near-infrared band, and red

o gef is the

geometric kernel weight at the red spectral band.

d’Entremont et al.（1999）proposed the normalized difference f-index (NDFI),

w h ic h w a s b a se d o n th e n o rm a liz e d d if fe re n c e o f volf and geof as follows

NDFI=geovol

geovol

ff

ff

Geometry Optical Models

The structure parameter sensitive index (SPEI) is defined by me for crop LAD identification, which was based on the weight of near-infrared band for the volumetric

kernel ( nirvolf ), the weight of red band for the geometric kernel ( red

o gef ), and the weight

of near-infrared band for constant corresponding to isotropic reflectance ( nirf iso ) as

follows

SPEI= OA/OAL ， OA = nirvolf -

10 iso

nirf- red

o gef ; OAL = nirvolf +

10 iso

nirf- red

o gef

For the vegetation canopy, the high leaf transmittance observation in the near-infrared band results in high multiple scattering within the canopy, and decreases the reflectance anisotropy. The features of LAI are therefore best described by

nadir nirf iso , while the features of LAI and leaf angle distribution are best described by

nadir nirf vol , and soil information is best described by nadir red o gef . The value of OA

indicated the crop LAD and leaf angle distribution information, and the value of OAL indicated the crop LAD, leaf angle distribution, and LAI information. By reasonably

combining the nirvolf , nirf iso , and red

o gef kernels’ weights, we can detect the differences of

crop LAD.

Identification of crop canopy geometry by bidirectional canopy reflected spectrum

A method based on the semi-empirical model of bidirectional reflectance distribution function (BRDF) was introduced in this study. !! The structural parameter sensitive index (SPEI) was used in this study for crop LAD identification. SPEI is proved to be more sensitive to identify erectophile, planophile, and horizontal LAD varieties than the structural scattering index (SSI) and the normalized difference f-index (NDFI). We found that it is feasible to identify horizontal, planophile, and erectophile LAD varieties of wheat by studying bidirectional canopy reflected spectrum.

Identification of crop canopy geometry based on bidirectional canopy reflected spectrum

(a) Mapping AMTIS image by SSIindex

(b) Mapping AMTIS image by NDFI index

(c) Mapping AMTIS image by SPEI index

Conclusion and Discussion Based on the radiation transfer model in canopy,

we simulated the FPAR vertical distribution in the canopy by considering the different geometry maize, and analyzed the influence of parameters such as LAI, LAD, solar elevation angle and the ratio of scattering light.

The result of field measured validation indicated that the model can be used to simulate the vertical distribution of FPAR in different geometry maize canopy.

The structural parameter sensitive index (SPEI) is proper for identification crop canopy geometry based on bidirectional canopy reflected spectrum.

ACKNOWLEDGMENT

The authors thank Prof. G.N. Flerchinger for the valuable discussion.

This study was supported by NSFC( 41071276, 40901173).

LETHBRIDGE UNIVERSITY

Craig Coburn& Philippe TeilletZhijie Wang

Thank you very much for your attention!

Dr. Liu Rongyuan liurongyuan@gmail.com

Pro. Huang Wenjiang huangwj@nercita.org.cn