Download presentation

Presentation is loading. Please wait.

Published byNikhil Hind Modified over 2 years ago

1
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 University Beijing Agriculture Information Technology Research Center July 27, 2011

2
Outline INTRODUCTION METHODS VALIDATION CONCLUSION and DISCUSION

3
Introduction FPAR (Fraction of absorbed Photosynthetically Active Radiation) FPAR=APRA/PAR To study on the vertical distribution of FPAR 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.

4
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.

5
Effect parameters canopy structure( Flenet,1996; Stoekle,1992) solar elevation angle( Zhang,1999 ) incident light intensity( Qi,2008 ) 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.

6
Methods Upward Flux Downward Flux （ SHAW model ) (Flerchinger ， 2007 ） (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?

7
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

8
Methods Boundary Condition Layer ’ s FPAR 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.

9
Validation Testing ground China National Experimental Station for Precision Agriculture (40 º 10´N, 116 º 26´E) Maize type/seeding time/experimental period NameVarietySeeding timeexperimental period Jingke25erectophile leaf angle distribution (ELAD) July 6thLittle coiled stage JingDan 28 Horizontal leaf angle distribution (HLAD) July 23rdJointing 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,

10
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

11
Precision Agriculture Research and Demonstration station (167ha)

12
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

13
Equation of leaf shape Plant geometry measurement （ Stewart ， 1993 ） LAI/LAD of arbitrary space The plant features could obtained by measuring the relationship among leaf ’ s shape, area, and position. The layer space is 20 cm Maize leaf and LAD simulation!

14
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.

15
LAI/Leaf Angle and Canopy Spectra Asner, 1998 MLA is Mean Leaf Angle

16
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.

17
n LAI=2.4 horizontal leaf varieties n LAI=2.6 erective leaf varieties Effect of LAD on Canopy Spectrum Canopy Reflectance was different for about the same LAI with different LAD

18
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.

19
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.

20
Model sensitivity analysis Sky scattering light ratio The increase of the ratio of sky scattering light caused the increase of FPAR in upper layer canopy

21
Leaf Angle Distribution (LAD) by beta distribution function and radiative transfer SAILH model for different LAD varieties.

22
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°

23
Identification of crop canopy geometry by bidirectional canopy reflected spectrum

24
Geometry Optical Models

25
n 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.

26
Identification of crop canopy geometry based on bidirectional canopy reflected spectrum

27
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.

28
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 Teillet Zhijie Wang

29
Thank you very much for your attention! Dr. Liu Rongyuan liurongyuan@gmail.com Pro. Huang Wenjiang huangwj@nercita.org.cn

Similar presentations

OK

Abstract: Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor.

Abstract: Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on ramayana in hindi Ppt on accounting standard 17 Ppt on health care services in india Ppt on good engineering practices Ppt on water softening techniques of neutralization Ppt on particle size distribution curve Ppt on power grid failure prep Ppt on promoting tourism in india Ppt on team building and motivation Ppt on atmospheric refraction for class 10