RESEARCH ON FPAR VERTICAL DISTRIBUTION IN DIFFERENT GEOMETRY MAIZE CANOPY

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

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

6. Methods How to establish models to describe the flux upward and downward based on the radiation transmission equations of SHAW (Simultaneous Heat and Water)) model? Upward Flux （ SHAW model) (Flerchinger，2007） Downward Flux (beam radiation) (scattering radiation)

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 leaf orientation value (LOV):LOV ≥ 45° were treated as erectophile variety; 25° < LOV < 45° were treated as horizontal variety, • Testing ground China National Experimental Station for Precision Agriculture (40º10´N, 116º26´E) • Maize type/seeding time/experimental period 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.

10. Study area Study site Beijing city • Location of Beijing Precision Agriculture Experimental Station Map of Beijing area • Location of the test site Soil sample points

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. Plant geometry measurement The plant features could obtained by measuring the relationship among leaf’s shape, area, and position. The layer space is 20 cm • Equation of leaf shape （Stewart，1993） • LAI/LAD of arbitrary space 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 MLA is Mean Leaf Angle Asner, 1998

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. Effect of LAD on Canopy Spectrum • LAI=2.4 horizontal leaf varieties • LAI=2.6 erective leaf varieties 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. 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°

23. Identification of crop canopy geometry by bidirectional canopy reflected spectrum

24. Geometry Optical Models

25. 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 NW5 NW1 NW2 NW4 NW3

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). Craig Coburn& Philippe Teillet Zhijie Wang LETHBRIDGE UNIVERSITY

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