Leaf area index retrivel for maize canopy using optimized leaf angle distribution function of PROSAIL model

被引：0
|
作者：
Su W. [1 ]
Guo H. [1 ]
Zhao D. [1 ]
Liu T. [1 ]
Zhang M. [1 ,2 ]
机构：
[1] College of Information and Electrical Engineering, China Agricultural University, Beijing
[2] College of Resources and Environment, Shandong Agricultural University, Tai'an
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2016年 / 47卷 / 03期
关键词：
GF-1; LAI; Leaf angle distribution function; Maize; PROSAIL model; TLS;
D O I：
10.6041/j.issn.1000-1298.2016.03.033
中图分类号：
学科分类号：
摘要：
Leaf area index (LAI) is one of the important parameters to describe the corn canopy structure. PROSAIL model is a mechanism model for retriving LAI, which can express canopy situation more truly. But the leaf angle distribution function used in PROSAIL model assumed that the leaf angle distribution is constant during whole crop growth period, and it can't reflect the actual leaf angle distribution of the corn plant. This paper studied the extracting method of the maize LAI based on the PROSAIL model, using GF-1 images and terrestrial LiDAR data. In order to get the leaf angle distribution of maize, the point cloud of maize was separated into small leaf units through voxel method, and then the surface was matched according to the point cloud in each voxel. The accurate leaf angle distribution function was acquired from statistics data of each leaf unit angle. Combining with the ellipsoid distribution function, the accurate leaf angle distribution function was got, which is used to optimize the PROSAIL model. In this research, the maize canopy LAI was retrived in Farm No.852, Heilongjiang Province, through traditional PROSAIL model and optimized PROSAIL model respectively. The main conclusion is as follows: all of the two methods of inversion of LAI have a good correlation with measured LAI as coefficient is 0.5576 and 0.8583 respectively, which proved that this model is credibly. But the result of inversed LAI based on unimproved model is low. After optimized model with TLS data, the inversion of LAI estimation accuracy was improved from 26.53% to 96.23%. Therefore, it can greatly improve the accuracy of LAI inversion by introducing the TLS point cloud data to improve crops leaf angle distribution function in PROSAIL model. © 2016, Chinese Society of Agricultural Machinery. All right reserved.
引用
收藏
页码：234 / 241and271
相关论文
共 20 条
  • [11] Jupp D.L.B., Culvenor D.S., Lovell J.L., Et al., Estimating forest LAI profiles and structural parameters using a ground-based laser called 'Echidna, Tree Physiology, 29, 2, pp. 171-181, (2009)
  • [12] Wang X., Fan W., Wen Y., Simulation of leaf inclination angle distribution of main tree species in Daxing'an Mountains of China based on the Campbell ellipsoid distribution function, Chinese Journal of Application Ecology, 24, 11, pp. 3199-3206, (2013)
  • [13] Jacquemoud S., Baret F., PROSPECT: A model of leaf optical properties spectra, Remote Sensing of Environment, 34, 2, pp. 75-91, (1990)
  • [14] Campbell G.S., Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution, Agricultural & Forest Meteorology, 36, 4, pp. 317-321, (1986)
  • [15] Zhang X., Xu D., Zhao M., Review on forest canopy structure, radiation transfer and canopy photosynthesis, Forest Research, 12, 4, pp. 411-421, (1999)
  • [16] Zhang J., Wang Y., Zhao Y., Et al., Dynamic simulation of leaf inclination angle distribution based on ellipsoidal function, Transactions of the Chinese Society for Agricultural Machinery, 40, 4, pp. 157-160, (2009)
  • [17] Li Y., Wang R., Wang X., Et al., Simulating rice leaf angle inclination distribution by elliptical distribution function, Journal of Biomathematics, 18, 1, pp. 105-108, (2003)
  • [18] Campbell G.S., Derivation of an angle density function for canopies with ellipsoidal leaf angle distributions, Agricultural & Forest Meteorology, 49, 3, pp. 173-176, (1990)
  • [19] Zheng G., Moskal L.M., Leaf orientation retrieval from terrestrial laser scanning (TLS) data, IEEE Transactions on Geoscience & Remote Sensing, 50, 10, pp. 3970-3979, (2012)
  • [20] Hosoi F., Nakai Y., Omasa K., Estimating of leaf inclination angle distribution of the wheat canopy using a portable scanning LiDAR, The Society of Agricultural Meteorology of Japan, 65, 3, pp. 297-302, (2009)
12