Optimized Spectral Indices Based Estimation of Forage Grass Biomass
AN Hai-bo1, LI Fei1, ZHAO Meng-li1*, LIU Ya-jun2
1. College of Ecology and Environmental Science, Inner Mongolia Agricultural University, Huhhot 010019, China 2. People’s Government of Mujia Yingzi Town, Chifeng 024005, China
Abstract：As an important indicator of forage production, aboveground biomass will directly illustrate the growth of forage grass. Therefore, Real-time monitoring biomass of forage grass play a crucial role in performing suitable grazing and management in artificial and natural grassland. However, traditional sampling and measuring are time-consuming and labor-intensive. Recently, development of hyperspectral remote sensing provides the feasibility in timely and nondestructive deriving biomass of forage grass. In the present study, the main objectives were to explore the robustness of published and optimized spectral indices in estimating biomass of forage grass in natural and artificial pasture. The natural pasture with four grazing density (control, light grazing, moderate grazing and high grazing) was designed in desert steppe, and different forage cultivars with different N rate were conducted in artificial forage fields in Inner Mongolia. The canopy reflectance and biomass in each plot were measured during critical stages. The result showed that, due to the influence in canopy structure and biomass, the canopy reflectance have a great difference in different type of forage grass. The best performing spectral index varied in different species of forage grass with different treatments (R2=0.00~0.69). The predictive ability of spectral indices decreased under low biomass of desert steppe, while red band based spectral indices lost sensitivity under moderate-high biomass of forage maize. When band combinations of simple ratio and normalized difference spectral indices were optimized in combined datasets of natural and artificial grassland, optimized spectral indices significant increased predictive ability and the model between biomass and optimized spectral indices had the highest R2 (R2=0.72) compared to published spectral indices. Sensitive analysis further confirmed that the optimized index had the lowest noise equivalent and were the best performing index in estimating biomass. In conclusion, optimizing wavebands combination was a promising algorithm for improving prediction abilities of biomass for forage grass.
Key words：Optimized algorithm;Forage grass;Biomass;Spectral index
安海波1，李 斐1*，赵萌莉1*，刘亚俊2 . 基于优化光谱指数的牧草生物量估算 [J]. 光谱学与光谱分析, 2015, 35(11): 3155-3160.
AN Hai-bo1, LI Fei1, ZHAO Meng-li1*, LIU Ya-jun2. Optimized Spectral Indices Based Estimation of Forage Grass Biomass . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(11): 3155-3160.
[1] FAN Yue-jun, HOU Xiang-yang, SHI Hong-xiao, et al（范月君，侯向阳，石红霄）. Acta Prataculturae Sinica（草业学报）, 2012, 21(3): 294. [2] LIU Hong-lai, LU Wei-hua, CHEN Chao（刘洪来，鲁为华，陈 超）. Acta Agrestia Sinica（草地学报），2013, 19(5): 865. [3] YANG Xiao-xia, REN Fei, ZHOU Hua-kun, et al（杨晓霞，任 飞，周华坤，等）. Journal of Plant Ecology（植物生态学报），2014, 38(2): 161. [4] NIU Zhi-chun, NI Shao-xiang（牛志春, 倪少祥）. Acta Geographica Sinica（地理学报）, 2003, 58(5): 696. [5] WANG Xun, LIU Shu-jie, ZHANG Jia-wei, et al（王 迅，刘书杰，张家卫，等）. Remote Sensing for Land & Resources(国土资源遥感), 2013, 25(3): 183. [6] LIU Zhan-yu, HUANG Jing-feng, WU Xin-hong, et al（刘占宇, 黄敬峰, 吴新宏，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2006, 22(2): 111. [7] ZHANG Hong-bin, YANG Gui-xia, HUANG Qing, et al（张宏斌，杨桂霞，黄 青，等）. Acta Prataculturae Sinica（草业学报）, 2009, 18(1): 134. [8] Li Fei, Bodo Mistele, Hu Yuncai, et al. Agricultural and Forest Meteorology, 2013, 180: 45. [9] Li Fei, Bodo Mistele, Hu Yuncai, et al. European Journal of Agronomy, 2013, 52: 199. [10] Hatfield J L, Gitelson A A, Schepers J S, et al. Application of Spectral Remote Sensing for Agronomic Decisions, 2008, 100: 117. [11] Li Fei, Yu Xinmiao, Guo Huifeng, et al. Field Corps Research, 2014, 157: 111. [12] Mutanga O, Skidmore A K. International Journal of Remote Sensing, 2004, 25(19): 4011.
