基于无人机高光谱遥感的水稻田间表型反演辐射传输模型（RTMs）

摘要：水稻田间表型信息的无损快速获取对水稻生长过程的精准管理至关重要。本研究基于无人机高光谱遥感技术反演 水稻的表型信息LAI（（ 叶面积指数）、叶片叶绿素含量（（ Cab）、冠层含水量（（ Cw）、干物质含量（（ Cdm）。利用改进的Sobol 全局灵敏度分析（（ GSA）方法对无人机高光谱遥感获得的400~1100nm光谱波段范围内PROSAIL模型的输入参数进行分 析。结果表明：Cab主要影响400～780 nm波段的光谱，Cdm主要影响760～1000 nm波段，Cw主要影响900～1100 nm 波段，而LAI则影响整个波段。利用M600无人机遥感平台获取水稻冠层400～1100 nm波段的高光谱数据，将辐射度定 标转化为冠层发射率，结合PROSAIL模型，通过构建代价函数，采用粒子群优化算法对水稻表型信息进行反演。结果表 明：（1）Cab反演精度为R2=0.833，RMSE=0.0969，其中RMSE为均方根误差；LAI反演精度为R2=0.816，RMSE=0.1012； Cdm的R2=0.793，RMSE=0.1084；Cw的R2=0.665，RMSE=0.1325，Cw的反演精度不是特别高。（2）同一波段会同时 受到多个参数的影响。（3）本研究在高水平的野外光谱辐射精度的基础上，采用水稻表型信息反演方法，拓展了无人机 水稻高光谱信息的获取领域，反演方法机制好、普适性强、易于实现，可为利用无人机高光谱遥感无损快速反演水稻生 化参数提供参考。

关键词：无人机，水稻表型反演，高光谱遥感，PROSAIL模型，全局灵敏度分析，精准管理

期刊：J Agric & Biol Eng

DOI：10.25165/j.ijabe.20171004.3076

设备：GaiaSky

Radiative transfer models (RTMs) for field phenotyping inversion of rice based on UAV hyperspectral remote sensing

Abstract：The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process. In this research, the phenotyping information LAI (leaf area index), leaf chlorophyll content (Cab), canopy water content (Cw), and dry matter content (Cdm) of rice was inversed based on the hyperspectral remote sensing technology of an unmanned aerial vehicle (UAV). The improved Sobol global sensitivity analysis (GSA) method was used to analyze the input parameters of the PROSAIL model in the spectral band range of 400-1100 nm, which was obtained by hyperspectral remote sensing by the UAV. The results show that Cab mainly affects the spectrum on 400-780 nm band, Cdm on 760-1000 nm band, Cw on 900-1100 nm band, and LAI on the entire band. The hyperspectral data of the 400-1100 nm band of the rice canopy were acquired by using the M600 UAV remote sensing platform, and the radiance calibration was converted to the canopy emission rate. In combination with the PROSAIL model, the particle swarm optimization algorithm was used to retrieve rice phenotyping information by constructing the cost function. The results showed the following: (1) an accuracy of R2=0.833 and RMSE=0.0969, where RMSE denotes root-mean-square error, was obtained for Cab retrieval; R2=0.816 and RMSE=0.1012 for LAI inversion; R2=0.793 and RMSE=0.1084 for Cdm; and R2=0.665 and RMSE=0.1325 for Cw. The Cw inversion accuracy was not particularly high. (2) The same band will be affected by multiple parameters at the same time. (3) This study adopted the rice phenotyping information inversion method to expand the rice hyperspectral information acquisition field of a UAV based on the phenotypic information retrieval accuracy using a high level of field spectral radiometric accuracy. The inversion method featured a good mechanism, high universality, and easy implementation, which can provide a reference for nondestructive and rapid inversion of rice biochemical parameters using UAV hyperspectral remote sensing.

Keywords：UAV, rice phenotyping inversion, hyperspectral remote sensing, PROSAIL model, global sensitivity analysis, precision management