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

Yu Fenghua, Xu Tongyu, Du Wen, Ma Hang, Zhang Guosheng, Chen Chunling


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
DOI: 10.25165/j.ijabe.20171004.3076

Citation: Yu F H, Xu T Y, Du W, Ma H, Zhang G S, Chen C L. Radiative transfer models (RTMs) for field phenotyping inversion of rice based on UAV hyperspectral remote sensing. Int J Agric & Biol Eng, 2017; 10(4): 150–157.


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


Yu F H, Xu T Y, Cao Y L, Yang G J, Du W, Wang S. Models for estimating the leaf NDVI of japonica rice on a canopy scale by combining canopy NDVI and multisource environmental data in Northeast China. Int J Agric & Biol Eng, 2016; 9(5): 132–142.

Li F, Victor A, Zhao H, Zhao Y J, Cui X F. PLSR-based airborne hyperspectral remote sensing retrieval of leaf nitrogen content in potato fields. Chinese Journal of Agrometeorology, 2014; 35(3): 338–343. (in Chinese)

Zang Y, Gu X Y, Zhou Z Y, Luo X W, Zang Y, Qi X Y, et al. Review of tensairity and its applications in agricultural aviation. Int J Agric & Biol Eng, 2016; 9(3): 1-14.

Gutierrez M. Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat. International Journal of Remote Sensing, 2015; 36(3): 701–718.

Wu B F, Gommes R, Zhang M, Zeng H W, Yan N N, Zou W T, et al. Global crop monitoring: a satellite-based hierarchical approach. Remote Sensing, 2015; 7(4): 3907–3933.

Calderón R, Navas-Cortés J A, Lucena C, Zarco-Tejada P J. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 2013; 139(23): 231–245.

Huang Y, Thomson S J, Brand H J, Reddy K N. Development of low-altitude remote sensing systems for crop production management. Int J Agric & Biol Eng, 2016; 9(4): 1–11.

Zhang P, Deng L, Lyu Q, He S L, Yi S L, Liu Y D, et al. Effects of citrus tree-shape and spraying height of small unmanned aerial vehicle on droplet distribution. Int J Agric & Biol Eng, 2016; 9(4): 45–52.

Chunhua Z, John M. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 2012; 12(13): 693-712.

Xue X Y, Tu K, Qin W C, Lan Y B, Zhang H H. Drift and deposition of ultra-low altitude and low volume application in paddy field. Int J Agric & Biol Eng, 2014; 7(4): 23–28.

Zhou Y, Hao J P, Zheng J. The development and research trend of beam string structure exploration. Industrial Building, 2013; 8(5): 155–160. (in Chinese)

Verrelst J, Dethier S, Rivera J P, Munoz-Mari J, Camps-Valls G, Moreno J. Active learning methods for efficient hybrid biophysical variable retrieval. IEEE Geoscience and Remote Sensing Letters, 2016; 13(6): 1012–1016.

Verrelst J, Rivera J P, Moreno J. ARTMO's global sensitivity analysis (GSA) toolbox to quantify driving variables of leaf and canopy radiative transfer models. EARSeL eProceedings, Speical Issue 2: 9th EARSeL Imaging Spectroscopy Workshop, 2015; pp.1-11

Verrelst J, Rivera J P, van der Tol C, Magnani F, Mohammed G, Moreno J. Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence. Remote Sensing of Environment, 2016; 166(6): 8-21.

Li X, Yu B, Xin L, Min T, Shi H. Canopy NDVI analysis and yield estimation for cotton in different nitrogen treatments. Transactions of the CSAM, 2014; 45(7): 231–236. (in Chinese)

Yang Y J. It is possible for unmanned aerial vehicle (UAV) fly into the billions of markets. Chinese Pesticide, 2015; 12: 48–55. (in Chinese)

Yasin Z M, Rahman T K A, Zakaria Z. Optimal least squares support vector machines parameter selection in predicting the output of distributed generation. IEEE, 2014: 152–157.

Luo X W. Thoughts on speeding up the development of agricultural aviation technology in China. Agriculture Technology & Equipment, 2014; 5: 7–15. (in Chinese)

Hunt E R, Doraiswamy P C, Mcmurtrey J E, Daughtry C S T, Perry E M, Akhmedov B. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation & Geoinformation, 2013; 21(4): 103–112

Hirooka Y, Homma K, Maki M, Sekiguchi K. Applicability of synthetic aperture radar (SAR) to evaluate leaf area index (LAI) and its growth rate of rice in farmers’ fields in Lao PDR. Field Crops Research, 2015; 176: 119–122.

Liu C G, Wang Y J, Pan K W, Jin Y Q, Jin L, Li W, et al. Photosynthetic carbon and nitrogen metabolism and the relationship between their metabolites and lipid peroxidation in dwarf bamboo (Fargesia rufa Yi) during drought and subsequent recovery. Trees, 2015; 29(6): 1–15.

Rivera J P, Verrelst J, Leoneko G, Moreno J. Multiple cost functions and regularization options for improved retrieval of leaf chlorophyll content and LAI through inversion of the PROSAIL model. Remote Sensing, 2013; 5(7): 3280–3304.

Mulla D J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng., 2013; 114(4): 358–371.

Piekarczyk J. Application of remote sensing in agriculture. Geoinformatica Polonica, 2014; 13(1): 69–75.

Huang Y, Lee M A, Thomson S J, Reddy K N. Ground-based hyperspectral remote sensing for weed management in crop production. Int J Agric & Biol Eng, 2016; 9(2): 98–109.

Full Text: PDF

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.