Winter Wheat Total Nitrogen Content Estimation Based on UAV
Hyperspectral Remote Sensing
YANG Xin1, 2, YUAN Zi-ran1, 2, YE Yin1, 2*, WANG Dao-zhong1, 2, HUA Ke-ke1, 2, GUO Zhi-bin1, 2
1. Institute of Soil and Fertilizer, Anhui Academy of Agricultural Sciences，Hefei 230031，China
2. Anhui Key Laboratory of Nutrient Cycling, Resources and Environment, Hefei 230031, China
Abstract：Nitrogen is one of the necessary nutrient elements for crops’ growth and development, and crops’ total nitrogen content is the main index to characterize its nitrogen status. Monitoring the spatial distribution of winter wheat total nitrogen content at the field scale can assist in accurate and quantitative topdressing and reduce environmental pollution. UAV (Unmanned aerial vehicle) hyperspectral data can provide an important data source for crop growth information inversion due to its high resolution, high timeliness and low cost. XGBoost (Extreme Gradient Boosting), a new ensemble learning algorithm with high efficiency and strong generalization ability, can be effectively applied to build a winter wheat total nitrogen content estimation model based on remote sensing data and predict the spatial distribution of winter wheat total nitrogen content at field scale. Therefore, this study selected the winter wheat at the jointing stage in the national soil quality observation and experimental station as the study object and carried out the following work: (1) we obtained the canopy imaging spectral image of winter wheat at the jointing stage with a hyperspectral imager mounted on a low-altitude UAV, and total nitrogen content data of 126 samples combined with ground sampling data. (2) The spectral characteristics of the winter wheat canopy at the jointing stage were analyzed, and the correlation between spectral reflectance of 176 bands and total nitrogen content was analyzed according to the Person correlation coefficient. (3) A winter wheat total nitrogen content estimation model based on UAV hyperspectral at the jointing stage was built with the XGBoost algorithm under different soil fertility conditions. The results showed that: (1) there was a strong correlation between spectral reflectance and total nitrogen content of winter wheat in 176 bands, and the correlation coefficients between spectral reflectance and total nitrogen content in all bands except 735.5 nm were greater than 0.5; (2) The UAV hyperspectral remote sensing estimation model of winter wheat total nitrogen content at jointing stage based on XGBoost algorithm shows high accuracy (R2=0.76, RMSE=2.68); (3) The estimation model of winter wheat total nitrogen content based on XGBoost algorithm can obtain the spatial distribution map of total nitrogen content at field scale under different soil fertility conditions, which shows a significant spatial difference on the whole. This study can provide a scientific basis for the accurate and quantitative topdressing of winter wheat and also provide a reference for the application of UAV hyperspectral remote sensing in precision agriculture.
[1] Hucklesby D P, Brown C M, Howell S E, et al. Agronomy Journal, 1971, 63(2): 274.
[2] Scheromm P, Martin G, Bergoin A, et al. Cereal Chemistry, 1993, 69: 664.
[3] Harper L A, Sharpe R R, Langdale G W, et al. Agronomy Journal, 1987, 79(6): 965.
[4] Daigger L A, Sander D H, Perterson G A. Agronomy Journal, 1976, 68(5): 815.
[5] Roth G W, Fox R H, Marshall H G. Agronomy Journal, 1989, 81(3): 502.
[6] Turner F T, Jund M E. Australian Journal of Experimental Agriculture, 1994, 34(7): 1001.
[7] WANG Lei, BAI You-lu(王 磊, 白由路). Journal of Plant Nutrition and Fertilizers (植物营养与肥料学报), 2006, 12(6): 902.
[8] Liu H, Zhu H, Wang P. International Journal of Remote Sensing, 2017, 38(8-10): 2117.
[9] Kaivosoja J, Pesonen L, Kleemola J, et al. Proc. SPIE, 2013, 8887: 88870H.
[10] Zhang J, Cheng T, Guo W, et al. Plant Methods, 2021, 17(1): 49.
[11] Xia L, Zhang R R, Chen L P, et al. Advances in Animal Biosciences, 2017, 8(2): 833.
[12] Shafiee S, Lied L M, Burud I, et al. Computers and Electronics in Agriculture, 2021, 183(4): 106036.
[13] WANG Li-ai, MA Chang, ZHOU Xu-dong, et al(王丽爱，马 昌，周旭东，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015, 46(1): 259.
[14] ZHANG Yue, TIAN Yuan-sheng, SUN Wen-yi, et al(张 玥, 田园盛, 孙文义, 等). Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2020, 40(1): 209.
[15] Su H, Yang X, Lu W, et al. Remote Sensing, 2019, 11(13): 1598.
[16] Xia Y F, Liu C Z, Li Y Y, et al. Expert Systems with Applications, 2017，78：225.
[17] Chen T, Guestrin C. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Minning, 2016, 785(https://doi.org/10.1145/2939672.2939785).
