Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle

ABSTRACT: The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.

摘要：水稻叶片的氮含量对其生长品质与作物产量具有显著影响。本文提出并验证了一种基于无人机（unmanned aerial vehicle, UAV）采集的高光谱遥感数据，实现水稻氮含量定量反演的非侵入式方法。研究依托M600无人机平台搭载的高光谱成像仪获取水稻冠层反照率图像，随后采用辐射校正方法对采集数据进行处理，得到冠层叶片的反射率信息。实验以沈阳农业大学的稻田为研究对象，将其划分为4个施肥水平：零氮、低氮、常规氮与高氮。随后采用高斯过程回归（Gaussian process regression, GPR）训练反演算法，筛选出贡献度最高的特定光谱波段，以此降低噪声并提升反演精度。同时引入主成分分析（Principal Component Analysis, PCA）进行降维，减少冗余信息，显著提升运算效率。与地面实测数据对比的结果表明，本文所提出的方法成功构建了水稻氮含量反演模型，其精度通过线性拟合决定系数（R²=0.8525）与均方根误差（RMSE=0.9507）进行量化评估。上述研究结果验证了高斯过程回归的应用有效性，同时为基于无人机高光谱遥感反演水稻氮含量提供了理论依据。