Abstract: Nitrogen (N),phosphorus (P) and potassium (Kalium,K) contents are important indicators of the nutritional value of pasture grasses,and their accurate detection is of great significance for the evaluation of grassland degradation and the development of animal husbandry. In this study,temperate desert steppe and temperate steppe in Inner Mongolia were used as research objects,and hyperspectral unmanned aerial vehicle (UAV) data and ground-based measured data were combined to screen the sensitive bands by using the least absolute value contraction and selection operator (Lasso) regression. Then,Partial least squares regression (PLSR) and Random forest (RF) algorithms were used to construct the estimation models of N,P,and K contents of natural pasture grasses,respectively. The results showed that the first derivative (FD) and the reciprocal of logarithm (Log(1/R)) treatments could help to improve the correlation between sensitive bands and N,P,and K contents;and the Lasso regression method greatly reduced the number of bands. The best inversion models constructed for N and K contents under both grass types were RF models,with R² between 0.76 and 0.90. The P content was optimized by the PLSR model under the temperate desert steppe with an R² of 0.72 and the RF model under the temperate steppe (R²=0.75). The results of this study have important reference value for grassland classification and grading,grazing management and grassland agriculture.

Key words: Natural forage, Unmanned aerial vehicle (UAV), Hyperspectral, Nutritional composition, Machine learning

摘要： 氮(Nitrogen，N)、磷(Phosphorus，P)、钾(Kalium，K)含量是衡量牧草营养价值的重要指标，其准确检测对于草地退化评价和畜牧业发展具有重要意义。本研究以内蒙古温性荒漠草原和温性草原为研究对象，结合高光谱无人机数据和地面实测数据，使用最小绝对值收缩和选择算子(Lasso)回归来进行敏感波段筛选，然后采用偏最小二乘回归(Partial ieast squares regression,PLSR)和随机森林(Random forest，RF)算法分别构建天然牧草N，P，K含量的估测模型。结果表明：一阶导数(First derivative，FD)和对数的倒数(Reciprocal of logarithm，Log(1/R))处理有助于提高敏感波段与N,P,K含量的相关性；Lasso回归方法可以大大地减少波段的数量；两种草地类型下N和K含量构建的最佳反演模型均为RF模型，R²在0.76~0.90之间；P含量在温性荒漠草原下最优的是PLSR模型，R²为0.72，而在温性草原下为RF模型(R²=0.75)。研究结果对于草地分等定级、放牧管理和草地农业等都具有重要参考价值。

关键词: 天然牧草, 无人机, 高光谱, 营养成分, 机器学习