This study investigated the potential of in-season airborne hyperspectral imaging for the calibration of robust forage yield and quality estimation models. An unmanned aerial vehicle (UAV) and a hyperspectral imager were used to capture canopy reflections of a grass-legume mixture in the range of 450 nm to 800 nm. Measurements were performed over two years at two locations in Southeast and Central Norway. All images were subject to radiometric and geometric corrections before being processed to ortho-images, carrying canopy reflectance information. The data (n = 707) was split in two, using half the data for model calibration and the remaining half for validation. Several powered partial least squares regression (PPLSR) models were fitted to the reflectance data to estimate fresh (FM) and dry matter (DM) yields, as well as crude protein (CP), dry matter digestibility (DMD), neutral detergent fibre (NDF), and indigestible neutral detergent fibre (iNDF) content. Prediction performance of these models was compared with the prediction performance of simple linear regression (SLR) models, which were based on selected vegetation indices and plant height. The highest prediction accuracies for general models, based on the pooled data, were achieved by means of PPLSR, with relative root-mean-square errors of validation of 14.2% (2550 kg FM ha⁻¹), 15.2% (555 kg DM ha⁻¹), 11.7% (1.32 g CP 100 g⁻¹ DM), 2.4% (1.71 g DMD 100 g⁻¹ DM), 4.8% (2.72 g NDF 100 g⁻¹ DM), and 12.8% (1.32 g iNDF 100 g⁻¹ DM) for the prediction of FM, DM, CP, DMD, NDF, and iNDF content, respectively. None of the tested SLR models achieved acceptable prediction accuracies.

这项研究调查了季节性机载高光谱成像对稳健的草料产量和质量估算模型进行标定的潜力。使用无人飞行器（UAV）和高光谱成像仪来捕获450纳米至800纳米范围内的豆科植物混合物的冠层反射。在挪威东南部和中部的两个地点进行了为期两年的测量。在对所有图像进行正射影像处理之前，所有影像都经过了辐射和几何校正，并带有冠层反射率信息。数据（n = 707）分为两部分，一半用于模型校准，另一半用于验证。将几个幂次偏最小二乘回归（PPLSR）模型拟合到反射率数据，以估计新鲜（FM）和干物质（DM）的产量以及粗蛋白（CP），干物质消化率（DMD），中性洗涤剂纤维（NDF）和不易消化的中性洗涤剂纤维（iNDF）含量。将这些模型的预测性能与基于所选植被指数和株高的简单线性回归（SLR）模型的预测性能进行了比较。基于汇总数据，通用模型的最高预测精度是通过PPLSR实现的，验证的相对均方根误差为14.2％（2550 kg FM ha^-1），15.2％（555千克干物质公顷^-1），11.7％（1.32克CP100克^-1 DM），2.4％（1.71克DMD100克^-1 DM），4.8％（2.72克NDF100克^{- 1} DM）和12.8％（1.32 g iNDF 100 g ^-1 DM）分别用于预测FM，DM，CP，DMD，NDF和iNDF含量。所测试的SLR模型均未达到可接受的预测精度。