Pasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass ( Lolium perenne ) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha −1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha −1 ). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.

中文翻译：

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比较估计多年生黑麦草生物量的方法：冠层高度和光谱植被指数

牧场管理高度依赖于准确的生物量估算。通常，此类活动被忽略，因为当前的方法耗时且经常被认为不准确。相反，光谱数据是一种很有前途的技术，可以自动化并提高估计的准确性和精确度。历史上，光谱植被指数已被广泛采用并提出了大量的数字。选择最佳指数或令人满意的指数子集以准确估计生物量并非易事，并且会影响新传感器的设计。本研究旨在比较基于冠层的技术（升板计）与光谱植被指数。它检查了 97 个植被指数和 11 个，026 种归一化比率指数组合与不同回归技术对 1 年期间收集的多年生黑麦草（ Lolium perenne ）的 900 个牧场生物量数据点进行配对。分析表明，基于冠层的技术优于标准归一化差异植被指数 (∆, 115.1 kg DM ha -1 RMSE)，相当于表现最佳的归一化比率指数，但不如使用不同回归部署的四个选定植被指数准确技术（最大 ∆, 231.1 kg DM ha -1 ）。在采用四个选定的植被指数时，随机森林是性能最好的回归技术，其次是支持向量机、多元自适应回归样条和线性回归。通过模型堆叠提高了估计精度。总之，