School of Technology, Environments & Design, University of Tasmania-Geography and Spatial Sciences, Hobart, TAS 7001, Australia.
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands.
Sensors (Basel). 2020 Dec 15;20(24):7192. doi: 10.3390/s20247192.
The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass () canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.
光谱数据的应用被视为一种快速且无损的方法，能够监测牧场生物量。尽管这项技术具有很大的潜力，但终端用户和传感器制造商都不确定在实际操作场景中所需的传感器规格和可实现的精度。本研究提出了一种简单的参数方法，能够从澳大利亚和荷兰两年内采集的多光谱数据中准确地反演黑麦草（Lolium perenne）冠层的高光谱特征。所反演的高光谱数据被用于生成最优指数和连续谱去除光谱特征，这些特征在科学文献中都有报道。为了进行性能比较，这两种模拟特征以及一组目前从原始波段值派生的植被指数都被用作随机森林算法的输入，并比较了这两种方法的精度。我们的结果表明，当在交叉验证和空间交叉验证中分别评估时，这两组特征的精度相似（均方根误差（RMSE）≈490 和 620 kg DM/ha）。这些结果表明，对于仅从冠层顶部反射率（550 至 790nm 范围内）获取牧场生物量而言，表现更好的方法并不依赖于使用高光谱或比当前传感器中可用的更多波段。
