PDF(Pubmed)
Abstract：
The carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.
摘要：
碳（C）和氮（N）比率是牧场养分利用和限制的关键指标。为了了解草食动物的分布和放牧方式，草的质量和数量信息很重要。在异构环境中，遥感提供了一个及时的，经济,以及在不同时空尺度上评估叶面生化比的有效方法。因此，这项研究概述了遥感技术的进步，局限性,以及绘制牧场C：N比的新机会。具体来说,本文重点研究了多光谱和高光谱传感器，并研究了它们的特性，吸收特征，经验和物理方法，以及预测草地C：N比的算法。文献表明，确定草地的C：N比与遥感技术的发展不符。因此，使用具有改进的光谱和空间属性的先进和免费可用的传感器，如Sentinel2和Landsat8/9，以及复杂的算法，可能会提供新的机会来估计区域尺度上的草原C:N比率，尤其是在发展中国家。近红外光谱波段，短波红外线,红色,和红色边缘被识别以预测植物中的C:N比率。根据最近的多光谱卫星图像开发的新指数，例如,前哨2由尖端算法辅助，可以提高叶面生化比的估计。因此,这项研究建议，未来的研究应采用新的卫星技术，以及机器学习算法的最新发展，以改善草地C：N比的映射。
