This article provides a critical review of progress, challenges, emerging gaps as well as future recommendations on the remote sensing of grass quality during the senescence phenological stage. The study adopted a critical approach and analysed nineteen peer-reviewed articles which were retrieved from Scopus, Web of Science, and Institute of Electrical and Electronics Engineers using key search words. Overall, the results showed that remote sensing has been used to map the quality elements of senescent grass as determined by the concentration of macronutrients, fibre content and biochemical variables such as chlorophyll content. Successful estimation of these variables was achieved using ground-based, airborne, and spaceborne sensors. Nonetheless, this critical review demonstrates that the choice of suitable remote sensing sensor for mapping grass quality attributes during senescence depends on the trade-offs between sensing characteristics, spatial coverage, and data availability. Critical assessment of retrieved literature showed that wavebands located in the red, red-edge, and shortwave infrared regions had the highest sensitivity to senescent grass quality constituents. Remote sensing algorithms reported within the retrieved studies include multivariate analysis techniques, machine learning algorithms and radiative transfer models. Although these are associated with different performances in different settings and vary in their strengths and limitations, it is argued that there is no specific algorithm that is suitable for a specific variable in the context of characterizing grass quality during the senescence period. In this regard, there is a need to assess and ascertain based on factors such as sample size and number of explanatory variables used which affect their accuracy. It is concluded that despite the noted progress in sensor capabilities, the new generation of space borne hyperspectral sensors such as Environmental Mapping and Analysis Program provides untapped prospects to advance the scientific inquiry for remote sensing grass quality during the senescence stage. The review therefore recommends that further research in this field can also consider the utility of such sensor systems, which are readily accessible to enhance the discreet detection of grass quality attributes over space and time. Precise detection of subtle changes in grass nutritional quality during the senescence phenological stage is essential for monitoring forage provisioning ecosystem services.

中文翻译：

---

衰老物候期遥感草质文献综述


本文对衰老物候阶段草质遥感的进展、挑战、新出现的差距以及未来的建议进行了批判性综述。该研究采用了批判性方法，分析了 19 篇同行评审的文章，这些文章是使用关键搜索词从 Scopus、Web of Science 和电气与电子工程师协会检索的。总体而言，结果表明，遥感已被用于绘制衰老草的质量要素，该要素由宏量营养素的浓度、纤维含量和生化变量（如叶绿素含量）决定。这些变量的成功估计是使用地基、机载和星载传感器实现的。尽管如此，这项重要的综述表明，选择合适的遥感传感器来绘制衰老过程中的草地质量属性取决于传感特性、空间覆盖范围和数据可用性之间的权衡。对检索到的文献的批判性评估表明，位于红色、红边和短波红外区域的波段对衰老草质量成分的敏感性最高。检索到的研究中报告的遥感算法包括多变量分析技术、机器学习算法和辐射传输模型。尽管这些与不同环境中的不同表现有关，并且其优势和局限性各不相同，但有人认为，在表征衰老期间草的质量时，没有适合特定变量的特定算法。 在这方面，有必要根据样本量和使用的解释变量数量等因素进行评估和确定，这些因素会影响其准确性。结论是，尽管传感器功能取得了显着进步，但新一代星载高光谱传感器（如环境绘图和分析计划）为推进衰老阶段遥感草质量的科学探索提供了尚未开发的前景。因此，本综述建议该领域的进一步研究也可以考虑这种传感器系统的效用，这些系统很容易获得，以增强对草地质量属性随空间和时间变化的谨慎检测。精确检测衰老物候阶段草营养质量的细微变化对于监测牧草供应生态系统服务至关重要。