A critical review of literature on remote sensing grass quality during the senescence phenological stage

Abstract

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.