Progress in Remote Sensing of Grass Senescence: A Review on the Challenges and Opportunities

Abstract

Grass senescence estimation in rangeland environments is particularly important for monitoring the conditions of forage quality and quantity. During senescence, grasses lose their nutrients from the leaves to the root systems and thereby affecting forage productivity. Numerous studies on the remote sensing of grasslands have been conducted during the senescent phenological stage. However, despite the efforts made in previous remote sensing studies on grass senescence, its role in estimating grass senescence is rudimentary. More so, the strengths and limitations presented by the newly developed remote sensing instruments in grass senescence estimation are not well documented. This work therefore provides a detailed overview on the progress of remote sensing applications in characterizing grass senescence. The review further highlights the challenges and possible opportunities presented by these techniques. Overall, the review indicates that the available studies on remotely sensed grass senescence applications are focused on understanding biophysical and biochemical properties and these studies identify the Leaf Area Index (LAI), biomass and chlorophyll content, among others, as the key indicators of grass senescence. Nonetheless, recent scientific research highlights a mismatch between studies on the grass senescence and the development in remote sensing technologies. The use of sophisticated and robust time-series analysis techniques like TIMESAT together with improved sensing characteristics from the new generation sensors seem to present new opportunities for the optimal quantification of grass senescence at resolutions complementary to the spatial extents of the rangelands. We therefore recommend further research in this field through the adoption of new satellite technologies and advanced spatial data analytics to enhance the monitoring of rangeland resources.

Estimation of Biomass and N Uptake in Different Winter Cover Crops from UAV-Based Multispectral Canopy Reflectance Data

“…The amount of destructive senescent plant material sampled explained a part of the scattering effects in the spectral reflectance. The spectra of the yellow leaves differ from green leaves, as reflection in red and green light increases and decreases in the range of NIR [25,46]. The influence of senescent leaves on the estimates was also shown by Di Bella et al [47] for ND red in a canopy of Italian ryegrass.…”

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

“…In studying fuel types, grass species have not been looked at only as an ecological factor but as the most flammable fuel type [40,41]. Notably, the NDVI has been widely employed to estimate vegetation phenology as well as its quality and growth condition [43][44][45]. NDVI, serving as an index of vegetation growth and coverage, finds extensive use in describing spatio-temporal characteristics of land use and land cover (LULC), including percent vegetation coverage [44][45][46][47].…”

“…Notably, the NDVI has been widely employed to estimate vegetation phenology as well as its quality and growth condition [43][44][45]. NDVI, serving as an index of vegetation growth and coverage, finds extensive use in describing spatio-temporal characteristics of land use and land cover (LULC), including percent vegetation coverage [44][45][46][47]. However, the NDVI cannot differentiate between trees, shrubs, and grass because of their similar spectral characteristics [22,40].…”