Remote Sensing Grassland Productivity Attributes: A Systematic Review

A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web of Science, Scopus, and DirectScience databases) showed a trend in the choice of the sensors, and the approaches to use are largely dependent on the extent of monitoring and assessment. Notably, all the reviewed articles demonstrate the growing demand for high-resolution sensors, such as hyperspectral scanners and computationally efficient image-processing techniques for the high prediction accuracy of GP at various scales of application. Further research is required to attract the synthesis of optical and radar data, multi-sensor data, and the selection of appropriate techniques for GP prediction at different scales. Mastering and listing major uncertainties associated with different algorithms for the GP prediction and pledging to reduce these errors are critical.

Automated summary

One-third of the Earth's land is grasslands used mainly for forage, and efforts are being made to develop tools to estimate grassland productivity (GP) in relation to climate change. Grassland productivity is an important indicator of ecosystem functioning and is commonly assessed using proxies such as aboveground biomass, leaf area index, and chlorophyll content. Remote sensing techniques, particularly high-resolution sensors, play a crucial role in calculating these proxies. A systematic review of published articles showed a growing demand for high-resolution sensors and computational image-processing techniques for accurate GP prediction at different scales. Future research should focus on integrating optical and radar data, selecting appropriate techniques for GP prediction, and reducing uncertainties associated with different algorithms.