Unlocking the synergistic potential of sensor technologies in grassland research

TL;DR

This paper reviews how combining different sensor technologies can improve grassland research and management.

Contribution

The paper introduces a framework organizing grassland sensor technologies into four pillars and highlights quantum-based approaches.

Findings

Sensor technologies like hyperspectral and thermal sensors are vital for grassland monitoring.

Integrating sensors with digital platforms offers holistic grassland management solutions.

Quantum-based sensors show promise for advancing grassland condition assessments.

Abstract

In recent decades, the rapid advancement of sensor technologies has revolutionized research in grassland ecosystems. A wide array of sensor technologies has significantly enhanced field-based studies, contributing to a deeper understanding of grassland conditions. This review examines the synergistic potential of integrating remote sensing (RS) with various sensor technologies, such as hyperspectral, multispectral, thermal, and electrochemical sensors, highlighting their crucial role in understanding field conditions. The bibliometric analysis shows the chronological change in the usage and purposes of various RS technologies. Additionally, integrating these technologies into digital platforms offers holistic solutions for grassland management, benefiting farmers and researchers alike. A key contribution of this review is the organization of the grassland sensor landscape into four complementary pillars—structural, spectral, quantum, and proximal/physiological—and their integration with remote sensing, model–data assimilation, and digital platforms. We also synthesize emerging quantum-based approaches (e.g., cosmic-ray neutron sensing, neutron probes, graphene quantum-dot devices) and discuss their practical relevance for grassland monitoring.

The online version contains supplementary material available at 10.1007/s44397-025-00020-2.

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