ReviewDiscover sensors2025
Unlocking the synergistic potential of sensor technologies in grassland research.
Review in Discover sensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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. Supplementary Information: The online version contains supplementary material available at 10.1007/s44397-025-00020-2.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.