Near-surface remote sensing applications for a robust, climate-smart measurement, monitoring, and information system (MMIS)

To reduce its greenhouse gas (GHG) impact, the United States government plans GHG Measurement, Monitoring, and Information System (MMIS) activities to better quantify GHG sources and sinks in natural, forested, and agricultural ecosystems. The national strategy discusses several areas where a robust MMIS can be strengthened by near-surface remote sensing (RS) technology—i.e. non-contact measurement of electromagnetic signals by sensors mounted near the ground, on towers, or on drones. Here, we outline specific applications of near-surface RS for an MMIS, using tools presently available and offering guidance on improvements needed for an expansion of their applications. Near-surface RS can help quantify carbon stocks by assessing vegetation structure and function, and it can inform cross-scale understanding of ecosystem carbon processes and properties. The integration of near-surface RS tools into an MMIS will overcome some limitations and uncertainties of current carbon cycle accounting methods and carbon project implementation. Development of robust, standardized near-surface RS systems can be accomplished through transdisciplinary partnerships among government agencies, academics, land managers, and the private sector. The result will hasten achievement of MMIS objectives of improved bottom-up and top-down GHG estimation and improved accessibility and standardization of GHG data and measurements.

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Publication Details

Journal
Carbon Management
Published
2025-02-20
DOI
https://doi.org/10.1080/17583004.2025.2465361
Citations
2
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
3.43

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article

Near-surface remote sensing applications for a robust, climate-smart measurement, monitoring, and information system (MMIS)

Matthew P. Dannenberg, Mallory L. Barnes, Zoe Pierrat, John A. Gamon et al.
2 citations
Carbon Management
Remote Sensing in Agriculture
3.43
article

Near-surface remote sensing applications for a robust, climate-smart measurement, monitoring, and information system (MMIS)

Matthew P. Dannenberg, Mallory L. Barnes, Zoe Pierrat, John A. Gamon, Benjamin R. K. Runkle, Troy S. Magney, Christopher J. Still, William Woodgate, Charles D. Southwick
article en
2 citations

Abstract

To reduce its greenhouse gas (GHG) impact, the United States government plans GHG Measurement, Monitoring, and Information System (MMIS) activities to better quantify GHG sources and sinks in natural, forested, and agricultural ecosystems. The national strategy discusses several areas where a robust MMIS can be strengthened by near-surface remote sensing (RS) technology—i.e. non-contact measurement of electromagnetic signals by sensors mounted near the ground, on towers, or on drones. Here, we outline specific applications of near-surface RS for an MMIS, using tools presently available and offering guidance on improvements needed for an expansion of their applications. Near-surface RS can help quantify carbon stocks by assessing vegetation structure and function, and it can inform cross-scale understanding of ecosystem carbon processes and properties. The integration of near-surface RS tools into an MMIS will overcome some limitations and uncertainties of current carbon cycle accounting methods and carbon project implementation. Development of robust, standardized near-surface RS systems can be accomplished through transdisciplinary partnerships among government agencies, academics, land managers, and the private sector. The result will hasten achievement of MMIS objectives of improved bottom-up and top-down GHG estimation and improved accessibility and standardization of GHG data and measurements.

Carbon ManagementVol. 16(1)
University of Nebraska–Lincoln (US), University of Iowa (US), Oregon State University (US), Jet Propulsion Laboratory (US), The University of Queensland (AU), University of Arkansas System (US), Indiana University Bloomington (US), Plant (United States) (US), Indiana University – Purdue University Indianapolis (US), University of Arkansas at Fayetteville (US), University of California, Davis (US)
National Science Foundation, U.S. Department of Agriculture, Science Mission Directorate, Australian Research Council
Openalex Percentile: Top 10%
Remote Sensing in Agriculture
3.43
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