Retrieving Leaf Area Index Using Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".
Deadline for manuscript submissions: 20 January 2025 | Viewed by 11290
Special Issue Editors
Interests: remote sensing of vegetation; terrestrial carbon cycle; agroecosystem modeling; machine learning
Interests: multi-sensor data fusion; crop phenology; biophysical parameter retrieval; time series analysis; near-real-time mapping
Special Issues, Collections and Topics in MDPI journals
Interests: multi-sensor data fusion; radiometric harmonization; machine learning; precision agriculture; satellite-based retrieval of vegetation biophysical properties and functional traits; satellite-based water use and productivity estimation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Leaves are the primary sites for energy, carbon, and water exchange between plants and the atmosphere. Leaf Area Index (LAI), defined as the amount of single-sided leaf area per unit of ground area, is an essential variable for modeling and understanding climate–ecosystem interactions. Remote sensing techniques are used to retrieve LAI at various spatial scales. For decades, remote-sensing-derived LAI data products have boosted scientific advancements in global vegetation change, agroecosystem monitoring, and earth system modeling. Important applications such as climate change mitigation, agricultural sustainability, and hydrological forecasting demand further progress of remotely sensed LAI towards higher accuracy, higher spatial–temporal resolution, and enhanced continuity.
This Special Issue calls for recent advances in the science and technology of using remote sensing to estimate LAI. Topics include but are not limited to: proximal/ground measurements, radiative transfer modeling and theoretical formulation, exploitation of emerging platforms such as UAV and SmallSat, utilization of optical/hyperspectral/LiDAR images, multi-source data fusion, novel machine/deep learning techniques, hybrid modeling, uncertainty quantification, and product development/description/validation. Review and commentary papers are also welcome.
Dr. Yanghui Kang
Dr. Feng Gao
Dr. Rasmus Houborg
Guest Editors
Manuscript Submission Information
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Keywords
- leaf area index
- remote sensing
- in situ sensor
- radiative transfer modeling
- LiDAR
- UAV
- smallsat
- data fusion
- machine learning
- validation
- uncertainty
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