Remote Sensing Observation Methods for Leaf Area Index (LAI) in Mountainous Regions
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 98
Special Issue Editors
Interests: quantitative remote sensing; canopy radiative transfer modeling
Special Issues, Collections and Topics in MDPI journals
Interests: quantitative remote sensing; carbon cycle; plant photosynthesis; aboveground biomass; spectral observation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Leaf Area Index (LAI) is a critical parameter in understanding the Earth's ecosystem, as it plays a key role in regulating the exchange of energy, water, and carbon between the land surface and the atmosphere. However, estimating LAI in mountainous regions is particularly challenging due to the complex terrain, heterogeneous land cover, and limited accessibility. Remote sensing technologies have shown great potential in estimating LAI over large areas, but the accuracy and reliability of these methods in mountainous regions are still uncertain. The importance of LAI estimation in these regions lies in its application in understanding and predicting the impacts of climate change, land degradation, and natural disasters.
This Special Issue focuses on the latest advances in remote sensing observation methods for LAI estimation in mountainous regions. We seek contributions that investigate the use of cutting-edge remote sensing technologies, such as unmanned aerial vehicle (UAV) photogrammetry, airborne LiDAR, and satellite-based multi-spectral and hyperspectral imaging. Additionally, we encourage submissions that explore the integration of multi-source data, machine learning techniques, and novel methods for addressing the challenges of LAI estimation in complex terrain. By bringing together researchers and experts in this field, this Special Issue aims to improve our understanding of LAI dynamics in mountainous regions and to develop more effective methods for monitoring and managing these critical ecosystems. Articles may address, but are not limited, to the following topics:
- Ground-based LAI estimation;
- Satellite remote sensing-based LAI retrieval;
- Radiative transfer modeling;
- Machine and deep learning for LAI estimations;
- LAI time series analysis;
- New theory and technology for LAI estimation.
Dr. Weiliang Fan
Dr. Qian Zhang
Dr. Meihong Fang
Guest Editors
Manuscript Submission Information
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Keywords
- leaf area index
- canopy structure
- ground-based observation
- satellite-based remote sensing
- remote sensing inversion
- ground-based remote sensing platform
- remote sensing modeling
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