Satellite-Based Radiative Transfer Modeling for Vegetation
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 95
Editors
Interests: multi-angle remote sensing technique to retrieve terrestrial vegetation parameters such as leaf area index and fractional vegetation cover; modeling of radiative transfer in vegetation canopy and field measurement based on unmanned automatic vehicle
Special Issues, Collections and Topics in MDPI journals
Interests: vegetation remote sensing; clumping index; validation and retrieval; topographic correction; canopy BRDF modeling
Special Issues, Collections and Topics in MDPI journals
Interests: 3D radiative transfer modeling of land surfaces and the atmosphere; 3D energy balance modeling of the vegetation canopy; the study of forests and crops by remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: agricultural engineering; remote sensing; agronomy; vegetaion trait retrieval
Special Issue Information
Dear Colleagues,
As a critical frontier issue in remote sensing, satellite-based radiative transfer modeling (RTM) provides the essential physical framework to translate Earth observations into accurate biophysical and biochemical canopy traits. Historically, 1D RTMs (e.g., the classic SAIL model) and geometric-optical models (e.g., GORT, 5SCALE) have laid a robust and invaluable foundation for broad-scale vegetation monitoring. However, as the demand to characterize highly heterogeneous ecosystems grows, the field is rapidly advancing toward sophisticated 3D RTMs (e.g., DART, LESS, and Eradiate) in order to capture greater structural realism. Today, a critical scientific gap remains in synergizing the efficiency of traditional analytical models with the detailed geometric accuracy of 3D RTMs, alongside the need to integrate these physical frameworks into broader cross-disciplinary research.
In this Special Issue, we aim to collect original research and comprehensive reviews that push the boundaries of satellite-based RTMs, and therefore seek studies bridging the gap between traditional remote sensing and disciplines such as plant physiology, structural biology, ecology, and climate science. We welcome innovative approaches to the development and application of RTMs, spanning from classic analytical frameworks to advanced 3D simulation platforms, as well as the development of hybrid models that seamlessly combine RTMs with machine learning. Furthermore, we strongly encourage research exploring scale-coupling (from leaf to canopy and atmosphere) and the physical integration of multi-source data to evaluate critical ecosystem dynamics across varying spatial scales.
Topics of interest include, but are not limited to, the following:
- Advances in and integration of 1D and 3D canopy RTMs;
- Development of leaf RTMs and the seamless coupling of leaf, canopy, and atmospheric RTMs;
- Modeling of canopy optical reflectance, sun-induced chlorophyll fluorescence (SIF), thermal infrared radiation, microwave scattering, and LiDAR waveforms;
- Development of hybrid models and physics-guided artificial intelligence (AI) modeling, integrating physical mechanisms with machine/deep learning for interpretable and highly generalizable parameter inversion;
- Robust mathematical algorithms and solutions for high-dimensional, ill-posed inversion problems in quantitative remote sensing;
- Inversion and retrieval of vegetation structural and biogeochemical parameters from multi-scale, multi-source satellite data;
- Coupling of RTMs with plant physiology and hydrology to assess ecosystem health (e.g., forest degradation).
We look forward to receiving your impactful contributions.
Dr. Xihan Mu
Dr. Jun Geng
Dr. Yingjie Wang
Dr. Junxiong Zhou
Dr. Weihua Li
Guest Editors
Manuscript Submission Information
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Keywords
- radiative transfer modeling (RTM)
- 3D canopy architecture
- vegetation parameter inversion
- multi-dimensional remote sensing
- physics-guided AI
- LiDAR and optical synergy
- geometric-optical models
- ecosystem health monitoring
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