Remote Sensing for Surface Biophysical Parameter Retrieval (2nd Edition): Cross-Scale Vegetation Biochemical and Structural Retrieval for Carbon and Ecosystem Monitoring

Dear Colleagues,

The quantitative retrieval of surface biophysical parameters remains a critical frontier in Earth observation research. Robust estimation of vegetation biochemical and structural characteristics, such as leaf chlorophyll content, pigment composition, nitrogen concentration, leaf area index (LAI), clumping index, canopy architecture, and above-ground biomass, underpins the accurate assessment of photosynthetic capacity, terrestrial carbon cycling, ecosystem productivity, and vegetation–climate feedbacks. While recent satellite missions equipped with red-edge, hyperspectral, thermal infrared, microwave, and LiDAR sensors have substantially improved the scope and quality of observations, significant methodological challenges continue to hinder the reliability of retrievals. Persistent uncertainties stem from cross-scale mismatches between leaf-level optical properties and canopy-level reflectance, structural heterogeneity, multiple-scattering effects, mixed-pixel contamination, terrain-induced anisotropy, and issues of scale representativeness. These uncertainties affect not only the retrieval products themselves but also the broader applications that depend on them, including carbon estimation, biomass assessment, ecosystem health evaluation, and climate change attribution. Physically based methods, especially radiative transfer modeling and spectral invariant theory, offer a strong theoretical basis for separating biochemical and structural information. In contrast, hybrid inversion frameworks that integrate physical principles with machine learning offer new opportunities for efficient, scalable retrieval.

Nevertheless, the systematic incorporation of cross-scale theory, structural correction, validation standards, and uncertainty propagation into operational product systems remains limited. This Special Issue seeks to advance the theoretical, methodological, and operational development of vegetation biochemical and structural parameter retrieval, with explicit attention to its relevance for carbon-cycle analysis, biomass estimation, ecosystem quality assessment, and human–environment interactions. Particular emphasis will be placed on innovative retrieval frameworks, multi-source and multi-scale integration, uncertainty quantification, rigorous validation, and applications that enhance the practical value of remote sensing products for ecosystem management and climate science.

• Physically grounded radiative transfer modeling and structural correction
• Cross-scale consistency from leaf to canopy to satellite observation
• Rigorous validation theory and standardized product assessment
• Uncertainty quantification and propagation into carbon and biomass estimates
• Multi-sensor integration for improved structural–biochemical coupling
• Long-term vegetation dynamics under climate variability and human activities
• Ecosystem quality change and land-use transition analysis supported by validated biophysical products
• Contributions that bridge methodological innovation with ecosystem and carbon applications at regional to global scales are especially encouraged.

Topics of Interest

• Radiative transfer modeling and spectral invariant approaches for vegetation biochemical and structural retrieval
• Coupled estimation of chlorophyll, LAI, clumping index, canopy architecture, and above-ground biomass
• Hybrid radiative transfer and physics-constrained machine learning inversion frameworks
• Validation systems and uncertainty propagation in chlorophyll, LAI, and biomass products
• Decametric- and high-resolution vegetation biochemical and structural product generation
• Carbon flux estimation and gross primary productivity sensitivity to retrieval uncertainty
• Vegetation dynamics and ecosystem quality change under climate variability
• Land-use and land-cover transitions and their influence on biophysical parameters
• Human settlement expansion, urban–rural transformation, and vegetation–thermal interactions
• Climate-change impacts on vegetation structure, biochemical status, and carbon storage

Original research and review articles providing methodological innovation, cross-platform validation, or global and regional product development are encouraged.

Dr. Faisal Mumtaz
Dr. Shahid Naeem
Dr. Chang Liu
Guest Editors

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