About this Research Topic
Vegetation biomass is one of the crucial parameters that control socio-economic behavior. However, biomass depends on several natural phenomena and land-atmosphere interactions. Hence, it is important to monitor and map the Spatio-temporal vegetation dynamics. Multisource remote sensing data can provide complementary information about vegetation morphological characteristics and their biochemical properties with corresponding changes. In particular, Synthetic Aperture Radar (SAR) has gained increasing attention in recent years due to its capability to produce diverse information through polarimetry, polarimetric-interferometry, and tomography.
In recent years, many catastrophic events such as drought, floods, and severe cyclones have damaged large amounts of crop yield within many regions across the globe. Moreover, the recent ongoing natural hazards, such as forest fire and deforestation created an alarming situation in the balance of global carbon content-related issues. This shows the great importance of vegetation mapping using a time- and cost-efficient method to identify the affected areas for better policy making and stabilizing the global socio-economic status.
This Research Topic welcomes submissions on novel physical, machine, and deep learning-based algorithms to monitor and map vegetation characteristics, from theory to application with the added value of SAR and optical data in vegetation characterization.
Topics to be included but not limited to are:
•Vegetation mapping and monitoring using SAR data;
•Vegetation biophysical parameter estimation (e.g., biomass, height, and plant area index);
•Use of SAR, PolSAR, PolInSAR, and TomoSAR data for vegetation characterization;
•Multisource data fusion for disaster mapping related to vegetation;
•Machine learning and deep learning in vegetation species classification and biophysical parameters estimation;
•Estimation of crop yield and global carbon monitoring.
In recent years, many catastrophic events such as drought, floods, and severe cyclones have damaged large amounts of crop yield within many regions across the globe. Moreover, the recent ongoing natural hazards, such as forest fire and deforestation created an alarming situation in the balance of global carbon content-related issues. This shows the great importance of vegetation mapping using a time- and cost-efficient method to identify the affected areas for better policy making and stabilizing the global socio-economic status.
This Research Topic welcomes submissions on novel physical, machine, and deep learning-based algorithms to monitor and map vegetation characteristics, from theory to application with the added value of SAR and optical data in vegetation characterization.
Topics to be included but not limited to are:
•Vegetation mapping and monitoring using SAR data;
•Vegetation biophysical parameter estimation (e.g., biomass, height, and plant area index);
•Use of SAR, PolSAR, PolInSAR, and TomoSAR data for vegetation characterization;
•Multisource data fusion for disaster mapping related to vegetation;
•Machine learning and deep learning in vegetation species classification and biophysical parameters estimation;
•Estimation of crop yield and global carbon monitoring.
Keywords: SAR, PolSAR, PolinSAR, Vegetation, Machine Learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
