Vegetation water content serves as a vital indicator of plant water availability, water stress, and ecosystem health. It significantly influences plant physiological processes such as stomatal conductance, transpiration, photosynthesis, and respiration. Additionally, vegetation water content plays a crucial role in forest fire risk assessment. Remote sensing offers a valuable tool for vegetation water monitoring, employing techniques like microwave, optical, and GNSS-R (global navigation satellite systems reflectometry). However, limitations exist. Passive microwave remote sensing suffers from coarse resolution, and cloud cover can hinder optical remote sensing data acquisition. Furthermore, GNSS-R techniques-based vegetation water monitoring has not been widely validated. A comprehensive comparison and understanding of the advantages of these techniques remain elusive. This Research Topic seeks high-quality articles addressing the use and application of remote sensing for improved vegetation water monitoring.

This Research Topic welcomes articles that address key areas such as enhancing the accuracy of vegetation water content through the development of biophysical or deep learning models. It also includes conducting comparative evaluations of various remote sensing techniques for quantifying vegetation water content against in-situ measurements as the reference. Another focus is enhancing our understanding of spatiotemporal dynamics in vegetation water content, including seasonal and inter-annual variations across diverse ecosystems and regions. We are particularly interested in exploring applications of vegetation water content datasets in ecological and climate change analyses, such as drought or fire risk assessment, and phenology studies. Additionally, investigating the coupling and decoupling mechanisms between vegetation carbon and water cycles is a key area of interest.

We welcome contributions covering all aspects of Vegetation Water Monitoring. Some topics of particular interest may include (but are not limited to):

• Vegetation water content estimation;

• Microwave remote sensing;

• Optical remote sensing;

• GNSS-R;

• Deep learning.

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Frontiers in Remote Sensing

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