Microwave remote sensing of soil moisture, above ground biomass and freeze-thaw dynamic: Modeling and empirical approaches
DOI:
https://doi.org/10.64700/mmm.54Keywords:
Microwave remote sensing, essential climate variables, probabilistic cellular automata, neural network operators, Bayesian inversionAbstract
Human actions have accelerated changes in global temperature, precipitation patterns, and other critical Earth systems. Key markers of these changes can be linked to the dynamic of Essential Climate Variables (ECVs) and related measures, such as Soil Moisture (SM), Above Ground Biomass (AGB), and Freeze-Thaw (FT) Dynamics. ECVs are crucial for understanding global climate changes, including hydrological and carbon cycles. Moreover, monitoring ECVs helps to validate climate models and inform policy decisions. Monitoring activities can be carried out at a global scale by using technologies like microwave remote sensing. However, other than proper technological developments, the study of ECVs requires suitable theoretical retrieval tools, which leads to the solutions of inverse problems. In this survey, we analyze and summarize the main retrieval techniques available in the literature for SM, AGB, and FT, performed on data collected with microwave remote sensing sensors. Furthermore, we present the project RETINA (REmote sensing daTa INversion with multivariate functional modeling for essential climAte variables characterization), recently funded by the European Union under the Italian National Recovery and Resilience Plan of NextGenerationEU, under the Italian Ministry of University and Research. The main goal of RETINA, is to create innovative techniques for analyzing data generated by the interaction of electromagnetic waves with the Earth’s surface, applying theoretical insights to address real-world challenges.
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Copyright (c) 2025 Laura Angeloni, Domenico Daniele Bloisi, Paolo Burghignoli, Davide Comite, Danilo Costarelli, Michele Piconi, Anna Rita Sambucini, Alessio Troiani, Alessandro Veneri
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