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News — Microwave remote sensing is essential for land monitoring, providing crucial insights into soil moisture and vegetation health by measuring the microwave radiation and backscatter emitted and scattered by the surface. However, current models rely heavily on zeroth-order radiative transfer theory and empirical assumptions, often overlooking dynamic changes in vegetation and soil properties (structure, moisture and temperature). These limitations result in inconsistencies and reduced accuracy across different frequencies and polarizations. Given these challenges, there is an urgent need for more refined research into the scattering and emission mechanisms of multi-frequency microwave signals to improve the precision and reliability of remote sensing technologies.
On January 9, 2025, a team of researchers from the University of Twente published a in the , introducing the Community Land Active Passive Microwave Radiative Transfer Modelling platform (CLAP) a multi-frequency microwave scattering and emission model, which integrates advanced soil surface scattering (ATS+AIEM) and vegetation scattering (TVG) models. CLAP incorporates appropriate vegetation structure, dynamic vegetation water content (VWC) and temperature changes, significantly improving upon existing technologies. Additionally, CLAP uncovers the frequency-dependent nature of grassland optical depth and highlights the significant impact of vegetation temperature on high-frequency signals, offering new insights for more accurate vegetation and soil monitoring.
The core strength of CLAP lies in its detailed modeling of soil and vegetation components. The team used long-term in situ observations from the Maqu site, including microwave signals, soil moisture, temperature profiles, and vegetation data, to drive CLAP and evaluate the model performance respectively. Results showed that during the summer, CLAP with cylinder parameterization for vegetation representation simulated grassland backscatter at X-band and C-band with RMSE values of 1.8 dB and 1.9 dB, respectively, compared to 3.4 dB and 3.0 dB from disc parameterization. The study also discovered that vegetation temperature variations significantly affect high-frequency signal diurnal changes, while vegetation water content changes primarily influence low-frequency signals. For example, at C-band, vegetation temperature fluctuations had a greater impact on signal changes (correlation coefficient R of 0.34), while at S-band, vegetation water content had a stronger influence (R of 0.46). These findings underscore the importance of dynamic vegetation and soil properties in microwave signal scattering and emission processes, which CLAP accurately reflects.
Dr. Hong Zhao, the lead researcher, commented, "The CLAP platform represents a major advancement in microwave remote sensing. By incorporating appropriate vegetation structure, dynamic vegetation and soil water content and temperature into the model, CLAP offers a more accurate representation of microwave signal scattering and emission processes. This innovation will significantly enhance our ability to monitor vegetation and soil conditions, providing more reliable data for ecosystem management and climate change research."
The team utilized extensive in situ data from the Maqu site as well as satellite microwave observations. These comprehensive datasets allowed the researchers to rigorously assess CLAP's performance across various frequencies and polarizations, ensuring its accuracy and reliability. The development of CLAP opens new possibilities for the future of microwave remote sensing. This technology can be integrated into upcoming satellite missions such as CIMR and ROSE-L to enhance the precision of soil moisture and vegetation monitoring. Additionally, CLAP can be incorporated into data assimilation frameworks to provide more accurate inputs for land surface models. The widespread application of this technology promises to have a profound impact on global environmental monitoring, agricultural production, and climate change research, supporting sustainable development efforts worldwide.
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Funding information
This work is carried out under the MINERVA: MIcrowaves for a New Era of Remote sensing of Vegetation for Agricultural monitoring project funded by the Netherlands Organization for Scientific Research (NWO) Partnerships for Space Instruments & Applications Preparatory Programme (PIPP) (KNW19001), NWO SMAP Freeze-Thaw project (ALW-GO/14-29) and the European Space Agency and the Ministry of Science and Technology of the P.R. China (ESA-MOST) Dragon project.
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The , an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.
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