Multifrequency microwave models promote land surface monitoring

The new study introduces a local land-active passive microwave radiation transport modeling platform (CLAP). This is an emissions model designed to revolutionize unified multi-frequency microwave scattering and land surface monitoring. This cutting-edge platform combines active and passive microwave signals to provide a potentially accurate simulation of soil moisture and vegetation conditions.

By incorporating a sophisticated interaction model of soil and vegetation, CLAP may address key limitations of existing remote sensing technologies, allowing for improved land monitoring accuracy. This study presents the ability of CLAP to improve microwave signal simulations, particularly at high frequencies, and shows significant advances in ecosystem management and climate change research.

Microwave remote sensing is essential for land monitoring and provides important insights into soil moisture and vegetation health by measuring microwave radiation and scattering emissions and scattered at the surface. However, current models rely heavily on zero-order radiative transfer theory and empirical assumptions, often overlooking dynamic changes in vegetation and soil properties (structure, moisture, temperature). These limitations result in discrepancies and reduced accuracy across different frequencies and polarizations.

Given these challenges, there is an urgent need for more sophisticated research into the scattering and ejection mechanisms of multi-frequency microwave signal to improve the accuracy and reliability of remote sensing technologies.

A team of researchers from Twente University has published a paper in the Journal of Remote Sensing. We have introduced the Community Land Active Passive Microwave Radiation Mobility Modeling Platform (CLAP). (ATS+AIEM) and Vegetation Scattering (TVG) models. CLAP incorporates appropriate vegetation structure, dynamic vegetation water content (VWC), and temperature changes, which significantly improves existing technologies.

Furthermore, CLAP reveals the frequency-dependent nature of the optical depth of grasslands, highlighting the large effect of vegetation temperature on high-frequency signals, providing new insights for more accurate vegetation and soil monitoring.

The core strength of applause lies in the detailed modelling of soil and planting. The team used long-term in situ observations from the MAQU site, including microwave signals, soil moisture, temperature profiles, and vegetation data, to drive applause and assess the model’s performance. The result is simulating backscattering of grasslands with X and C bands with RMSE values ​​of 1.8 and 1.9 dB, respectively, compared to 3.4 and 3.0 dB from disk parameterization, with applause for cylinder parameterization of vegetation representation. It was shown that it was done.

This study found that vegetation temperature variations significantly affect the daily changes of high-frequency signals, but that changes in vegetation water content primarily affect low-frequency signals. For example, in the C-band, vegetation temperature variation had a significant effect on signal changes (correlation coefficient R is 0.34), while in the S-band, vegetation water content had a stronger effect (R of R) R). These findings highlight the importance of dynamic vegetation and soil properties in microwave signal scattering and emission processes, and applause is accurately reflected.

“The crap platform represents a major advancement in microwave remote sensing. By incorporating appropriate vegetation structures, dynamic vegetation, soil moisture content and temperature into the model,” said Chief Investigator Dr. Hong Zao. CLAP provides a more accurate representation of microwaves. Signal scattering and emission processes significantly improve the ability to monitor vegetation and soil conditions, and more reliable data for ecosystem management and climate change research Provides.

The team utilized extensive IN Situ data and satellite microwave observations from the MAQU site. These comprehensive datasets allowed researchers to rigorously evaluate the performance of CLAP across different frequencies and polarizations, ensuring their accuracy and reliability. CLAP development opens new possibilities for the future of microwave remote sensing. This technology can be integrated into future satellite missions such as CIMR and Rose-L to increase the accuracy of soil moisture and vegetation monitoring.

Additionally, CLAP can be incorporated into the data assimilation framework to provide more accurate input for the surface model. The widespread application of this technology is committed to having a significant impact on global environmental surveillance, agricultural production and climate change research, supporting global sustainable development efforts.