The retrieval of soil surface parameters, in particular soil moisture and roughness, based on Synthetic Aperture Radar (SAR) data, has been the subject of a large number of studies, of which results are available in the scientific literature. However, although refined methods based on theoretical/analytical scattering models have been proposed and successfully applied in experimental studies, at the operative level very simple, empirical models with a number of adjustable parameters are usually employed. One of the reasons for this situation is that retrieval methods based on analytical scattering models are not easy to implement and to be employed by non-expert users. Related to this, commercially and freely available software tools for the processing of SAR data, although including routines for basic manipulation of polarimetric SAR data (e.g., coherency and covariance matrix calculation, Pauli decomposition, etc.), do not implement easy-to-use methods for surface parameter retrieval. In order to try to fill this gap, in this paper we present a user-friendly computer program for the retrieval of soil surface parameters from Polarimetric Synthetic Aperture Radar (PolSAR) imageries. The program evaluates soil permittivity, soil moisture and soil roughness based on the theoretical predictions of the electromagnetic scattering provided by the Polarimetric Two-Scale Model (PTSM) and the Polarimetric Two-Scale Two-Component Model (PTSTCM). In particular, nine different retrieval methodologies, whose applicability depends on both the used polarimetric data (dual- or full-pol) and the characteristics of the observed scene (e.g., on its topography and on its vegetation cover), as well as their implementation in the Interactive Data Language (IDL) platform, are discussed. One specific example from Germany's Demmin test-site is presented in detail, in order to provide a first guide to the use of the tool. Obtained retrieval results are in agreement with what was expected according to the available literature.
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http://dx.doi.org/10.3390/s20185085 | DOI Listing |
Nat Commun
December 2024
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China.
Small-scale continuum robots hold promise for interventional diagnosis and treatment, yet existing models struggle to achieve small size, precise steering, and visualized functional treatment simultaneously, termed an "impossible trinity". This study introduces an optical fiber-based continuum robot integrated imaging, high-precision motion, and multifunctional operation abilities at submillimeter-scale. With a slim profile of 0.
View Article and Find Full Text PDFJ Phys Chem Lett
December 2024
Macao Institute of Materials Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau SAR 999078, China.
The powerful data processing and pattern recognition capabilities of machine learning (ML) technology have provided technical support for the innovation in computational chemistry. Compared with traditional ML and deep learning (DL) techniques, transformers possess fine-grained feature-capturing abilities, which are able to efficiently and accurately model the dependencies of long-sequence data, simulate complex and diverse chemical spaces, and explore the computational logic behind the data. In this Perspective, we provide an overview of the application of transformer models in computational chemistry.
View Article and Find Full Text PDFSci Rep
December 2024
Earth Observatory of Singapore, Nanyang Technological University, Singapore, 639798, Singapore.
Coastal populations are susceptible to relative sea-level (RSL) rise and accurate local projections are necessary for coastal adaptation. Local RSL rise may deviate from global mean sea-level rise because of processes such as geoid change, glacial isostatic adjustment (GIA), and vertical land motion (VLM). Amongst all factors, the VLM is often inadequately estimated.
View Article and Find Full Text PDFAm J Prev Med
December 2024
School of Public Health, University of Hong Kong, Hong Kong SAR, China.
Introduction: To assess 10-year trends (2010-2020) in household secondhand smoke exposure (SHSe) from inside their own homes and from their neighbours in Hong Kong adolescents and analyse changes by socioeconomic status (SES).
Methods: Data from the 2010 to 2020 School-based Smoking Survey among Students (total responses were 228,623) were analysed in 2023. Weighted prevalence and temporal trends of SHSe were calculated across years.
Adv Sci (Weinh)
December 2024
Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Digital PCR (dPCR) has transformed nucleic acid diagnostics by enabling the absolute quantification of rare mutations and target sequences. However, traditional dPCR detection methods, such as those involving flow cytometry and fluorescence imaging, may face challenges due to high costs, complexity, limited accuracy, and slow processing speeds. In this study, SAM-dPCR is introduced, a training-free open-source bioanalysis paradigm that offers swift and precise absolute quantification of biological samples.
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