相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article
Environmental Sciences
Raphael Quast et al.
Summary: This paper presents the retrieval of high-resolution soil moisture data from Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter measurements using a new bistatic radiative transfer modeling framework (RT1). The performance of the soil moisture retrievals is analyzed with respect to the ERA5-Land reanalysis dataset. The results demonstrate the potential of RT1 for the retrieval of high-resolution soil moisture data from SAR time series.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Geochemistry & Geophysics
Gustavo H. X. Shiroma et al.
Summary: This article presents a projection algorithm that uses area elements to represent radar samples and associates them with map coordinates. It accurately performs geocoding and slant-range projection, while improving computation efficiency.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
David Small et al.
Summary: This article presents a methodology for producing wide-area backscatter images. By combining backscatter measurements of a single region seen from multiple satellite tracks, the method provides wide-area coverage and corrects for slope effects. The approach is suitable for various applications, such as wet snow monitoring, land cover classification, or short-term change detection.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Fang Yuan et al.
Summary: Digital Earth Africa is providing an operational Sentinel-1 normalized radar backscatter dataset for Africa. Developed in partnership with Sinergise, this dataset is compliant with the CEOS Analysis Ready Data for Land (CARD4L) specification and can be accessed through the Digital Earth Africa platform. Applying radiometric terrain correction, this dataset is expected to support various applications in Africa, including natural resource monitoring and land cover mapping. It is the first free and open continental scale analysis ready data of its kind.
Article
Multidisciplinary Sciences
Josef Kellndorfer et al.
Summary: This dataset is the first of its kind to provide spatial representation of multi-seasonal C-band Synthetic Aperture Radar (SAR) interferometric repeat-pass coherence and backscatter signatures globally. It contains detailed information on how decorrelation affects interferometric measurements of surface displacement, making it valuable for various mapping applications.
Article
Remote Sensing
Alena Dostalova et al.
Summary: This study quantified the influence of radiometric terrain flattening (RTF) on forest mapping and classification over Austria, and found that it improved the overall accuracy of mapping and classification, with stronger effects in regions with steep topography.
REMOTE SENSING LETTERS
(2022)
Article
Environmental Sciences
Bernhard Bauer-Marschallinger et al.
Summary: By utilizing the systematic monitoring schedule and global land coverage of the Copernicus Sentinel-1 SAR mission, along with a priori generated probability parameters, we developed a datacube-based flood mapping algorithm to enhance the accuracy and robustness of fully automated flood monitoring and classification.
Review
Computer Science, Artificial Intelligence
Arsenios Tsokas et al.
Summary: This review presents the main approaches developed around satellite and airborne Synthetic Aperture Radar (SAR) imagery and summarizes the wide range of SAR imagery applications. The most popular methods and their applications are organized in a cohesive manner. The applications of SAR data are classified into earth observation and object detection applications, further categorized into land, sea, and ice applications. The article presents the basic methodologies and recent advances in land cover classification, object detection, and parameter retrieval from SAR data. The advantages, disadvantages, and particular characteristics of each method are highlighted. The study shows that the usage of SAR contributes to the improvement of techniques and the enhancement of reliability.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Geography, Physical
Claudio Navacchi et al.
Summary: This paper discusses the methods of generating backscatter datacubes using the Sentinel-1 mission, introduces a simplified workflow relying on its orbital stability, and proposes some improvements to speed up processing and reduce computational costs.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Alena Dostalova et al.
Summary: The constellation of two Sentinel-1 satellites offers unprecedented SAR data coverage with high spatial and temporal resolution, showing potential for forest mapping and classification at a continental scale in Europe.
Article
Environmental Sciences
Moritz Bruggisser et al.
Summary: With the increasing frequency of forest fires in the mid-latitudes and alpine regions, fire risk assessments have become more important. Airborne laser scanning provides accurate forest structure information, while data from the Sentinel-1 synthetic aperture radar mission can be used to update this information.
Article
Multidisciplinary Sciences
Bernhard Bauer-Marschallinger et al.
Summary: The normalized microwave backscatter map S1GBM of Earth's land surface is obtained from satellite SAR observations, achieving angle normalization in most regions but facing challenges of insufficient coverage.
Article
Environmental Sciences
Adugna Mullissa et al.
Summary: The Sentinel-1 satellites offer temporally dense and high spatial resolution SAR imagery, which is a valuable data source for various SAR-based applications. The Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days, providing valuable data for a wide range of applications.
Article
Engineering, Electrical & Electronic
Iris de Gelis et al.
Summary: The study explores the potential of using a fully convolutional network (FCN) for automatic sea ice concentration (SIC) estimation. By down-sampling input data and parameterizing the architecture, the FCN model is able to simulate the work of an analyst and achieve a high classification accuracy. A comprehensive database is used for testing, leading to an overall accuracy of 78.2% for the 6-class classification approach.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Ecology
Mortimer M. Muller et al.
ECOLOGICAL INFORMATICS
(2020)
Article
Environmental Sciences
Alireza Taravat et al.
Article
Computer Science, Information Systems
Catherine Ticehurst et al.
Article
Computer Science, Information Systems
John Truckenbrodt et al.
Article
Environmental Sciences
Marius Rueetschi et al.
Article
Geochemistry & Geophysics
Marko Makynen et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2017)
Article
Environmental Sciences
Thomas Nagler et al.
Article
Computer Science, Interdisciplinary Applications
Bernhard Bauer-Marschallinger et al.
COMPUTERS & GEOSCIENCES
(2014)
Article
Environmental Sciences
Ramon Torres et al.
REMOTE SENSING OF ENVIRONMENT
(2012)
Article
Geochemistry & Geophysics
David Small
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2011)