Remote sensing involves collection of information about an object or phenomenon without direct contact with it. Sensors mounted on platforms such as aircraft, satellites, or drones enable the collection of data about Earth's surface, including land cover, vegetation, topography, and geological features. Traditionally, remote sensing data processing and information extraction have been carried out manually by human analysts. However, this approach is time-consuming, labor-intensive, and prone to errors. Machine learning algorithms offer a promising solution to this challenge. By training algorithms on large amounts of labeled remote sensing data, these techniques can automatically classify and extract useful information from images. One of the key advantages of machine learning-based approaches is their ability to handle large volumes of data quickly and accurately. Overall, the combination of remote sensing and machine learning has the potential to revolutionize our understanding of the Earth's surface and its processes, and to support a wide range of applications in fields such as agriculture, environmental monitoring, and urban planning.
The purpose of this Research Topic is to introduce research advances in the application of remote sensing technology, with a specific focus on using machine learning for information extraction from massive multispectral and hyperspectral satellite and unmanned aerial vehicle (UAV) data, for monitoring vegetation, water bodies, and land use changes under the background of climate change. Contributions are welcome to new methods and applications for extracting plant phenotypic traits, as well as to evaluating the impacts of climate change on plant phenotypic traits, water bodies, and land use, particularly using machine learning-based multisource remote sensing data fusion and information extraction. The scope of this Research Topic includes, but is not limited to, the following topics:
• Satellite On-orbit Intelligent Processing;
• On-orbit Geometry Calibration;
• Positioning Geometric Accuracy Verification;
• Multi-sensor Remote Sensing;
• Agricultural Remote Sensing and its Application;
• Remote Sensing of Water Environment and its Application;
• Forest Remote Sensing and its Application;
• Satellite Derived Bathymetry with New Sensors;
• Machine learning and deep learning for multi-source data processing and data fusion;
• Vegetation phenology extraction using multi- and hyperspectral images;
• Mapping vegetation phenology and vegetation growth monitoring;
• Time-series analysis monitoring of agriculture and forest using High-throughput data.
Remote sensing involves collection of information about an object or phenomenon without direct contact with it. Sensors mounted on platforms such as aircraft, satellites, or drones enable the collection of data about Earth's surface, including land cover, vegetation, topography, and geological features. Traditionally, remote sensing data processing and information extraction have been carried out manually by human analysts. However, this approach is time-consuming, labor-intensive, and prone to errors. Machine learning algorithms offer a promising solution to this challenge. By training algorithms on large amounts of labeled remote sensing data, these techniques can automatically classify and extract useful information from images. One of the key advantages of machine learning-based approaches is their ability to handle large volumes of data quickly and accurately. Overall, the combination of remote sensing and machine learning has the potential to revolutionize our understanding of the Earth's surface and its processes, and to support a wide range of applications in fields such as agriculture, environmental monitoring, and urban planning.
The purpose of this Research Topic is to introduce research advances in the application of remote sensing technology, with a specific focus on using machine learning for information extraction from massive multispectral and hyperspectral satellite and unmanned aerial vehicle (UAV) data, for monitoring vegetation, water bodies, and land use changes under the background of climate change. Contributions are welcome to new methods and applications for extracting plant phenotypic traits, as well as to evaluating the impacts of climate change on plant phenotypic traits, water bodies, and land use, particularly using machine learning-based multisource remote sensing data fusion and information extraction. The scope of this Research Topic includes, but is not limited to, the following topics:
• Satellite On-orbit Intelligent Processing;
• On-orbit Geometry Calibration;
• Positioning Geometric Accuracy Verification;
• Multi-sensor Remote Sensing;
• Agricultural Remote Sensing and its Application;
• Remote Sensing of Water Environment and its Application;
• Forest Remote Sensing and its Application;
• Satellite Derived Bathymetry with New Sensors;
• Machine learning and deep learning for multi-source data processing and data fusion;
• Vegetation phenology extraction using multi- and hyperspectral images;
• Mapping vegetation phenology and vegetation growth monitoring;
• Time-series analysis monitoring of agriculture and forest using High-throughput data.
