IEEE Geoscience and Remote Sensing Magazine

Top Research Topics at IEEE Geoscience and Remote Sensing Magazine?

The primary areas of discussion in IEEE Geoscience and Remote Sensing Magazine are Remote sensing, Remote sensing (archaeology), Artificial intelligence, Synthetic aperture radar and Data science. The concepts on Remote sensing presented in IEEE Geoscience and Remote Sensing Magazine can also apply to other research fields, including Field (computer science), Earth observation, Satellite, Meteorology and Radar imaging. The research on Remote sensing (archaeology) tackled can also make contributions to studies in the areas of Telecommunications, Sensor fusion and Library science.

The study on Sensor fusion presented is investigated in conjunction with research in Data mining. The journal connects the study in Library science with the closely related area of Engineering physics. Computer vision and Pattern recognition are some topics wherein Artificial intelligence research discussed in it have an impact.

The work on Synthetic aperture radar tackled in it brings together disciplines like Radar and Interferometry. The journal focuses on Data science research which is adjacent to topics in CONTEST.

• Remote sensing (30.28%)
• Remote sensing (archaeology) (25.82%)
• Artificial intelligence (9.62%)

What are the most cited papers published in the journal?

• Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources (1079 citations)
• Hyperspectral Remote Sensing Data Analysis and Future Challenges (1031 citations)
• Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (962 citations)

Research areas of the most cited articles at IEEE Geoscience and Remote Sensing Magazine:

The published articles primarily tackle Remote sensing, Hyperspectral imaging, Artificial intelligence, Remote sensing (archaeology) and Data mining. Field (computer science), Bistatic radar, Satellite, Global Positioning System and Sensor fusion are some topics wherein Remote sensing research discussed in the most cited papers has an impact. While the journal publications focused on Artificial intelligence, they were also able to explore topics like Radar imaging, Computer vision and Pattern recognition.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Statistics
• Machine learning

The previous edition focused in particular on these issues:

The discussions in IEEE Geoscience and Remote Sensing Magazine mainly cover the fields of Remote sensing, Remote sensing (archaeology), Artificial intelligence, Synthetic aperture radar and Image (mathematics). Specifically, studies on Radiometer are prevalent in the Remote sensing works discussed. IEEE Geoscience and Remote Sensing Magazine addresses concerns in Remote sensing (archaeology) which are intertwined with other disciplines, such as Feature extraction, Earth observation, Geospatial analysis and Library science.

The journal explores issues in Feature extraction which can be linked to other research areas like Pixel and Spatial analysis. Topics in Artificial intelligence explored in IEEE Geoscience and Remote Sensing Magazine were investigated in conjunction with research in Scale (map) and Pattern recognition. While Synthetic aperture radar is the focus of IEEE Geoscience and Remote Sensing Magazine, it also provided insights into the studies of Radar, Clutter, Radar imaging and Speckle pattern.

The most cited articles from the last journal are:

• High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (39 citations)
• A Large-Scale Benchmark Data Set for Evaluating Pansharpening Performance: Overview and Implementation (13 citations)
• Interpretable Hyperspectral Artificial Intelligence: When nonconvex modeling meets hyperspectral remote sensing (12 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in IEEE Geoscience and Remote Sensing Magazine (based on the number of publications) are:

• Martti Hallikainen (15 papers) absent at the last edition,
• James L. Garrison (15 papers) published 2 papers at the last edition, 2 less than at the previous edition,
• Werner Wiesbeck (15 papers) absent at the last edition,
• Devis Tuia (15 papers) published 2 papers at the last edition,
• Naoto Yokoya (14 papers) published 3 papers at the last edition, 1 more than at the previous edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in IEEE Geoscience and Remote Sensing Magazine (based on the number of publications) are:

• German Aerospace Center (22 papers) published 5 papers at the last edition, 1 more than at the previous edition,
• Wuhan University (17 papers) published 6 papers at the last edition, 4 more than at the previous edition,
• Goddard Space Flight Center (15 papers) absent at the last edition,
• University of Genoa (13 papers) absent at the last edition,
• Jet Propulsion Laboratory (12 papers) published 2 papers at the last edition the same number as at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 22.41% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 46.67% were posted by at least one author from the top 10 institutions publishing in the journal. Another 15.56% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.78% of all publications and 20.00% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Prospects in Remote Sensing and Related Fields

For readers intrigued by the fascinating world of remote sensing and eager to contribute to its increasingly important research, it may be useful to explore the career prospects associated with it. The world is witnessing an increasing demand for professionals with proficiency in this field and related areas like AI, data science, and synthetic aperture radar, given the contemporary relevance of these technologies. They offer various opportunities, ranging from meteorologists and computer scientists to geoscientists and agricultural engineers.

Another sector influenced by remote sensing is education. For example, those proficient in remote sensing are often sought after to teach relevant subjects at schools, colleges, and universities, and provide training programs. Instructors proficient in remote sensing can integrate their knowledge into generating innovative teaching methods that support complex learning processes, like those required in understanding earth observation, geospatial analysis, and more.

Position prerequisites differ according to each role and organization. For instance, to be a presсhool teacher in Indiana, one would require a different skill set. Those keen on exploring the teaching route can click preschool teacher requirements in Indiana to learn more.

Remember, as interesting as the world of remote sensing is, career options associated with it are equally diverse. Before venturing into any career path, it's vital to understand all the information, from educational qualifications to necessary licenses and certifications. Only a thorough understanding of the requirements can lead us to a thriving career!