Remote Sensing Publications 1961–2023—Analysis of National and Global Trends

Abstract

Remote sensing underpins significant twenty-first century technical capabilities and innovations. Thus, understanding the technical expertise and financial drivers of the field is of national and international importance, as they are inextricably linked with intellectual property generation. Using 126,479 peer-reviewed journal papers and their affiliated funder information from two major publication databases, this study benchmarks current practices, documents historical shifts, and identifies emerging directions in the remote sensing industry and academic publishing. In 70 years, the field has moved from producing only a dozen scholarly papers a year to more than 13,000 annually, without equivalent growth in publication venues but with a rise in the mean number of authors from three to five in less than 25 years. The largest contributor (research and funding) is China, which has rapidly ascended since 2000 to now dominate the field with a near-majority stake. China’s dominance, representing 47% of all remote sensing journal papers worldwide, is mirrored in affiliated patents.

1. Introduction

From driverless vehicles to space exploration, the concept of remote sensing as a form of automated, non-contact documentation intersects nearly every field of study. Herein, the term “remote sensing” refers to the definition provided by NASA [1]: “Remote sensing is the acquiring of information from a distance”, which is considered in this research at multiple scales, from handheld to stationary to mobile (e.g., bike, van, train) to air (drone, helicopter, plane, satellite). The main focus is on line-of-sight technologies such as imagery, laser scanning, bathymetry, and hyperspectral imagery. These technologies are largely outgrowths of the surveying field and, as such, focus on field deployment (as opposed to laboratory work) in the built and natural environments. Consequently, medical imaging (e.g., magnetic resonance imaging, X-rays, and computed tomography scans) is excluded from this analysis due to representing both fundamentally distinct development and the use of arguably affiliated technologies that are typically non-line-of-sight and unable to be used outside a laboratory setup.

In 2022, remote sensing was a USD 452 billion global market and is expected to grow to USD 681 in 2025 and USD 1.44 trillion by 2030 [2]. The market has been driven by the trends of hardware miniaturization, increasing affordability, improved positioning, and greater data integration opportunities. Like many other fields, the affiliated technical breakthroughs and subsequent adoptions are not uniform across the globe. Anecdotal observations by reviewers and readers may note geographical shifts from the United States and Europe towards Asia, and China in particular, but select instances in particular publications or sub-areas are insufficient to draw data-driven conclusions. Without a quantitative, objective basis for documenting the evolution of scholarship in this area, conclusions cannot be drawn as to the impact of national policies and priorities and the subsequent impact on scholarship and sector growth at a national level. Furthermore, such documentation offers the possibility of creating a meaningful baseline, both to predict and check future trends and as a means to compare scholarship in this field to that in other fields, if so desired. As will be discussed, such an undertaking has yet to be pursued with a particular focus on long-term, longitudinal changes.

To help establish such trends and to create a baseline for future monitoring, this paper uses academic journal publications as a proxy to illustrate the current state of practice and historical changes over the past 72 years, as well as patterns in intellectual property generation. Establishing current research productivity and identifying the affiliated funding may provide strong indicators for subsequent commercial dominance in the remote sensing field and other fields for which remote sensing data underpin capabilities, such as in the rapidly growing fields of augmented reality, autonomous navigation, and digital twins.

While the term “remote sensing” entered the lexicon through a patent in 1958 [3], today’s understanding of the word includes both older and newer techniques. Presently, the concept encompasses approaches as old as bathymetry (which dates back to the late 19th century) [4], photogrammetry in regard to land surveying in 1906 [5], and thermal imagery with respect to land characterization in 1934 [6]. The term is also regularly employed when discussing multispectral imaging starting in the late 1960s [7,8] and satellite imagery in 1970 [9,10]. While there is a general understanding that remote sensing sensors, data, and platforms continue to grow in availability, influence, and applications, to date, quantification of that impact has been predominantly domain-specific [11,12]. A notable exception is the detailed work by Zhuang et al. [13] covering all remote sensing papers for the years 1991–2010 [13] and that by Zhang et al. [14], which primarily considered papers published in the MDPI journal Remote Sensing.

The present paper aims to take a more inclusive approach to this documentation in an effort to both provide baseline information that predates 1991 and extends to more recent years and to determine critical trends and drivers through an examination of the peer-reviewed literature, in addition to those previously identified by Zhuang et al. [13]. A comparison of select findings is provided in the Discussion section.

2. Methodology

This paper analyzes peer-reviewed journal publications related to remote sensing over the period 1961–2023. The publications indexed in the online resource Engineering Village [15] were examined for author affiliation by country and attributed national funding. Further investigation considered journal availability and the publication trends of selected journals for the same time period, including trends in authorship. The study scope was selected to commence in 1961, as prior to this year (1) there was a paucity of publications (as will be described in the Results) and (2) digital access to papers was highly limited and inconsistent.

2.1. Data Sources

The data were sourced from the online platform Engineering Village (EV) [15] as this is the most comprehensive resource for science and engineering publications. The system includes all major publishers and professional societies, thereby providing a relatively exhaustive entry point that is not heavily influenced by the location or business strategy of specific commercial or not-for-profit organizations. Additionally, unlike individual publishers, the platform offers a relatively large number of search fields and reports the query results in a qualitative manner—something not offered by other platforms (see Section 2.2 for functionalities). EV jointly hosts two sub-databases: Compendex [16] and Inspec [16]. According to the website, Compendex includes information about individual journal and conference papers, as well as dissertations, standards, books, and some preprints, which are sourced from major science and engineering publishers (e.g., Elsevier, Wiley, Taylor and Francis, MDPI) and professional societies such as the Institute of Electrical and Electronics Engineers (IEEE), the American Society of Mechanical Engineers (ASME), the Society of Automotive Engineers (SAE), and the Association for Computing Machinery (ACM) [16]. In contrast, Inspec was created by the Institution of Engineering and Technology (IET), with a narrower focus on publications related to physics, electrical engineering and electronics, computers and control, and information technology [16].

While both resources include a range of publication types, including theses, magazines, and conference proceedings, only the publication type of “journal article” was selected. This decision was intended to restrict inclusion to publications that were peer-reviewed (as not all conferences are) and to avoid the geopolitical impacts of wars, natural disasters, and epidemics on travel-based publications such as conferences, as some were held and some were suspended at various times—mostly notably during COVID-19, when there were severe travel restrictions that inconsistently altered conference proceeding participation, as some were moved online, some postponed or canceled, and others continued with significantly less international participation. Furthermore, the ad hoc nature of many conferences further complicates the ability to track publication trends and attributes in a single venue. This is less problematic with journals, as many persist through multiple periods within the selected study duration. Journal titles were selected based on the stated aims and scope of each journal (see full list of journals and years of publication in

Table A4. Remote sensing and photogrammetry journals—72 titles.

Journal	1961–1970	1971–1980	1981–1990	1991–2000	2001–2010	2011–2020	2021–2023
ASCE J. of Surveying & Mapping Div.	x
American Soc. for Photogrammetry & Remote Sensing	x	x			x
Applied Sciences						x	x
Applied Spectroscopy		x	x	x	x	x	x
Arabian J. of Geosciences						x	x
Australian J. of Geodesy, Photogrammetry & Surveying			x	x
Australian Surveyor		x	x
Automation in Construction							x
Bulletin Geodesique		x
Canadian J. of Remote Sensing		x	x	x	x	x	x
Canadian Surveyor		x	x
Chinese Geodetic & Cartographic Journal (in Chinese, Cehui Xuebao)					x	x	x
Computers & Geosciences			x	x	x	x	x
Computers, Environment & Urban Systems				x		x	x
Earsel Advances in Remote Sensing				x
Earth Observation & Remote Sensing				x
Earth-Oriented Applications of Space Technology			x
Egyptian J. of Remote Sensing & Space Science						x	x
European J. of Remote Sensing						x	x
French J. of Photogrammetry & Remote Sensing					x	x
French Soc. of Photogrammetry		x
French Soc. of Photogrammetry & Remote Sensing		x	x	x	x
Geo-Spatial Information Science					x	x	x
Geojournal			x	x	x	x	x
Geomatics & Information Science of Wuhan Univ.					x	x	x
Geomatics, Natural Hazards & Risk					x	x	x
Geospatial Solutions					x
Geoscience & Remote Sensing						x	x
IEEE Geoscience & Remote Sensing Letters					x	x	x
IEEE Geoscience & Remote Sensing Magazine						x	x
IEEE J. of Selected Topics in Applied Earth Observations & Remote Sensing					x	x	x
IEEE Trans. on Geoscience Electronics	x	x
IEEE Trans. on Geoscience & Remote Sensing		x	x	x	x	x	x
IEEE Trans. on Image Processing				x	x	x	x
International Geoscience & Remote Sensing Symp.			x	x
International J. of Applied Earth Observation and Geoinformation						x	x
International J. of Digital Earth						x	x
International J. of Remote Sensing			x	x	x	x	x
ISPRS International J. of Geo-Information						x	x
ISPRS J. of Photogrammetry & Remote Sensing			x	x	x	x	x
ITC J.			x	x
J. of Applied Photographic Engineering		x	x
J. of Applied Remote Sensing					x	x	x
Journal of Applied Spectroscopy		x	x
J. of Flight Science & Space Research (in German)			x
J. of Geo-Information Science							x
J. of Geodesy				x	x	x
J. of Geomatics					x
J. of Geophysical Research	x	x	x	x	x
J. of Imaging Technology			x
J. of Quantitative Spectroscopy & Radiative Transfer		x	x	x	x	x	x
J. of Sensors						x	x
J. of Surveying Engineering			x	x	x	x	x
J. of Surveying German		x	x	x
J. of the Indian Soc. of Remote Sensing					x	x	x
J. of the Optical Soc. of America	x	x	x	x
Open Geosciences							x
Optoelectronics, Instrumentation & Data Processing		x	x	x	x	x	x
Photogrammetria	x	x	x
Photogrammetric Engineering	x	x
Photogrammetric Engineering & Remote Sensing		x	x	x	x	x	x
Photogrammetric Record			x	x	x	x	x
Proc. of the IEEE	x	x	x	x	x	x	x
Radiotekhnika			x
Remote Sensing, MDPI					x	x	x
Remote Sensing Applications/Society and Environment						x	x
Remote Sensing Letters					x	x	x
Remote Sensing Reviews			x	x
Remote Sensing of Environment	x	x	x	x	x	x	x
Science of Surveying & Mapping					x
Sensors				x	x	x	x
Spectroscopy & Spectral Analysis (in Chinese, Guang Pu Xue Yu Guang Pu Fen Xi)					x	x	x
Solar System Research	x	x	x	x	x	x
Soviet J. of Optical Technology		x
Soviet J. of Remote Sensing			x	x
J. of Remote Sensing (in Chinese, Yaogan Xuebao)						x	x

). If a journal only published papers that simply used remote sensing as part of a standard workflow (as opposed to the development and/or testing of affiliated hardware, software, or algorithms or storage thereof with respect to the aforementioned definition of remote sensing), then they were excluded. As impact factors did not exist in the 20th century, such information was not available for much of the study duration. Thus, that was not employed herein for the inclusion or exclusion of a journal. Notably, no single aggregated metric was discoverable across the many decades of study. Finally, if a journal was selected for inclusion, all related data were reported for every reporting period for which the journal was published. Within Engineering Village (EV), the two sub-databases can be queried separately or jointly. Problematically, the metadata of the two databases are not identical (see Appendix A 

Table A1. Original data schema—maximum attributes.

Attribute	Data Type	Description	Notes
Title	String	Article title
Accession number	Long	A unique 14-digit number assigned to each record assigned by EV
Author	String	All authors
Author affiliation	String	Authors’ corresponding affiliations
Corresponding author(s)	String	One of the authors and a contact
Source	String	Journal
Abbreviated source title	String	Abbreviated journal title
Publisher	String	Publisher
Volume	Int	Volume
Issue	Int	Issue
Pages	String	Pages
Issue date	Date	Issue date
Publication date	Date	Mostly publication year, sometimes contains month
Publication year	Year	Publication year	Compendex only
Language	String	Language
ISSN	Int	ISSN
E-ISSN	Int	E-ISSN
DOI	String	Digital Object Identifier
Article number	Int	Article number
CODEN	String	Six-character representations of source titles
Country of publication	String	Country of publication
Document type	String	Document type; journal article in our case
Abstract	String	Abstract of the article
Number of references	Int	Number of references
Main heading	String	A subject classification that serves to represent the main topic of the document	Compendex only
Controlled/subject terms	String	A list of subject terms used to describe the content of a document in the most specific and consistent way possible
Uncontrolled terms	String	Not assigned from the Engineering Village’s Thesaurus, but derived from the abstract and author keywords
Classification code	String	A numerical hierarchy of general subject categories
IPC code	String	International Patent Classification (IPC) codes pertain to a hierarchical classification for patents for different areas of technology	Inspec only
CPC code	String	Cooperative Patent Classification (CPC) system was jointly developed by the European Patent Office (EPO) and the United States Patent and Trademark Office (USPTO)	Inspec only
Treatment	String	Nature of the content based on the focus
Discipline	String	Discipline	Inspec only
Funding details	String	Sponsors; missing values exist
Funding text	String	More details about the funding
Open access type(s)	String	Open Access, Gold, Hybrid Gold, Bronze, Green	Compendex only
Database	String	Compendex/Inspec
Copyright	String	Copyright
Data provider	String	Engineering Village

; representation of select fields is described in Appendix A 

Table A2. Acronyms and color representations of countries in Figure 4 and Figure 5.

Country/Region	Color
US	Cornflower blue
UK	Gold
Canada	Peru
China	#EB4D4D
Israel	Pink
Netherlands	Lawn green
Romania	Lime green
Japan	Gray
France	Dark sea green
USSR	Light coral
Germany	Yellow green
Australia	Cyan
India	Wheat
Italy	#AA70F6
Russia	Light salmon
Spain	Light green
Europe	Medium sea green
Sweden	Forest green

and

Table A3. Acronyms for funding bodies.

Country (Color)	Sponsors
US (blue)	NASA: National Aeronautics and Space Administration
	NSF: National Science Foundation
	ONR: Office of Naval Research JPL: Jet Propulsion Laboratory DOE:
	U.S. Department of Energy JSC: Johnson Space Center
	NOAA: National Oceanic and Atmospheric Administration
China (red)	NNSF: National Natural Science Foundation of China
	CAS: Chinese Academy of Sciences
	NKRDP: National Key Research and Development Program of China
	973 Prgm: National Basic Research Program of China (973 Program)
	FRFCU: Fundamental Research Funds for the Central Universities
	PSF: China Postdoctoral Science Foundation
Canada (brown)	NSERC: Natural Sciences and Engineering Research Council of Canada
Europe (green)	ESA: European Space Agency EC: European Commission
	Horizon 2020: Horizon 2020 Framework Programme
UK (yellow)	NERC: Natural Environment Research Council
Italy (purple)	AST: Agenzia Spaziale Italiana

) and neither was found to be consistently populated. Thus, multiple searching strategies were devised to both find entries and then identify and remove duplicates, while retaining the most complete metadata possible. At the time that the data were initially queried, Engineering Village did not offer an automated means for duplication removal. Today, while this feature is available, when tested, the “automatically removed” duplicates were only a fraction of the 90% duplications identified manually by the research team. The team’s original query returned 247,145 records. After removing duplicates, the final data set included 126,479 records.

2.2. Attribute Harmonization

Within EV, selective attributes for all entries are visible in the sidebar. These include the country of origin and funder. For these two attributes, the numbers reported herein were taken directly by the selected time period, with a focus on the top 10 producers and top 10 funder sponsors for each decade. However, in the top group of sponsors, the California Institute of Technology (US) appeared in 1981–1990 and the National Eye Research Center (UK) in the periods 1981–1990 and 1991–2000. As these are predominantly institutions, rather than funders, they were excluded as top funders and replaced with the next highest-ranked entity. For the period 1981–1990, the European Space Agency and the National Oceanic and Atmospheric Administration were substituted. For the period 1991–2000, the Agenzia Spaziale Italiana was substituted.

2.3. Cleaning

For other characteristics reported, the relevant entries were downloaded and examined. However, within EV, the number of downloadable entries per query is limited to 5000 records. Thus, a comprehensive filtering strategy was needed to find all entries that contained the terms “remote sensing” and/or “photogrammetry”. The first step in the filtering strategy employed a list of all system-generated journal titles that appeared in the EV sidebar for the entire study period. This involved querying for the two controlled vocabulary terms and restricting the query to only journal articles. The resulting list included the titles of all journals that published at least one paper in which the controlled vocabulary terms of “remote sensing” or “photogrammetry” appeared. Additional terms such as “bathymetry” and “hyperspectral” were also tested, but the results consistently appeared as subsets of the larger query results. Problematically, terms like “imaging” returned large volumes of medical-related results, which were inappropriate for this study.

As one of the goals of the research herein was to understand publishing trends within the field of remote sensing, the initial list of journal titles was culled to retain only journals whose primary focus was remote sensing or photogrammetry; this strategy followed that previously adopted by Zhuang et al. (2013) [13], which showed that the top 20 journals contained 86% of all relevant publications. Herein, 72 journals were selected based on their stated aims (Appendix B Table A4). Queries were then performed by journal title for each year in 1961–2023. This was conducted in a joint search of Compendex and Inspec. Notably, the “language of publication” was not considered, although the vast majority of records (both current and historical) were in English.

The query results were then manually inspected to create a master file per journal and per decade that sought to remove duplicates and include complete metadata for the remaining records. Duplicate records existed in two forms: (1) those appearing in both the Compendex and Inspec databases and (2) records appearing under different abbreviations of a single journal’s name. When a DOI number was available (starting in 2000), the DOI was the primary means of identifying duplicates, and verifications were performed through article titles as a secondary measure. If a DOI was unavailable, titles were used as the sole means of identifying and removing duplicates. As part of this, journal names were standardized manually to reduce querying errors. The total number of authors per article was manually extracted by the research team.

The original, downloaded data contained more than 30 attributes per article (see Appendix A, Table A1). The study herein employed the fields Title, Author, Source, DOI, and Database. The Publication Year field was not included due to a large number of missing values. Instead, the field was manually populated based on the querying per year results. While this may appear circular in nature, the system appeared to have used record metadata that were not downloadable.

3. Results and Discussion

This section examines the growth in papers and journals and the geographic spread of remote sensing scholarship. Changes in funders and national dominance are then examined. Finally, trends in authorship and the number of publications per journal are considered.

3.1. Publication Growth

Table 1. Overview of paper production and relevant journals *.

Period	Av. Number of Papers/Yr	Number of Journals	Av. Papers per Journal/Yr	Total Papers
1961–1970	14.7	9	1.63	147
1971–1980	104.9	25	4.20	1049
1981–1990	575.7	33	17.45	5757
1991–2000	967.0	31	31.19	9670
2001–2010	1640.3	36	45.56	16,403
2011–2020	5308.5	43	123.45	53,085
2021–2023	13,456.0	43	312.93	40,368

* See Appendix B Table A4 for full journal list.

shows a breakdown across the seven periods [(six with 10 years each and the seventh with only 3 years (2021–2023)]. The reported values show exponential growth in papers per period (R² = 0.96), with less than 15 papers per year in the period 1961–1970 to 13,456 per year in the period 2021–2023. This near thousand-fold increase in papers is unattributable to a growth in journals dedicated to remote sensing/photogrammetry, which increased from only nine in the years 1961–1970 to 43 in the 2011–2023 period. Likely drivers are explored in this paper’s Discussion section.

The period of 2011–2020 saw a more than tripling in the number of published remote sensing papers. A similar rise was seen for the next period, but with almost a further tripling in only 3 years. As the data for 2021 to 2023 only included 12 quarters, rather than a full forty quarters for the decade, exponential fitting through the first 12 periods produced an R-squared value of 0.96, which would imply a more than 10-fold increase for the full period of 2021–2030.

3.2. Journal Publishing Trends

As noted in Table 1 and shown in Figure 1, the overall number of papers per journal has increased over time. A critical part of this is the significant growth in the number of papers being published per journal. Despite some fluctuations in the lower boundaries of the number of papers published by each journal, the upper boundaries continue to surpass those of previous periods, with some journals publishing thousands of papers per year. For further insights as to the distribution of papers across remote sensing and photogrammetry journals, the reader is referred to Zhang et al. (2019) [14] for a per-publication analysis.

Not only has the number of papers per year grown but the number of affiliated authors has also increased, showing a tendency for more collaborations on a single paper. As shown in Figure 2, the average number of authors per paper rose from two in the 1970s to five by 2020, with a clear trend of approaching six authors per paper in the coming years. This group collaboration approach may be a major factor in the proliferation of publications.

3.3. Authorship Trends

Another aspect is that the maximum number of authors on a single paper has also increased, with only six in 1961–1970, followed by 12 in 1971–1980, to 29 in 1981–1990. Both of the next two periods saw a near doubling of the 1981–1990 value, with 55 in 1991–2001 and 53 in 2001–2010. This then increased to 99 in 2011–2020 and further rose to 118 in 2021–2023; these surprisingly high contribution lists were manually checked for correctness. Figure 2 shows a shift in how research in this field is being conducted that may be contributing to this high growth in papers. Specifically, despite the continued presence of single-author papers in each period, there is a clear shift in the research landscape to large-scale collaborations, leading to an overall increase in co-authorship.

3.4. Geographic Scholarship Distributions

Some of the growth in publications may be attributed to more global participation. Figure 3 shows scholarly contributions in the period 1961–1980 in North America, Russia, India, Australia, Brazil, and much of Western Europe. Over the next 20 years, there was more participation from countries in Eastern Europe, South America, Southeast Asia, the Middle East, and Africa (especially in Northern Africa). By 2020, nearly all countries were producing at least occasional remote sensing scholarship, with a final dozen publishing for the first time in the years 2021–2023. While Figure 3 shows when remote sensing scholarship began in each of these countries, the current and previous levels of scholarship are not reflected.

3.5. National Production Trends

To understand the initial and evolving dominance in remote sensing, Figure 4 shows, for each decadal period, the percentage of papers from the top 10 contributing countries. From 1961 to 1970, 100% of all papers came from only six countries: two in North America; three in Europe; and two in Israel. The next decade shows broadening participation, with Japan, the USSR, Australia, and India appearing in the top 10 producers, who only accounted for 91.39% of this decade’s publications. In the first two decades, the US dominated—accounting for the majority of publications, with overwhelming percentages (88% in the period 1961–70 and 70% in the period 1971–1980); see Figure 4a,b. Over the period 1981–1990, while the US still produced more than 50% of the publications created by the top 10 producers, other countries increased their percentages, and the USSR moved to second place, with 9%, with all other countries remaining at the same level or growing slightly; see Figure 4c. Notably, only 13.80% of the papers in this period were published by other countries; see Figure 4c. Broader participation grew significantly over the next 33 years (peaking at 25.4% in the period 2011–2020); see Figure 4f. In the period 1991–2000, the US contribution dropped to 48% of those published by the top 10 producers, and the UK was second with 8%; see Figure 4d. With the dissolution of the USSR in 1991, Russia is reported at only 5%.

In this period, for the very first time, China appears in the top 10 producers, with nearly 4% (the same as India), and Australia drops out of the top 10. Arguably, the next decade showed the greatest change. Over the years 2001–2010 (Figure 4e), the US dropped to only 36%, and China’s share increased to 23% of publications. Additionally, Spain displaced Russia out of the top 10 in publishing, and the contributions of the top 10 countries slipped to 77.10%. China’s ascendency continued in the period 2011–2020, reaching 44% (Figure 4f) and, for the first time, displaced the US out of the highest position (dropping to 21%). In the period 2011–2020, for the first time, the top 10 contributing countries represented less than three-quarters of the publications; see Figure 4. This trend was slightly reversed in the years 2021–2023, in part as a result of China’s growing dominance in remote sensing publishing. Specifically, China now accounts for 62% of the publications from the top 10 contributing national contributors and almost half (47%) of all papers produced in the years 2021–2023. This shows a complete reversal of the US’s preeminence in the first 30 years of this study; see Figure 4g. In fact, over 60 years, the US dropped from producing 88% of all publications in the period 1961–1970 to only 9.29% (12% of those from the top 10 countries) in the years 2021–2023.

3.6. National Funding Trends

To illustrate the drivers of this major geopolitical shift in publishing, Figure 5 shows the top 10 funders in each period starting in 1981; prior to this, funder information was not available. From 1981 to 2023, there are 18 unique funding entities: the US (6), the UK (2), China (6), Canada (1), Europe (3), and Italy (1). Notably, there is not a one-to-one correlation between the funders in Figure 6 and countries in Figure 5, as several countries have multiple organizations appearing in the top funder group. However, there is clearly a trend of dominance in publishing aligning with the number of national funding agencies. Specifically, while the US has six named funders in the period 1981–1990, over the next 30 years, this drops to two named funders starting in the period 2011–2020. Conversely, China first appears in the top 10 producers in the period 2001–2010, with two funders, but grows to six funders in the next decade, and it is currently at five.

Consistent with the distribution of publications by country, before 2001, the US was the most frequently cited funder for remote sensing and photogrammetry research, with the National Aeronautics and Space Administration (NASA) accounting for around 50% of the funding affiliation. This was followed by America’s National Science Foundation (NSF), which was acknowledged in approximately 10% of papers. From 2001 to 2010, the funding distribution began to diversify, with broader contributions from other countries and regions, including China, Canada, Europe, and the UK. However, from 2011 to 2020, there was a dramatic increase in funding from China. In fact, in this period, the National Natural Science Foundation of China (NNSF) surpassed NASA to become the top affiliated funder. In the period of 2011–2020, China was affiliated with 40% of all remote sensing papers, while the US was only affiliated with 10.8%. This trend vastly intensified in the period 2021–2023, with 53.8% of papers having Chinese affiliation and the US only having affiliations with 5% of all remote sensing papers.

Since obtaining and normalizing the impact of each grant by funding level was not feasible, the frequency of attribution was plotted against the number of acknowledged funding agencies from each country for each period, as shown in Figure 6. For this, the number of funders per country was aggregated and averaged over each study period. For example, in the period 2011–2020, the top ten selected funders accounted for 53.83% of all publications. China had six funding agencies who were acknowledged in 74% (Figure 5d) of the publications produced by the top 10 nations (thus, the actual affiliations represented 39.83% across all nations). This weighted value was then plotted against the average number of funders from that country over that period (typically 10 years). Overall, the R-squared value is 0.833. When only a single country is considered, the fitting values rise to 0.966 for China and 0.990 for the US. The near 1:1 ratio of funding to publications for China is probably slightly lagging given the large recent acceleration in funding.

3.7. Technology and Application Trends

To understand how technology and application interests appeared over these decades, individual knowledge graphs were produced for the four most recent periods of this study (Figure 7). In each period, the top 20 most frequent words appearing in the publication titles were identified. Then, a spaCy language model (en_core_web_md) was employed to compute semantic similarities among these words, establishing edges between semantically related terms in the resulting knowledge graph. Each node was sized and colored proportionally to the word frequency, with larger and darker nodes representing higher frequencies.

In the period 1991–2000 (Figure 7a), five data sources or equipment types appeared (red rings). Three are strongly related to satellites (satellite, spectral, synthetic aperture, and aperture radar). The fourth term is “image”. The only application area of note is vegetation (green ring), and no specific techniques appear (no purple circles).

In the period 2001–2010 (Figure 7b), there are no significant changes from the previous period. There are six data sources or equipment types (red circles). Again, they mostly have strong affiliations with satellites (satellite, spectral, hyperspectral, and MODIS), as well as the more generic terms “image” and “high resolution”. The term “land cover” is added to “vegetation” as the two application areas (green circles). No specific techniques appear (purple circle).

In the third period of 2011–2020 (Figure 7c), much remains the same, but “neural network” appears, as does “time series”, although the latter is not connected to any of the other terms. For the final three years of study (2021–2023), there are significantly more changes (Figure 7d). Although the inputs are relatively similar (i.e., satellite, sentinel, hyperspectral, image, image, and high resolution, all with red circles), there is the strong emergence of techniques (purple circles) dominating the field, with “network”, “neural network”, “deep learning”, and “machine learning”, as well as the previously noted “time series” (but this time connected to “deep learning”). Additionally, no application area (i.e., green circles) appears, likely indicating a much broader range of fields adopting these techniques.

3.8. Contributions

This paper provides a quantifiable analysis of publishing trends in remote sensing, which is important, as it both establishes a set of measurable benchmarks for the industry and offers a verifiable means to examine what would otherwise be anecdotal considerations. For example, a scholar might, in their own personal experience, observe a rise in certain ethnic names, specific institutions, or even country affiliations in recent manuscript reviews, submitted papers, or current reading. However, this would be insufficient to determine (1) whether or not this is true, (2) the extent of the trend, and (3) other potentially contributing factors. Through a multi-decadal analysis of 126,479 journal papers having either the term “remote sensing” or “photogrammetry”, appearing in 72 journals devoted to these fields, a definitive baseline has now been established. The largest trends relate to the rapid proliferation of papers and the complete participatory inversion of scholarly participation by the United States and China.

This paper also establishes in some places, while further confirming in others trends within the publishing industry related to remote sensing, including (1) greater global participation in scholarly publications, (2) the rate of publication growth overall and per journal, (3) the rate of growth in the number of authors per publication, and (4) the relationship between funding availability and publications at a national level.

3.9. Growth in Publications

The data presented herein demonstrated exponential growth in papers over the period 1961–2023. A similar exponential trend was reported by Zhang et al. (2019) [14] for the years 2009–2017, which was much faster than for scholarly papers overall, according to the Science and Engineering Indicators report from the US NSF, which shows 12.1 million publications in 2003, versus only 33 million in 2023 [17].

There are four readily identifiable drivers for the two-orders-of-magnitude growth in annual remote sensing papers in academic journals. The first is the expansion of funders and greater global participation. This can be most easily seen in Figure 1, as the total number of countries contributing to paper generation significantly expands. Figure 1 documents the first period in which a country published a remote sensing or photogrammetry paper but does not indicate their relative contributions to the total publication count or changes in geopolitical conditions (e.g., the dissolution of the USSR). Over the past quarter century, there has been the great expansion of remote sensing research across the global south (see Figure 1). This may be attributable to the second driver, which is the decreased cost and greater accessibility of easy-to-use sensors (e.g., the Velodyne puck [18]) and remote sensing platforms (e.g., rotary drones) [19,20].

The second driver is a move across the entire academic publishing industry away from physical publishing to “virtual only” issues, which freed journals from publisher-mandated maximum annual page counts for journals, thereby no longer restricting a journal to a specific number of articles or pages per issue or per calendar year. This change in publishing has also greatly truncated the submission-to-publication cycle, as previously noted by Zhang et al. (2019) [14], looking at the publishing period 2009–2018.

Thirdly, there has been a large expansion of countries that have undertaken and openly published national aerial laser scanning scans, a trend first noted in 2015 [21] and most recently seen in the completion of the United States’ first national scan [22].

The final (and potentially largest) impact on article growth is from the adoption of artificial intelligence (AI) in remote sensing research [both machine learning (ML) and deep learning (DL)]. Without considering the potential impacts of ChatGPT [23] and other tools that generate content, both ML and DL have enabled a wave of publications, because scholarship can be achieved without the production of new raw data and the affiliated time and costs of conducting such experiments. This can be achieved either through more effective data reuse or through the generation of synthetic data. Figure 8 shows explicitly the rise in remote sensing papers using the terms “ML” and/or “DL” each year. This trend is also visible in the rise in terms like “deep learning” and “convolutional neural networks” in the past 13 years, as shown in the comparative knowledge graphs in Figure 7.

3.10. Cycles of Funding, Papers, and Resulting Patents

As early as 2013, China’s likely ascendency in remote sensing was noted in a study covering the 20 years of 1991–2010, where Zhuang et al. [13] ranked China as the second-highest cumulative contributor across that period. In that publication, the rapid rise in prominence was attributed to China’s satellite program. While the term “satellite” appears prominently in Figure 7a for the years 1991–2000, its importance greatly decreases in all subsequent periods. Thus, China’s further dominance in scholarly work in remote sensing and photogrammetry must be ascribed to other factors, such as research funding availability, as shown in Figure 4, Figure 5 and Figure 6. Notably, for the period 2009–2018, Zhang et al. [14] ranked the 50 institutions producing the largest number of remote sensing papers. Of those, 19 were Chinese, and China already held the top four spots (two universities and two governmental agencies).

The impact of remote sensing research funding is readily mirrored in the filing of patents. Using Google’s worldwide patent search for each of the seven sub-periods studied herein, the total and annual numbers are as shown in

Table 2. Patents by period including term “remote sensing”.

Years	1961–1970	1971–1980	1980–1990	1991–2000	2001–2010	2011–2020	2021–2023	2021–2025 *
Patents	92	368	1934	4974	15,944	72,830	43,048	58,953
Per year	9.2	36.8	193.4	497.4	1594	7283	14,349	17,864

* As per 7 May 2025.

, while Figure 9 shows the percentages of patents filed by the top 19 patent filers in each period, clustered by national origin. Figure 9 strongly reflects the radical reversal of the US’s dominance through the end of the 20th century to the extremely rapid rise of China in less than a quarter of a decade.

The importance of such observations is in their implications for national prosperity. The use of patents as a proxy for the prediction of the gross domestic product (GDP) is a long-established process that has been employed for more than 35 years [25] and is actively used today [26,27]. Work by Paula and Silva [28] and other researchers has directly linked research and development spending to companies and higher education institutions (public and private), which has fueled increased national patent applications and, thus, contributed to national prosperity.

In 2017, Kwon et al. [29], in an examination of all patents covering 1980–2011, noted a strong surge in the number of patents from Taiwan, Korea, and China, as well as their improved quality (as measured by citations). In this period, while the US dominated IP, there was only a three-fold increase in patent filing compared to the 100-fold increase seen for China. More crucially, in the years 2000–2011, according to Google Patents [24], there were 34,095 patents with the term “remote sensing”, of which already more than a quarter of them were Chinese in origin, despite China filing less than 6% of all patents for this period (

Table 3. Patent filings by national origin [28].

Country	1980–1999	1990–1999	2000–2011	Total
United States (US)	393,595	602,865	1,053,089	2,049,549
China (CN)	139	571	11,825	12,535
Rest of the World (RW)	314,738	504,955	1,005,371	1,825,064
Total	708,472	1,108,391	2,070,285	3,887,148

), thus showing a clear and targeted set of investments and returns by China in the remote sensing field. The patent information is included through 2025, as patents lag behind both funding and publications due to the lengthy interrogation process to which they are subjected.

4. Conclusions

This paper establishes quantitatively longitudinal trends and baseline trends related to the publication of peer-reviewed journal publications in remote sensing and photogrammetry and affiliated national funding. Major observations include (1) the exponential growth in papers in this field far outpacing general science and engineering publication trends, which is occurring without similar growth in journals; (2) a steady increase in the number of authors per paper, which doubled in only 30 years and appears to be continuing upward; (3) international participation in remote sensing scholarship expanded greatly, with over 160 countries consistently contributing since 2011; (3) since 1961, a complete inversion in the national affiliations of authors, with the US producing 88% of the remote sensing publications in the period 1961–1970 but only 9% in the years 2021–2023; (4) conversely, China now produces more than 47% of all remote sensing papers (2021–2023), which represents a significant rise from only 3% in the years 1991–2000; (5) funding trends strongly mirror those of publications, with Chinese funders acknowledged in more than 53% of the publications from 2021 to 2023 and the US in only 5% of them; (6) these trends are also heavily influenced by the number of funders per nation, with an R-squared value of 0.833, correlating with the affiliated publication levels.