Nothing Special   »   [go: up one dir, main page]
Next Article in Journal
Evaluating Tree Species Mapping: Probability Sampling Validation of Pure and Mixed Species Classes Using Convolutional Neural Networks and Sentinel-2 Time Series
Previous Article in Journal
Enhancing Remote Sensing Object Detection with K-CBST YOLO: Integrating CBAM and Swin-Transformer
Previous Article in Special Issue
The Extraction of Terrestrial Water Storage Anomaly from GRACE in the Region with Medium Scale and Adjacent Weak Signal Area: A Case for the Dnieper River Basin
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 16
Issue 16
10.3390/rs16162886
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Trends and Innovations in Surface Water Monitoring via Satellite Altimetry: A 34-Year Bibliometric Review

by
Zhengkai Huang
1,
Rumiao Sun
1,
Haihong Wang
2,* and
Xin Wu
1
1
School of Transportation Engineering, East China Jiaotong Univeristy, Nanchang 330013, China
2
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(16), 2886; https://doi.org/10.3390/rs16162886
Submission received: 15 June 2024 / Revised: 31 July 2024 / Accepted: 6 August 2024 / Published: 7 August 2024
(This article belongs to the Special Issue Recent Progress in Understanding Global Sea Level Rise Using Space and Earth Observations)
Browse Figures
Figure 1
<p>Flowchart of the technological roadmap.</p> "> Figure 2
<p>Graph of publication volume and citation frequency of the literature.</p> "> Figure 3
<p>Information on the 20 authors with the most publications: (<b>a</b>) Research Output, (<b>b</b>) Centrality Index, (<b>c</b>) H-index.</p> "> Figure 4
<p>Author partnership analysis: (<b>a</b>) CiteSpace partnership analysis, (<b>b</b>) VOSviewer–based partnership network.</p> "> Figure 5
<p>Journal and discipline analysis: (<b>a</b>) journal titles; (<b>b</b>) disciplines.</p> "> Figure 6
<p>Literature co-citation network diagram.</p> "> Figure 7
<p>Statistics of hotspot areas: (<b>a</b>) is the percentage of each water body in the total number of documents; (<b>b</b>) represents the top 5 hotspot research areas in the water body.</p> "> Figure 8
<p>Keyword co-occurrence network diagram.</p> "> Figure 9
<p>Keyword emergence analysis chart, visualizing 52 keywords. The solid circle in the figure marks the peak research year for the keyword during this timeframe, with its size indicative of the volume of literature published on the keyword in that year.</p> "> Figure 10
<p>Comparison of citation counts between Web of Science, Google Scholar, and Scopus.</p> ">
Versions Notes

Abstract

:
The development of satellite altimetry has significantly advanced the application of satellite Earth observation technologies in surface water monitoring, resulting in a substantial body of research. Although numerous reviews have summarized progress in this field, their analyses are often limited in scope and fail to provide a systematic, quantitative assessment of the current research prospects and trends. To address this gap, we utilize CiteSpace and VOSviewer bibliometric software to analyze 13,500 publications from the WOS database, spanning the years from 1988 to 2022. Our analysis focused on publication volume, authorship, collaboration networks, and content. We also compare data from Google Scholar and Scopus to validate the reliability of our dataset. Our findings indicate a steadily growing research potential in this field, as evidenced by trends in publication volume, authorship, journal influence, and disciplinary focus. Notably, the leading journals are primarily in the realm of remote sensing, while key disciplines include geology, remote sensing science, and oceanography. Keyword analysis revealed current research hotspots such as sea-level rise, snow depth, and machine learning applications. Among various water body types, research on glaciers ranks second only to ocean studies. Furthermore, research focus areas are shifting from large oceanic regions like the Pacific and Atlantic Oceans to significant inland water bodies, notably the Tibetan Plateau and the Amazon basin. This study combines qualitative and quantitative methods to analyze vast amounts of information in the field of surface water monitoring by satellite altimetry. The resulting visualizations provide researchers with clear insights into the development trends and patterns within this domain, offering valuable support for identifying future research priorities and directions.

1. Introduction

Surface water graces our planet, manifesting in oceans, lakes (reservoirs), rivers, wetlands, and glaciers [1]. It is an indispensable resource for human survival and development [2]. Scientific research indicates that over the past three decades since 1993, there has been an accelerated rise in global sea levels [3]. This phenomenon not only threatens to inundate low-lying coastal areas, exacerbating storm surges and flooding, but also has significant implications for agriculture, ecological environments, and economic development. Additionally, changes in global water storage are not to be overlooked. Due to climate change and excessive human water use, more than half of the world’s large lakes and reservoirs have seen a reduction in water volume over the past 30 years [4]. This consequently makes the monitoring of surface water indispensable, as it offers critical insights into our environment and aids in strategic planning. Traditional monitoring methods have their drawbacks, such as a limited duration and sparse coverage [5]. Microwave remote sensing technology offers new perspectives and application potential for traditional satellite observation techniques by providing the ability to penetrate through clouds and vegetation, as well as its sensitivity to the monitoring of surface moisture [6,7]. The era of satellite technology has ushered in a new age of observing the Earth from space. The satellite altimetry techniques enable us to monitor surface water on a global scale with greater precision [8]. Technological advancements have stimulated a surge in scientific discourse. A plethora of research papers on surface water monitoring has emerged [9]. It presents a new challenge: synthesizing this wealth of information.
Given that governments typically oversee traditional water-level data, numerous research institutions and scholars have turned to satellite altimetry technology to monitor surface water changes [10], thereby amassing a significant corpus of research on the subject. Current review articles on satellite altimetry in hydrology often concentrate on specific areas or single water bodies. For example, Church et al. [11] focused on global sea-level rising based on satellite data and coastal sea-level readings. Xu et al. [12] utilizing altimetry data from the ICESat (Ice, Cloud, and land Elevation Satellite) and ICESat-2 satellites, observed that the water levels of the majority of global lakes and reservoirs have been rising from 2003 to 2021. Kouraev et al. [13] discussed monitoring snow depth on ice with altimetry and the SSM/I (Special Sensor Microwave/Imager) sensor. Reviewing this extensive research demands considerable reading time. Moreover, due to the diverse knowledge and understanding among researchers, subjective judgments can differ greatly, which might result in different conclusions, as Huang et al. [9] noted. This review aims to overcome the limitations of traditional reviews in satellite monitoring of surface water. It seeks to categorize water bodies and perform a quantitative analysis of the related literature. The goal is to uncover trends in monitoring surface water changes using various satellite technologies.
Recently, scientometrics has been extensively used to describe the development of disciplines, illustrate the intrinsic mechanisms within a field, and reveal research trends. By visualizing quantitative information indicators within a specific domain, it enables researchers to gain deep insights into various aspects of interest [14]. Several scholars currently employ scientometrics to analyze the advance of satellite altimetry. Eito et al. [15] quantitively reviewed the scientific product of radar altimetry based on the Scopus database. Yang et al. [16] conducted a scientometric analysis of the achievements and future trends in satellite altimetry, utilizing a dataset comprising 8541 publications from the Web of Science Core Collection (WOS CC) that spanned the period from 1970 to 2021. Both of these used only the meta-data in a single database and did not compare with other databases. Huang et al. [9] gave a comprehensive review on inland water monitoring using multi-source satellite Earth observation technology including satellite altimetry. These reviews grounded in scientometric and bibliometric methodologies have significantly deepened our comprehension of advances related to satellite altimetry, albeit with a distinct emphasis in each study. The first publication offered an overview and analysis of the scientific and technological literature of radar altimetry, with the objective of assessing the outcomes of research efforts and investments [15]. The second was dedicated to providing a grand picture of SA, summarizing the achievements over the past half-century and projecting forthcoming trends [16]. Whilst, the third contribution to the literature focused on a more circumscribed research domain, specifically the monitoring of inland water bodies from space [9].
As artificial intelligence (AI) technology continues to advance, AI-assisted reading is emerging as a promising and powerful tool for the analysis and review of the scientific literature. AI-assisted reading, when used correctly, can greatly benefit the scientific community by improving the efficiency and effectiveness of literature review processes. Recent studies have highlighted the capability of AI tools to facilitate researchers in the analysis and interpretation of literature reviews, thereby enhancing the efficiency of the research process [17]. Biswas et al. [18] conducted a validation study on the accuracy of ChatGPT as a literature reviewer, revealing its substantial congruence with human reviewers. Nevertheless, AI-assisted reading faces challenges in resolving ambiguities present in complex documents.
This study aims to achieve two goals. The primary goal is to conduct a scientific and quantitative analysis of the literature on satellite altimetry monitoring of surface water and describe the historical achievements, current status, and future trends in this research field. The secondary goal is to verify the feasibility of artificial intelligence tools to assist in the reading of the scientific and technological literature. The subsequent sections of this paper are structured as follows: Section 2 delineates the data collection procedures and research methodologies, encompassing the assembly of the Web of Science (WOS) dataset and detailing the operational protocols and techniques of bibliometric tools such as CiteSpace and VOSviewer. Section 3 presents the analytical outcomes, encompassing a quantitative dissection of publication output, citation metrics, contributing authors, journals, disciplines, co-citation networks, keyword analysis, and the identification of research hotspots. Section 4 provides an in-depth comparative analysis of citation volumes among the Google Scholar, Scopus, and WOS databases, enriched by an investigation into the key influential literature. In this section, we have employed GPT-4.0 to conduct a meticulous analysis of academic publications. Section 5 encapsulates the research findings and discussions of this paper, further delineating new trends in the field and prospects for future exploration.

2. Data Collection and Research Methodology

2.1. Database

The WOS was developed by Eugene Garfield of the Institute for Scientific Information (ISI) in 1964 as an information retrieval tool. It was originally known as the SCI (Science Citation Index) and primarily used for citation indexing, with new citation indexes gradually added thereafter [19]. We selected only the WOS CC [20], which comprises over 21,100 globally distributed, peer-reviewed, high-quality academic journals (including open-access journals), covering over 250 disciplines in natural sciences, social sciences, arts, and humanities, as well as conference proceedings and books. Google Scholar, developed by Anurag Acharya, is a scholarly search engine that is part of the world’s largest search engine, Google [21]. It enables researchers to discover a vast array of the academic literature from various regions across the globe. Scopus, on the other hand, is a navigation tool produced by Elsevier. It is updated daily and includes abstracts and references cited since 1996 from over 14,000 active journals (or 16,000 if including inactive journals), covering all fields of knowledge [22]. This paper will compare the citation counts of the literature retrieved from the WOS database with those from Google Scholar and Scopus databases to validate the discrepancies in citation volumes across the three databases.

2.2. Construction of the Literature Dataset

The search terms were (TS = (altimeter or altimetry or Jason-1 or Jason-2 or Jason-3 or TOPEX/Poseidon or ERS-1 or ERS-2 or ENVISAT or Saral or ICESat or ICESat-2 or CryoSat-2 or Sentinel-3 or Sentinel-6 or GEOSAT or GFO or HY-2 or Haiyang-2)) AND TS = (“inland water” or wetland or lake or river or glacier or swamp or reservoir or coastal or sea or ocean). A total of 13,500 documents (from 1988 to 2022) were retrieved, including conference proceedings, review articles, data papers, online published papers, letters, news, and editorial materials. Table 1 presents information about the retrieved literature database, which comprises contributions from 4725 research institutions in 97 countries and regions, involving 23,677 authors and 1593 journals. On average, each paper was cited approximately 38.76 times and received 31.65 citations.

2.3. Scientometric Software

Scientometric analysis measures elements like the literature, authors, and publication counts in a given research domain. It aims to uncover the cutting edge of research, including key methodologies, trending topics, and emerging techniques, as well as to predict future directions in the field. These insights can provide scholars with new research ideas and guide novice researchers in choosing research directions. Scientometric software, developed based on the principles of scientometric analysis, enables the construction of various visualization network maps. The scientometric software utilized in this study includes CiteSpace and VOSviewer.
CiteSpace is an information visualization software developed by Dr. Chaomei Chen of Drexel University in the United States, based on Java language and citation analysis theory [23]. It allows for the numerical examination of extensive document collections and creates a “science knowledge map”. This map illustrates the structure, patterns, and spread of the scholarly literature. Within a research field, there exists a plethora of literature containing research frontier content that evolves over time. Visualizing these research frontiers aids in understanding the trends in the field. In this study, CiteSpace was used to conduct analyses like co-citation of authors, co-citation of documents, and keyword burst detection. These analyses explore how authors collaborate, how documents are interconnected, and shifts in the field’s research hotspots.
VOSviewer is a free software developed by Van Eck and Waltman of the Centre for Science and Technology Studies at Leiden University in the Netherlands in 2009. It is based on Java and primarily focuses on bibliometric data, with an emphasis on graphical visualization of bibliometric analyses [24]. The core functionality of VOSviewer is “co-occurrence clustering”, where the simultaneous appearance of two items indicates their correlation. This correlation exists in various types, with different strengths and directions. By clustering based on measures of relationship strength and direction, it can identify different types of groups. This study utilized VOSviewer for its excellent clustering visualization capabilities, analyzing author collaboration relationships and keyword clustering.
The two aforementioned software packages possess distinct characteristics. CiteSpace software excels in revealing the dynamic development patterns of disciplines and identifying research frontiers within disciplines. On the other hand, VOSviewer is more adept at presenting knowledge maps clearly when handling large volumes of literature data or conducting cluster analysis [25]. Therefore, this study integrates the strengths of both software packages to conduct a comprehensive visualization analysis of the quantitative relationships within the literature from multiple perspectives. Furthermore, although these two bibliometric tools can analyze a vast amount of literature data, they also have certain limitations. For instance, there is the issue of timeliness: since it takes time to update and process data, the latest research results may not be reflected in the results immediately, making it difficult to discover new research trends in a timely manner. Therefore, we also need to supplement these emerging research trends that may be delayed through literature reviews.

2.4. Technological Roadmap

First, we search the WOS database for literature on satellite altimetry for surface water monitoring using specific keywords. Next, we use bibliometric tools to examine publication numbers, citations, author details, and co-citation links among prominent authors. We also gather stats on the journals and fields where these papers appear. Then, by co-citation, keyword co-occurrence, and burst analyses, we identify hot research areas, topics and new trends. Lastly, we delve into papers in these hot areas to assess the current state and future trends of the field qualitatively, cross-checking our quantitative findings. The detailed tech roadmap is shown in Figure 1.

3. Results

3.1. Analysis of Publication Volume and Citation Frequency

We have compiled a total of 13,500 literature entries from 1988 to 2022 (as shown in Figure 2), revealing three distinct phases in the publication volume.
(1)
The period from 1988 to 1995 marks the nascent phase of development, coinciding with the pioneering era of satellite altimetry technology. During this time, satellites such as GEOSAT (GEOdetic Satellite) and Seasat emerged as prominent figures, laying the foundational stones for monitoring surface water via satellite altimetry [26]. Satellites were mainly for watching sea surface topography. Early satellites had trouble seeing clearly and staying steady. Because of this, not many papers were written (under 150). Most papers were first shared at academic conferences.
(2)
Between 1996 and 2011, satellite altimetry entered its mature phase, characterized by the remarkable success of a series of satellite missions, such as the ERS (European Remote Sensing) family and T/P (Topex/Poseidon) family satellites. During this period, the technology of satellite altimeters saw consistent refinements, resulting in remarkable enhancements in their accuracy and stability. For example, Jason-1 was primarily tasked with providing high-precision heights to ensure the continuity of ocean monitoring. Compared to T/P, Jason-1 reduced in mass and power consumption without compromising performance, capable of measuring ocean topography at the centimeter level [27]. These improvements paved the way for the progressive adoption of altimetry satellites in the monitoring of inland water bodies [28,29].
(3)
From 2011 to 2022, the number and types of altimetry satellites rose sharply, leading to a wealth of academic papers. A key event was China’s launch of the HY-2 (HaiYang-2) satellite in 2011, equipped with four instruments for global marine environmental monitoring [30]. Over these 11 years, Chinese researchers published 2067 papers, averaging 188 annually. The United States also stepped up its publication rate in this field, with an average of 95 papers per year in the first two phases, jumping to 204 papers per year during this period. Other countries like France and the UK (United Kingdom) have similarly boosted their publication output.
Citation count is crucial for gauging academic success. Papers with more citations typically have a larger academic influence. Figure 2 illustrates the yearly citation count for papers in this field, mirroring the trend in publication volume. Before 1995, the citation count was low due to the low publication volume, suggesting the field was in a slow growth phase. After 1996, the citation count rose in tandem with publication volume. The sudden jump in 1998 is due to Huang et al.’s [31] development of a method for analyzing nonlinear and non-stationary data. This innovative approach breaks down complex datasets into a few small “intrinsic mode functions”, revolutionizing the processing of altimetry data. By December 2023, this paper had been cited 14,850 times. From 1999 to 2019, the citation rate generally climbed rapidly (averaging 1595 times a year), peaking in 2019 at around 3700 times. Notably, despite the field’s continued growth in publication volume, the 2022 literature citation count saw a significant drop.

3.2. Author

To identify authors with major contributions in this field, we employed CiteSpace for author analysis. We have determined the centrality and h-index for the most active authors, considering their publication output. We also mapped out the author collaboration networks to reveal the dynamics of partnerships within the research community.

3.2.1. Author Centrality and H-Index Analysis

In the knowledge graph generated by CiteSpace, the centrality of a node refers to the number of shortest paths passing through that node, and its value reflects the influence and importance of a node in the network. The higher the centrality value, the more strategically important the node is in the network, and the greater its influence. Nodes with centrality values exceeding 0.1 are generally identified as central nodes, indicating their crucial roles in the network [32]. In the WOS database, the h-index (Hirsch index) is a widely used indicator to measure the productivity and impact of an author’s publications [33]. The h-index refers to a scholar with an index of h, having published h papers that have each been cited at least h times by other works [34]. Figure 3 presents the top 20 authors by citation count, with blue bars indicating the centrality information of each author and orange bars representing the h-index of the authors. The figure shows that a few authors have centrality values exceeding 0.1, indicating their significant influence in the field of satellite monitoring of surface water. However, there is no linear relationship between an author’s citation frequency and centrality. For instance, Chelton has a centrality value exceeding 0.1, and his research focuses on oceanic eddies, studying their variability and spatial distribution globally using satellite altimetry techniques [35], a topic frequently appearing in the literature retrieved for this study. Fu collaborates closely with Chelton, and both have made significant contributions to eddy research. Although Wingham’s paper has not been cited particularly frequently, his centrality exceeds 0.1, mainly contributing to glaciology, especially the study of ice cover in the Arctic region and changes in sea surface height in the Arctic Ocean [36]. Combining the research topics of these two authors, it can be observed that the field of satellite monitoring of surface water has prominent research in oceanic eddies and glaciers. Additionally, while the h-index of the authors in Figure 3 does not exhibit a linear relationship with publication count, most authors have an h-index above 40, indicating their high academic level and influence in this field.

3.2.2. Author Cooperation Analysis

The academic research landscape is intricately woven with a network of collaborations and scholarly exchanges. By juxtaposing the knowledge maps generated by CiteSpace and VOSviewer, we gain a deeper understanding of the collaborative patterns among scholars and discern the nuanced differences in how these two tools process identical datasets. Figure 4 illustrate the collaboration networks of approximately the top 1000 researchers in the domain of satellite altimetry for surface water studies, as depicted by these two bibliometric software applications. Figure 4a, crafted with CiteSpace, presents an author co-citation network with 994 nodes interconnected by 1225 ties. It delineates two principal research clusters. The first cluster, marked by lighter nodes and spearheaded by Cazenave, is anchored in research prior to 2008, focusing on sea-level monitoring via satellite altimetry [37]. This research is pivotal to understanding the global hydrological cycle’s influence on terrestrial and atmospheric water content. The second cluster, centered around Shum and predominantly post-2008, is dedicated to the application of satellite altimetry for inland water monitoring, exemplified by studies on the Amazon basin’s water levels through river radar altimetry [38]. Core authors such as Gómez-Enri and Srokosz serve as connectors, fostering tight-knit relationships between these two principal research factions. Contrastingly, Figure 4b, produced with VOSviewer using the same dataset, unveils additional smaller research teams, namely the Camps and Kumar groups. The Camps team primarily utilizes signals from the Global Navigation Satellite System to advance marine altimetry, while the Kumar team explores the applications of the SARAL/AltiKa (Satellite with Argos and AltiKa) satellite in altimetry, sea-ice measurement and sea-current monitoring. In essence, the analysis of author collaboration networks through CiteSpace and VOSviewer showcases the distinct advantages of each software. CiteSpace excels in revealing the intellectual progression within academic spheres and the interconnections among scholars. VOSviewer, conversely, excels in identifying and highlighting smaller, yet focused research communities. By synthesizing the insights from both tools, we observe a notable evolution in research focus over time, mirroring the academic community’s shifting interests in response to societal and environmental imperatives. In satellite altimetry, key topics revolve around the sea-level rise and the fluctuation of inland water levels. The precise quantification of the global mean sea-level rise and its geographic distribution, facilitated by satellite altimeter data, has enhanced our comprehension of both global and regional sea-level dynamics [39]. Furthermore, our analysis of the author collaboration network also spotlights the in-depth research conducted by smaller research teams in specific niches. This not only underscores the extensive and complex nature of academic collaboration networks but also illuminates the specialized domains and focal points of various research groups. Such insights are invaluable for gaining a multifaceted understanding of the collaborative dynamics within the field.

3.3. Journal Publication Volume and Discipline

This paper tallies the journal titles and disciplines of documents in the WOS database, encompassing 1593 journals across 67 disciplines. Figure 5a highlights the top 10 journals in this domain. The leaders are Journal of Geophysical Research: Oceans with 1183 articles, IEEE International Symposium on Geoscience and Remote Sensing with 757 articles, and Remote Sensing with 715 articles—journals all dedicated to remote sensing studies. This indicates that seminal works in satellite-based surface water monitoring concentrate on remote sensing and oceanography. Figure 5b details the publication counts by discipline. Geology with 4379 articles, remote sensing with 4373 articles, and oceanography with 4014 articles significantly outpace other fields. Other disciplines involved include engineering, water resources, and environmental and ecological science. This illustrates that multi-source satellite monitoring of surface water has emerged as an interdisciplinary research focus of interest to a variety of academic fields. Figure 5b illustrates that the study of surface water monitoring intersects with a multitude of Earth science disciplines, including remote sensing, geology, oceanography, hydrology, and meteorology. This research typically exhibits a pronounced interdisciplinary characteristic, where a single publication may integrate insights from several fields. Consequently, it is imperative that experts from diverse backgrounds collaborate in devising research strategies, synthesizing data from various sources, and leveraging a multidisciplinary array of theories and methodologies to address related themes effectively.

3.4. Literature Co-Citation Analysis

The literature co-citation analysis examines the interconnections among documents based on how often they are cited together. Given the extensive document set retrieved, the analysis was narrowed to visually represent the top 9% of documents, as determined by their citation frequency. Co-citation analysis serves a dual purpose: it indicates the prominence of literature through citation counts and reveals shifts in research topics and interests. Based on the three different periods mentioned earlier, the regions in Figure 6 can be roughly categorized into three main groups: (1) the deep blue region represents the citation status of the literature published by the group led by Fu, LL. Fu et al. [40] mainly focus on the application of the TOPEX/Poseidon satellite system in ocean circulation research and precise sea-level measurements; (2) the yellow and light-green regions represent the group led by Chelton, which primarily focuses on ocean eddy research, especially the exploration of mesoscale eddy variations in the global ocean [41]; (3) the red regions mainly represent the group led by Markus and his research team, who mainly conduct research on determining the elevation changes of glaciers and ice sheets using laser altimetry technology [42]. It is from this stage of research that the field gradually expands to inland water bodies. Markus’s 2017 publication has notably become the most cited, indicating a surge in interest and attention towards glacier research, especially as global greenhouse effects become more pronounced. The research of a glacier includes the elevation of the glacier, the topography of the ice surface, the thickness of ice and snow, and the evolution of the ice sheet. Analyzing these three categories reveals an evolution in research interests over time. Initially, research was predominantly centered on ocean circulation and sea-level measurements. This focus then shifted in the mid-term to the more complex study of ocean eddies. Presently, the field is directing significant efforts towards understanding changes in glaciers and ice sheets. This evolution reflects not only the complexity and diversity of scientific research itself, but also the impact of global environmental change on research issues.

3.5. Research Area

Hotspot analysis of research areas offers a multifaceted look at the study of different water body types. It illustrates the global distribution of research on various water bodies and their key focus areas. Figure 7a indicates the literature percentage for each water body type, with oceans leading at 75%, followed by rivers at 9%, lakes at 7%, glaciers at 5%, and wetlands at 2%. Marine research takes a leading role in the application of satellite altimetry, attributed to its extensive research history, broad thematic scope, and early technological compatibility. Since the 1970s, satellite altimetry has been primarily dedicated to oceanography, encompassing sea-level changes, ocean currents, marine gravity fields, and bathymetric mapping. The advent of new altimeters, with enhanced precision and resolution, has spurred rapid development in inland water body research, particularly in the last decade and a half, where the application of inland water monitoring has seen a significant increase. Figure 7b details the top five research areas for the mentioned water bodies. In ocean studies, the Pacific Ocean, Atlantic Ocean, Indian Ocean, Arctic Ocean, and South China Sea are the top hotspots, with publication counts of 1922, 1773, 963, 805, and 758, respectively. These numbers significantly surpass those of other water body types. In the field of oceanic research, scientific issues such as monitoring trends in sea-level change, improving the accuracy of marine gravity field models, analyzing the impact of ocean circulation patterns on global climate, and studying the role of material cycles in the ocean on global change have attracted significant attention [43]. River research is focused on the Amazon River (167), Yangtze River (87), Tibetan Plateau rivers (74), Mekong River (69), and Mississippi River (35). This concentration highlights the unique environmental significance and research demands of these river basins. How to use satellite altimetry data to estimate the elevation changes of inland rivers, as well as issues related to large, multi-channel, and branched river systems in remote areas, have been subjects of ongoing interest [44]. For lake research, hotspots include the Tibetan Plateau lakes (190), the Great Lakes of the United States (116), Nam co Lake (59), Poyang Lake (57), and the Amazon basin lakes (56). This distribution reflects the geographical spread of studies on lake hydrological changes. Interestingly, despite being primarily a river region, the Amazon basin shows up more frequently in lake research. This is largely because researchers compare the Amazon basin when assessing the accuracy of altimetry satellites over lakes, to validate the differences in monitoring techniques for lakes versus the Amazon. The issues of water-level inversion for lakes and the development of long-term, stable global water-level datasets have become prominent scientific questions of the moment [45]. In glacier research, Greenland (244), Tibetan Plateau glaciers (117), Alaska (83), Svalbard glaciers (77), and the Himalayas (51) are key focal points. Studying these regions can enhance our understanding of glacier dynamics in the context of global warming. The scientific issues of sea-level rise caused by the melting of polar ice sheets, marine ice floes, and terrestrial glaciers in polar regions have garnered widespread attention in recent years [46]. Wetland research, though less abundant, has hotspots in the Amazon basin wetlands (43), South Florida wetlands (11), Tibetan Plateau wetlands (10), Yangtze River basin wetlands (8), and Mekong River basin wetlands (6). The problem of wetland monitoring in some large watersheds has been paid attention to. Its hot scientific issues are the seasonal and interannual changes in wetland water levels, and the natural and human factors that affect wetland changes.
The analysis of hotspot research areas underscores the Tibetan Plateau and the Amazon basin as focal points for inland water body research. This prominence is largely due to their distinctive geographical attributes and their responses to climate change. The Tibetan Plateau, dubbed the “Roof of the World”, hosts lakes that are relatively untouched by human activity, offering an ideal setting to observe natural systems’ reactions to climate change. Additionally, the lakes in the Tibetan Plateau region exhibit a significant response to climate change. Through the use of altimetry data, a comprehensive overview of the changes in these lakes can be obtained [47]. With advancements in next-generation radar altimeters, the precision of altimetry data is on the rise [48], furnishing more dependable information for examining the alterations in glaciers and lakes in this region. Current studies suggest that radar altimeters function as virtual glacier monitoring stations, supplying crucial data for tracking glacier melt driven by climate change [49]. Some scholars leverage data from multiple altimeters to craft models that generate extended time series of lake surface data on the Tibetan Plateau. As a result, research interest in this area is escalating. The Amazon basin stands as the world’s largest river basin, undergoing notable land–water transformations. Given the paucity of quantitative research on the Amazon’s anomalous climate using conventional hydrological observation data, scholars are pivoting to multi-source altimetry satellites to monitor the basin’s land–water shifts. Their studies concentrate on gauging and forecasting water-level fluctuations. For instance, research by Schwatke et al. [50] shows that the SARAL satellite can offer reasonably precise water-level data for the Amazon basin, laying a vital foundation for accurate long-term river level estimation. These insights reveal that, while marine regions in the field of satellite-based surface water monitoring result in a substantial amount of literature annually, there is a burgeoning interest among researchers in probing significant inland water body areas. The Tibetan Plateau and the Amazon basin, in particular, are capturing the spotlight in these scientific inquiries.

3.6. Keywords

3.6.1. Keyword Co-Occurrence Analysis

Co-occurrence analysis groups keywords that frequently appear together, creating visual clusters that represent significant themes within a research domain. This method allows for a quick and clear visualization of the field’s key topics and their interconnections, enhancing our grasp of the research landscape’s structure. In the study at hand, VOSviewer software was employed to generate a keyword co-occurrence network graph, as depicted in Figure 8. This graph illustrates the relationships among the top 150 keywords associated with satellite monitoring of surface water, categorized into four distinct clusters, each represented by a different color. The size of each keyword node in the graph is indicative of its significance or weight within the dataset—the larger the node, the greater its weight. The blue cluster is dedicated to glacier-related research, encompassing terms that pertain to the monitoring of glaciers, the changes they undergo, and their broader environmental effects. The green cluster addresses topics related to vast marine areas, such as ocean tides and the seasonal fluctuations in sea levels, indicative of a global marine focus. The red cluster concentrates on inland water bodies, including but not limited to lakes and rivers. The yellow cluster zeroes in on satellites used in altimetry, with keywords including specific satellite models like GEOSAT and Jason-1. A notable observation from Figure 8 is that the nodes within the green cluster are generally larger, suggesting that a substantial portion of research efforts continues to be allocated to the study of oceanic regions. This observation underscores the ongoing importance of oceanic research within the broader context of satellite monitoring of surface water.

3.6.2. Keyword Burst Analysis

“Burst” denotes a concept that rapidly gains prominence or increases significantly within a specific timeframe. Keyword burst analysis identifies keywords that experience a sharp increase in frequency and rapid growth by examining their temporal distribution. This method helps in analyzing the emerging areas and trends in a discipline. Figure 9 illustrates the burst diagram for the top 52 selected keywords, segmented into three periods corresponding to the previously mentioned timeframes: the early period from 1988 to 1996 (blue), the middle period from 1997 to 2011 (red), and the recent period from 2012 to 2022 (green). Each solid circle represents the peak year of research for each keyword, with larger circles signifying greater research intensity. The duration between the start and end years for each keyword indicates a period of concentrated research activity. The keywords have been categorized into five distinct classes: area, method, satellite, theme, and others. The research area focus has shifted. It was once on large marine areas. Now, it is on the Tibetan Plateau. We have identified that current research methods primarily encompass deep learning and machine learning techniques. Regarding satellites, initial research focused primarily on GEOSAT. In the middle period, with satellites like the ERS-1 (European Remote Sensing 1) satellite enhancing the precision of satellite observations, global-scale ocean research commenced. Currently, the research emphasis is shifting towards satellites like CryoSat-2. Our analysis reveals that while early research encompassed a broad spectrum of topics, contemporary studies have progressively focused on specific themes such as surface water and climate change. During the early research period, there was intense research on altimetry data, suggesting that researchers were focused on effectively utilizing altimetry data at the nascent stages of this technology. As time advanced, especially post-2012, further improvements in satellite precision facilitated efficient monitoring of inland water bodies. This led to a research pivot towards topics such as “ice thickness”, “water level”, “snow depth”, etc., reflecting growing scholarly interest in glacier fields, partly due to the recent melting of some major glaciers. In this context, Gardner et al. [51] evaluated the contribution of various glaciers to a sea-level rise by integrating satellite gravity measurements with altimetry techniques. Baumhoer et al. [52] investigated the application of deep learning algorithms to model glacier terminus velocities. These shifts underscore that with enhanced hardware and algorithmic progress, researchers can adopt diverse methodologies to study glacier regions. Beyond the aforementioned areas, our classification reveals a notable trend in the “others” category, where “coastal” emerges as a prominent keyword. This underscores a burgeoning focus on harnessing altimetry technology for coastal regions, reflecting the current trajectory of research in this field. Keyword burst analysis reveals that research in satellite monitoring of surface water is progressively moving towards the examination of inland water bodies. Moreover, with the progression of computer machine learning algorithms, future research is anticipated to simulate changes in inland water bodies, aiming to deduce broader global-scale water body transformations.

4. Discussion

4.1. Database Comparison

Figure 10 shows a comparison of citation counts for a sample of 100 papers across three databases. Red circles indicate the WOS versus Google Scholar comparison, with a regression line described by y1 = 1.645x − 43.781 and a correlation coefficient R1 of 0.983. Purple triangles show the WOS versus Scopus comparison, with the regression line y2 = 1.144x − 19.516 and a correlation coefficient R2 of 0.994. The slopes of 1.645 (WOS vs. Google Scholar) and 1.144 (WOS vs. Scopus) suggest that the WOS generally has lower citation counts than the other two databases. This is largely because Google Scholar and Scopus encompass a broader array of journals, leading to higher citation numbers [53]. Google Scholar taps into a variety of resources, including Google Books, Google Patents, and Google Scholar Metrics, and covers diverse document types. It also captures citations from the non-English literature and has been known to have issues with duplicate citations [54,55]. Scopus, developed by Elsevier, offers broader coverage in the STM (Science, Technology, and Management) fields compared to the WOS CC [56]. The high correlation coefficients for citations from the WOS when compared to Google Scholar and Scopus, both above 0.98, signify a strong positive correlation. This also underscores the reliability of the literature data used for quantitative analysis in this study. However, due to differences in the coverage and citation counts of various databases, it is essential to consider data from multiple literature databases when conducting reviews or evaluating the impact of papers to ensure the robustness and accuracy of bibliometric analyses.

4.2. Top Paper Analysis

We performed an exhaustive review of 89 papers sourced from our database, comprising 18 review articles and 71 research papers of various other categories. By employing the GPT-4.0 AI tool, we sought to investigate the feasibility of harnessing artificial intelligence to streamline and hasten the process of literature content analysis. This exploration underscores a forward-looking approach to integrating advanced technologies in the academic review process, potentially enhancing efficiency and the depth of analysis.

4.2.1. Analysis of Hot Papers

The 18 review articles spanned from 2013 to 2021, with a primary focus on the research progress of a global sea-level rise [57,58]. Within this collection, Abraham et al. explored the impact of thermal expansion and glacier melting on the sea-level rise, utilizing data from tide gauges and radar altimeters. By 2021, Garcia et al. expanded the understanding of global ocean changes by examining variations in ocean climate indicators and employing visualization techniques to analyze these indicators. Researchers such as Ablain, Woodworth, Cazenave, and Wöppelmann [59,60,61,62] concentrated on specific factors contributing to sea-level changes. These factors included ocean thermal expansion, glacier melting, the reduction of Greenland and Antarctic ice sheets, and changes in land water storage, with assessments of their individual impacts on the sea-level rise. This body of literature offers crucial overviews of the field and reviews of technological advancements, guiding future research directions. The studies underscore the complexity of the issue of a sea-level rise and highlight the pivotal role of satellite altimetry in monitoring and assessing this global challenge. Relevant research suggests that with improvements in monitoring technology and the evolution of data analysis methods, scientists are better equipped to understand and predict a sea-level rise and its potential impacts. This enhanced capability provides a scientific foundation for tackling the challenges posed by global climate change.
The remaining 71 non-review articles are broadly divided into two categories: technical and engineering articles, which number 8, and application and research articles, totaling 59. This division underscores the wide-ranging and varied disciplines and knowledge areas engaged within this research field. The technical and engineering articles are primarily concerned with detailing the intricacies of satellite altimetry technology and the methodologies for processing the satellite data. An example of this is the work by Taburet et al. [63], which introduced altimetry technology and products that integrate data from multiple satellites, showcasing the technical focus typical of this category of the literature. On the other hand, the application and research articles delve into subjects across Earth sciences, including geophysics, environmental studies, oceanography, and climatology. These articles involve the use of satellite data for monitoring surface changes, analyzing ocean dynamics, studying climate change, conducting environmental surveillance, and more. The diverse array of topics and the extensive scope of disciplines indicate the growing significance of satellite altimetry technology and its applications in scientific research. The interdisciplinary quality of this technology positions it as an essential tool in a multitude of research domains.

4.2.2. GPT4.0 Literature Reading

The current study meticulously selected 60 key hotspot papers for review, consisting of 25 review articles and 35 research papers. We embarked on a comparison of insights gleaned from traditional manual reading versus those obtained through the application of GPT-4.0, with the goal of ascertaining the potential of GPT-4.0 to supplant human readership in the context of a literature review. To achieve this objective, we formulated a variety of questions tailored to different types of literature. The experimental findings indicate that GPT-4.0 can extract the main information from the literature in an average time of about 5 min, significantly less than the duration required for manual reading. It also maintains a high level of accuracy, thus holding the promise to enhance the efficiency of a literature review for researchers. Table 2 presents a detailed account of the accuracy of the answers derived from different types of literature. Manifestly, GPT-4.0 demonstrates exceptional precision in deriving conclusions and extracting pertinent data from scholarly works, achieving an accuracy that can peak at 92%. However, we also encountered occasional inaccuracies in GPT-4.0’s responses. A case in point is the instance pertaining to the paper by Laxon et al. [64]; when asked about the data utilized, GPT-4.0 erroneously mentioned records of submarine underwater grass, whereas the actual focus of the paper was on the melting of Arctic Sea ice. We surmise that this deviation may be attributed to a mistranslation of specialized terminology. Previous research has also highlighted that artificial intelligence might grapple with ambiguity when addressing complex research articles, potentially resulting in nuanced divergences between AI-generated interpretations and those of human readers [18]. Additionally, examining Table 2 reveals a decline in the precision of our questions. These questions are meant to clarify new insights from the literature and evaluate its contributions to various fields. The accuracy rate has now fallen to 88%. This reduction in accuracy may stem from the fact that some papers did not explicitly address these elements, and the responses generated by GPT-4.0, which were based on contextual inference, might not consistently align with the original text. In light of these findings, while artificial intelligence technology boasts a commendable accuracy rate and the capacity to enhance reading efficiency, it is imperative to exercise caution and verify the correctness of the results yielded during its application.

4.3. Satellite Sensor

The history of altimetric satellites is a microcosm of the progress in Earth observation technology. Table 3 outlines key information about major altimetric satellites. Starting with the launch of GEOSAT in 1985, equipped with a Navy radar altimeter, we embarked on an in-depth exploration of oceanography and geodesy. The subsequent addition of ERS-1 and ERS-2, with their Ku-band Radar Altimeter, further refined the measurement of ocean surface topography. Entering the 21st century, the launches of ICESat (2003) and ICESat-2 (2018) marked a new era in the monitoring of ice sheets and sea ice. The Advanced Topographic Laser Altimeter System (ATLAS) on the ICESat-2 satellite provides a breakthrough advantage for high-precision measurements in coastal areas and shallow-water topography. This system has high spatial resolution, capable of capturing subtle changes in underwater terrain, and expands the detection range through innovative multi-beam technology [65]. In clear waters such as the Great Barrier Reef, ICESat-2 can achieve centimeter-level accuracy in depth measurements [66]. The deployment of Geoscience Laser Altimeter System and ATLAS laser altimeters not only enhanced measurement precision but also deepened our understanding of changes in polar ice caps. The latest generation of SWOT (Surface Water and Ocean Topography) satellites, furnished with advanced sensors including the Ka-band Interferometric Radar Altimeter and both Ku-band and C-band Nadir-looking Radar Altimeters, has significantly enhanced our ability to observe changes in oceanic and terrestrial hydrology. The evolution of these satellites and their sensors has collectively built a multidimensional, high-precision Earth observation network, providing invaluable data and insights for our comprehension of the Earth system.
Current altimetry data primarily relies on optical and infrared remote sensors, with microwave remote sensing data accounting for a relatively small proportion due to its lower spatial resolution. Traditional altimeters have inherent sampling issues, such as dense sampling along the orbit and sparse, asymmetric sampling across the orbit. However, the successful application of the new generation of wide-swath interferometric imaging altimeters promises to make high-temporal and spatial resolution, long-term continuous monitoring, and seamless coverage of sea surface height measurements a reality on a global scale. The massive amount of altimetry data brought by this technology will further promote the new development of marine big data in the field of sub-mesoscale oceanography [67]. The SWOT satellite, as a representative of the new generation of altimetry satellites, uses wide-swath interferometric altimetry technology to provide high-resolution ocean and surface water data. At the same time, constellation technology, as another important direction in the development of altimetry satellite sensors [68], also indicates the broad prospects for future ocean observation.

5. Conclusions

The present study employed both scientometric analysis and manual reading methods to conduct qualitative and quantitative research on the literature in the field of satellite monitoring of surface water. We conducted analyses such as journal and subject analysis, co-citation analysis, hotspot research area analysis, keyword analysis, etc. Additionally, we focused on reading hotspot papers and utilized GPT-4.0 artificial intelligence as an auxiliary reading tool to explore the application potential of artificial intelligence in literature analysis and to analyze the current research status in this field.
We find that: (1) Between 1988 and 2022, publications exhibited a fluctuating growth trend, indicating continued research by numerous scholars in the field of satellite altimetry and surface water. Although marine research predominates, there has been a gradual shift towards the study of inland large-scale water bodies in recent years, such as the Tibetan Plateau and the Amazon River basin. In terms of citation volume, the Google Scholar database has higher citation counts compared to the Scopus and WOS databases. (2) The collaboration patterns among researchers in this field show distinct temporal variations. Before 2000, researchers led by Cazenave primarily focused on using satellite altimetry data to monitor changes in the sea level. After 2000, researchers led by Shum and Chapron began to pay attention to inland water bodies. Current research interests mainly focus on sea level, snow depth, coastal areas, etc. The future research trend is to use machine learning algorithms to predict the temporal change sequences of the coastal sea level and glacier snow depth, enabling researchers to better understand the dynamic distribution changes of global water resources and address increasingly severe challenges in water resource management and climate change, (3) Journal and discipline analysis revealed that the main disciplines in this field are geology, remote sensing science, and oceanography, with remote sensing journals being predominant. This indicates that with the improvement of satellite accuracy, researchers can obtain hydrological data from regions that were previously inaccessible to humans using remote sensing satellites. (4) Through analysis of hotspot papers, we found that specific research topics cover monitoring of global water storage changes, glacier melting, sea-level-change research, etc., and artificial intelligence technology can assist researchers in accelerating literature reading to some extent.
In a meticulous review of the literature on satellite altimetry for surface water monitoring spanning from 1988 to 2022, we encapsulate the field’s evolution and current status. Looking ahead, satellite altimetry technology is poised for advancement towards higher spatial and temporal resolutions, enabling seamless and dynamic surveillance of global water resource fluctuations. The ongoing development and deployment of innovative satellite sensors, exemplified by the SWOT (Surface Water and Ocean Topography) satellite, promise to elevate the precision and scope of surface water monitoring, particularly enhancing the frequency and expanse of data collection. The harnessing of machine learning and deep learning algorithms is set to refine data processing methodologies, augmenting the efficacy and precision with which valuable insights are gleaned from vast satellite datasets. Furthermore, interdisciplinary collaboration stands out as a pivotal trajectory for future progress. The convergence of surface water monitoring with disciplines like climate science, environmental science, and geographic information science will foster a more holistic comprehension of the Earth’s system. Theis paper also underscores the imperative for future research to concentrate on the utilization of knowledge maps. These tools are pivotal for the systematic integration and analysis of data, unearthing profound scientific questions and potential application horizons within the domain of surface water monitoring. Knowledge graphs will be instrumental in forging a more holistic and interconnected research vista. Ultimately, with the escalation of international collaboration, surface water monitoring research is destined to embrace a more globalized future. Through joint ventures on international projects, the sharing of data resources, research findings, and monitoring technologies will be pivotal in tackling worldwide water and environmental challenges.

Author Contributions

Conceptualization, Z.H.; methodology, Z.H. and R.S.; software, X.W. and R.S.; formal analysis, X.W.; validation, Z.H. and H.W.; writing—original draft preparation, R.S.; writing—review and editing, Z.H. and H.W.; supervision, Z.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was sponsored by the National Natural Science Foundation of China (42264001, 42204047, 42364002), the Open Research Program of Key Laboratory of Marine Environmental Survey Technology and Application, the Ministry of Natural Resources (MESTA-2020-A004), the Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province (20225BCJ23014), and the Hebei Water Conservancy Research Plan (2022-28).

Data Availability Statement

The data utilized in this article are sourced from the renowned WOS Core Collection database (https://www.webofscience.com/wos/woscc/basic-search (accessed on 21 September 2023)). For bibliometric analysis, we employed CiteSpace (V6.1.R6) in its 64-bit Advanced configuration (https://citespace.podia.com/ (accessed on 26 September 2023)) and VOSviewer (V1.6.19), available at VOSviewer’s website (https://www.vosviewer.com/ (accessed on 22 September 2023)). For graphical representations, we utilized OriginPro 2024, a 64-bit software in its Student Edition (SR1, V10.1.0.178), which is available for download at OriginLab’s official site (https://www.originlab.com/ (accessed on 1 December 2023)). Additionally, we incorporated GMT (V6.0.0-win64) for certain graphical tasks, and detailed installation instructions can be found on the GMT China’s documentation page (https://docs.gmt-china.org/6.0/ (accessed on 18 April 2024)).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Pekel, J.F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2021, 540, 418–422. [Google Scholar] [CrossRef]
  2. Chuai, X.; Yuan, Y.; Zhao, R.; Song, S. High-Resolution Monitoring of Inland Water Bodies across China in Long Time Series and Water Resource Changes. Environ. Dev. Sustain. 2021, 23, 3673–3695. [Google Scholar] [CrossRef]
  3. DeConto, R.M.; Pollard, D.; Alley, R.B.; Velicogna, I.; Gasson, E.; Gomez, N.; Sadai, S.; Condron, A.; Gilford, D.M.; Ashe, E.L.; et al. The Paris Climate Agreement and future sea-level rise from Antarctica. Nature 2021, 593, 83–89. [Google Scholar] [CrossRef]
  4. Yao, F.; Livneh, B.; Rajagopalan, B.; Wang, J.; Crétaux, J.F.; Wada, Y.; Berge-Nguyen, M. Satellites reveal widespread decline in global lake water storage. Science 2023, 380, 743–749. [Google Scholar] [CrossRef]
  5. Farouk, M.I.H.Z.; Jamil, Z.; Latip, M.F.A. Towards Online Surface Water Quality Monitoring Technology: A Review. Environ. Res. 2023, 238, 117147. [Google Scholar] [CrossRef] [PubMed]
  6. Prigent, C.; Jimenez, C.; Bousquet, P. Satellite-derived global surface water extent and dynamics over the last 25 years (GIEMS-2). J. Geophys. Res. Atmos. 2020, 125, e2019JD030711. [Google Scholar] [CrossRef]
  7. Yan, Q.; Liu, S.; Chen, T.; Jin, S.; Xie, T.; Huang, W. Mapping Surface Water Fraction Over the Pan-tropical Region Using CYGNSS Data. IEEE Trans. Geosci. Remote Sens. 2024, 62, 1–14. [Google Scholar] [CrossRef]
  8. Huang, C.; Chen, Y.; Zhang, S.; Wu, J. Detecting, Extracting, and Monitoring Surface Water from Space Using Optical Sensors: A Review. Rev. Geophys. 2018, 56, 333–360. [Google Scholar] [CrossRef]
  9. Huang, Z.; Wu, X.; Wang, H.; Hwang, C.; He, X. Monitoring Inland Water Quantity Variations: A Comprehensive Analysis of Multi-Source Satellite Observation Technology Applications. Remote Sens. 2023, 15, 3945. [Google Scholar] [CrossRef]
  10. Schwatke, C.; Dettmering, D.; Bosch, W.; Seitz, F. DAHITI–an Innovative Approach for Estimating Water Level Time Series over Inland Waters Using Multi-Mission Satellite Altimetry. Hydro. Earth Syst. Sci. 2015, 19, 4345–4364. [Google Scholar] [CrossRef]
  11. Church, J.A.; White, N.J. Sea-Level Rise from the Late 19th to the Early 21st Century. Surv. Geophys. 2011, 32, 585–602. [Google Scholar] [CrossRef]
  12. Xu, N.; Ma, Y.; Wei, Z.; Huang, C.; Li, G.; Zheng, H.; Wang, X.H. Satellite observed recent rising water levels of global lakes and reservoirs. Environ. Res. Lett. 2022, 17, 074013. [Google Scholar] [CrossRef]
  13. Kouraev, A.V.; Shimaraev, M.N.; Buharizin, P.I.; Naumenko, M.A.; Crétaux, J.-F.; Mognard, N.; Legrésy, B.; Rémy, F. Ice and Snow Cover of Continental Water Bodies from Simultaneous Radar Altimetry and Radiometry Observations. Surv. Geophys. 2008, 29, 271–295. [Google Scholar] [CrossRef]
  14. Li, J.; Goerlandt, F.; Reniers, G. An Overview of Scientometric Mapping for the Safety Science Community: Methods, Tools, and Framework. Saf. Sci. 2021, 134, 105093. [Google Scholar] [CrossRef]
  15. Eito-Brun, R. A Quantitative Approach to the Scientific Production on Radar Altimetry. Adv. Space Res. 2021, 68, 1216–1224. [Google Scholar] [CrossRef]
  16. Yang, L.; Lin, L.; Fan, L.; Liu, N.; Huang, L.; Xu, Y.; Mertikas, S.P.; Jia, Y.; Lin, M. Satellite Altimetry: Achievements and Future Trends by a Scientometrics Analysis. Remote Sens. 2022, 14, 3332. [Google Scholar] [CrossRef]
  17. Huang, J.; Tan, M. The Role of ChatGPT in Scientific Communication: Writing Better Scientific Review Articles. Am. J. Cancer Res. 2023, 13, 1148–1154. [Google Scholar] [PubMed]
  18. Biswas, S.; Dobaria, D.; Cohen, H.L. Focus: Big Data: ChatGPT and the Future of Journal Reviews: A Feasibility Study. Yale J. Biol. Med. 2023, 96, 415–420. [Google Scholar] [CrossRef] [PubMed]
  19. Singh, V.K.; Singh, P.; Karmakar, M.; Leta, J.; Mayr, P. The Journal Coverage of Web of Science, Scopus and Dimensions: A Comparative Analysis. Scientometrics 2021, 126, 5113–5142. [Google Scholar] [CrossRef]
  20. Liu, W. The Data Source of This Study Is Web of Science Core Collection? Not Enough. Scientometrics 2019, 121, 1815–1824. [Google Scholar] [CrossRef]
  21. Noruzi, A. Google Scholar: The New Generation of Citation Indexes. libri 2005, 55, 170–180. [Google Scholar] [CrossRef]
  22. Moya-Anegón, F.; Chinchilla-Rodríguez, Z.; Vargas-Quesada, B.; Corera-Álvarez, E.; Muñoz-Fernández, F.J.; González-Molina, A.; Herrero-Solana, V. Coverage Analysis of Scopus: A Journal Metric Approach. Scientometrics 2007, 73, 53–78. [Google Scholar] [CrossRef]
  23. Chen, C. CiteSpace II: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature. J. Am. Soc. Inf. Sci. 2006, 57, 359–377. [Google Scholar] [CrossRef]
  24. Van Eck, N.; Waltman, L. Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [PubMed]
  25. Jian, F.; Jingda, D. Comparison of Visualization Principles between Citespace and Vosviewer. J. Libr. Inf. Sci. Agric. 2019, 31, 31–37. [Google Scholar]
  26. Porter, D.L.; Glenn, S.M.; Dobson, E.B.; Crowley, M.F. Extension and Validation of a Gulf Stream GEOSAT Synthetic Geoid. J. Atmos. Ocean. Technol. 1996, 13, 514–531. [Google Scholar] [CrossRef]
  27. Ménard, Y.; Fu, L.-L.; Escudier, P.; Parisot, F.; Perbos, J.; Vincent, P.; Desai, S.; Haines, B.; Kunstmann, G. The Jason-1 Mission Special Issue: Jason-1 Calibration/Validation. Mar. Geod. 2003, 26, 131–146. [Google Scholar] [CrossRef]
  28. Crétaux, J.-F.; Calmant, S.; Romanovski, V.; Shabunin, A.; Lyard, F.; Bergé-Nguyen, M.; Cazenave, A.; Hernandez, F.; Perosanz, F. An Absolute Calibration Site for Radar Altimeters in the Continental Domain: Lake Issykkul in Central Asia. J. Geod. 2009, 83, 723–735. [Google Scholar] [CrossRef]
  29. Cheng, K.-C.; Kuo, C.-Y.; Tseng, H.-Z.; Yi, Y.; Shum, C.K. Lake Surface Height Calibration of Jason-1 and Jason-2 Over the Great Lakes. Mar. Geod. 2010, 33, 186–203. [Google Scholar] [CrossRef]
  30. Jiang, X.; Lin, M.; Liu, J.; Zhang, Y.; Xie, X.; Peng, H.; Zhou, W. The HY-2 Satellite and Its Preliminary Assessment. Inter. J. Digi. Earth 2012, 5, 266–281. [Google Scholar] [CrossRef]
  31. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.-C.; Tung, C.C.; Liu, H.H. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis. Proc. R. Soc. London. Ser. A 1998, 454, 903–995. [Google Scholar] [CrossRef]
  32. Zheng, K.; Wang, X. Publications on the Association between Cognitive Function and Pain from 2000 to 2018: A Bibliometric Analysis Using CiteSpace. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 2019, 25, 8940–8951. [Google Scholar] [CrossRef] [PubMed]
  33. Hirsch, J.E. An Index to Quantify an Individual’s Scientific Research Output. Proc. Natl. Acad. Sci. USA 2005, 102, 16569–16572. [Google Scholar] [CrossRef] [PubMed]
  34. Kay, J.; Memon, M.; De Sa, D.; Simunovic, N.; Duong, A.; Karlsson, J.; Ayeni, O.R. The H-Index of Editorial Board Members Correlates Positively with the Impact Factor of Sports Medicine Journals. Orthop. J. Sports Med. 2017, 5, 232596711769402. [Google Scholar] [CrossRef]
  35. Fu, L.-L.; Chelton, D.B.; Le Traon, P.-Y.; Morrow, R. Eddy Dynamics from Satellite Altimetry. Oceanography 2010, 23, 14–25. [Google Scholar] [CrossRef]
  36. Armitage, T.W.K.; Bacon, S.; Ridout, A.L.; Thomas, S.F.; Aksenov, Y.; Wingham, D.J. Arctic Sea Surface Height Variability and Change from Satellite Radar Altimetry and GRACE, 2003–2014. J. Geophys. Res. Ocean. 2016, 121, 4303–4322. [Google Scholar] [CrossRef]
  37. Minster, J.F.; Cazenave, A.; Serafini, Y.V.; Mercier, F.; Gennero, M.C.; Rogel, P. Annual Cycle in Mean Sea Level from Topex–Poseidon and ERS-1: Inference on the Global Hydrological Cycle. Glob. Planet. Chang. 1999, 20, 57–66. [Google Scholar] [CrossRef]
  38. Seyler, F.; Calmant, S.; Da Silva, J.S.; Moreira, D.M.; Mercier, F.; Shum, C.K. From TOPEX/Poseidon to Jason-2/OSTM in the Amazon Basin. Adv. Space Res. 2013, 51, 1542–1550. [Google Scholar] [CrossRef]
  39. Cipollini, P.; Calafat, F.M.; Jevrejeva, S.; Melet, A.; Prandi, P. Monitoring Sea Level in the Coastal Zone with Satellite Altimetry and Tide Gauges. Integr. Study Mean Sea Level Its Compon. 2017, 38, 33–57. [Google Scholar]
  40. Fu, L.-L.; Christensen, E.J.; Yamarone, C.A.; Lefebvre, M.; Ménard, Y.; Dorrer, M.; Escudier, P. TOPEX/POSEIDON Mission Overview. J. Geophys. Res. 1994, 99, 24369–24381. [Google Scholar] [CrossRef]
  41. Chelton, D.B.; Schlax, M.G.; Samelson, R.M. Global Observations of Nonlinear Mesoscale Eddies. Prog. Oceanogr. 2011, 91, 167–216. [Google Scholar] [CrossRef]
  42. Markus, T.; Neumann, T.; Martino, A.; Abdalati, W.; Brunt, K.; Csatho, B.; Farrell, S.; Fricker, H.; Gardner, A.; Harding, D. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2): Science Requirements, Concept, and Implementation. Remote Sens. Environ. 2017, 190, 260–273. [Google Scholar] [CrossRef]
  43. Sun, Z.; Guan, B.; Zhai, Z.; Ouyang, M. Research progress of ocean satellite altimetry and its recovery of global marine gravity field and seafloor topography model. Acta Geod. Cartogr. Sin. 2022, 51, 923–934. [Google Scholar]
  44. Jarihani, A.; Callow, J.; Johansen, K.; Gouweleeuw, B. Evaluation of multiple satellite altimetry data for studying inland water bodies and river floods. J. Hydrol. 2013, 505, 78–90. [Google Scholar] [CrossRef]
  45. Shu, S.; Liu, H.; Beck, R.; Frappart, F.; Korhonen, J.; Lan, M.; Xu, M.; Yang, B.; Huang, Y. Evaluation of historic and operational satellite radar altimetry missions for constructing consistent long-term lake water level records. Hydrol. Earth Syst. Sci. 2021, 25, 1643–1670. [Google Scholar] [CrossRef]
  46. Wang, X.; Cheng, X.; Gong, P.; Huang, H.; Li, Z.; Li, X. Earth science applications of ICESat/GLAS: A review. Inter. J. Remote Sens. 2011, 32, 8837–8864. [Google Scholar] [CrossRef]
  47. Jiang, L.; Liu, J.; Zhang, X.X. Monitoring lakes and reservoirs using satellite radar altimetry: Theory, methods, and progresses. J. Remote Sens. 2022, 26, 104–114. [Google Scholar] [CrossRef]
  48. Jiang, L.; Nielsen, K.; Andersen, O.B. Improvements in Mountain Lake Monitoring from Satellite Altimetry over the Past 30 Years–Lessons Learned from Tibetan Lakes. Remote Sens. Environ. 2023, 295, 113702. [Google Scholar] [CrossRef]
  49. Hwang, C.; Wei, S.-H.; Cheng, Y.-S.; Abulaitijiang, A.; Andersen, O.B.; Chao, N.; Peng, H.-Y.; Tseng, K.-H.; Lee, J.-C. Glacier and Lake Level Change from TOPEX-Series and CryoSat-2 Altimeters in Tanggula: Comparison with Satellite Imagery. Terr. Atmos. Ocean. Sci. 2021, 32, 1–20. [Google Scholar] [CrossRef]
  50. Schwatke, C.; Dettmering, D.; Börgens, E.; Bosch, W. Potential of SARAL/AltiKa for Inland Water Applications. Mar. Geod. 2015, 38, 626–643. [Google Scholar] [CrossRef]
  51. Gardner, A.S.; Moholdt, G.; Cogley, J.G.; Wouters, B.; Arendt, A.A.; Wahr, J.; Berthier, E.; Hock, R.; Pfeffer, W.T.; Kaser, G.; et al. A Reconciled Estimate of Glacier Contributions to Sea Level Rise: 2003 to 2009. Science 2013, 340, 852–857. [Google Scholar] [CrossRef] [PubMed]
  52. Baumhoer, C.A.; Dietz, A.J.; Kneisel, C.; Kuenzer, C. Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning. Remote Sens. 2019, 11, 2529. [Google Scholar] [CrossRef]
  53. Falagas, M.E.; Pitsouni, E.I.; Malietzis, G.A.; Pappas, G. Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and Weaknesses. FASEB J. 2008, 22, 338–342. [Google Scholar] [CrossRef] [PubMed]
  54. Martín-Martín, A.; Orduna-Malea, E.; Thelwall, M.; López-Cózar, E.D. Google Scholar, Web of Science, and Scopus: A Systematic Comparison of Citations in 252 Subject Categories. J. Infect. 2018, 12, 1160–1177. [Google Scholar] [CrossRef]
  55. Adriaanse, L.S.; Rensleigh, C. Web of Science, Scopus and Google Scholar: A Content Comprehensiveness Comparison. Electr. Power Libr. 2013, 31, 727–744. [Google Scholar] [CrossRef]
  56. Gavel, Y.; Iselid, L. Web of Science and Scopus: A Journal Title Overlap Study. Online Inf. Rev. 2008, 32, 8–21. [Google Scholar] [CrossRef]
  57. Abraham, J.P.; Baringer, M.; Bindoff, N.L.; Boyer, T.; Cheng, L.J.; Church, J.A.; Conroy, J.L.; Domingues, C.M.; Fasullo, J.T.; Gilson, J.; et al. A Review of Global Ocean Temperature Observations: Implications for Ocean Heat Content Estimates and Climate Change. Rev. Geophys. 2013, 51, 450–483. [Google Scholar] [CrossRef]
  58. Garcia-Soto, C.; Cheng, L.; Caesar, L.; Schmidtko, S.; Jewett, E.B.; Cheripka, A.; Rigor, I.; Caballero, A.; Chiba, S.; Báez, J.C. An Overview of Ocean Climate Change Indicators: Sea Surface Temperature, Ocean Heat Content, Ocean pH, Dissolved Oxygen Concentration, Arctic Sea Ice Extent, Thickness and Volume, Sea Level and Strength of the AMOC (Atlantic Meridional Overturning Circulation). Front. Mar. Sci. 2021, 8, 642372. [Google Scholar]
  59. Legeais, J.-F.; Ablain, M.; Zawadzki, L.; Zuo, H.; Johannessen, J.A.; Scharffenberg, M.G.; Fenoglio-Marc, L.; Fernandes, M.J.; Andersen, O.B.; Rudenko, S. An Improved and Homogeneous Altimeter Sea Level Record from the ESA Climate Change Initiative. Earth Syst. Sci. Data 2018, 10, 281–301. [Google Scholar] [CrossRef]
  60. Woodworth, P.L.; Melet, A.; Marcos, M.; Ray, R.D.; Wöppelmann, G.; Sasaki, Y.N.; Cirano, M.; Hibbert, A.; Huthnance, J.M.; Monserrat, S.; et al. Forcing Factors Affecting Sea Level Changes at the Coast. Surv. Geophys. 2019, 40, 1351–1397. [Google Scholar] [CrossRef]
  61. Cazenave, A.; Meyssignac, B.; Ablain, M.; Balmaseda, M.; Bamber, J.; Barletta, V.; Beckley, B.; Benveniste, J.; Berthier, E.; Blazquez, A. Global Sea-Level Budget 1993-Present. Earth Syst. Sci. Data 2018, 10, 1551–1590. [Google Scholar]
  62. Wöppelmann, G.; Marcos, M. Vertical Land Motion as a Key to Understanding Sea Level Change and Variability. Rev. Geophys. 2016, 54, 64–92. [Google Scholar] [CrossRef]
  63. Taburet, G.; Sanchez-Roman, A.; Ballarotta, M.; Pujol, M.-I.; Legeais, J.-F.; Fournier, F.; Faugere, Y.; Dibarboure, G. DUACS DT2018: 25 Years of Reprocessed Sea Level Altimetry Products. Ocean Sci. 2019, 15, 1207–1224. [Google Scholar] [CrossRef]
  64. Laxon, S.W.; Giles, K.A.; Ridout, A.L.; Wingham, D.J.; Willatt, R.; Cullen, R.; Kwok, R.; Schweiger, A.; Zhang, J.; Haas, C.; et al. CryoSat-2 Estimates of Arctic Sea Ice Thickness and Volume. Geophys. Res. Lett. 2013, 40, 732–737. [Google Scholar] [CrossRef]
  65. Zhu, X.; Wang, C.; Xi, X.; Nie, S.; Yang, X.; Li, D. Research progress of ICESat-2/ATLAS data processing and applications. Infrared Laser Eng. 2020, 49, 76–85. [Google Scholar]
  66. Xu, N.; Wang, L.; Zhang, H.S.; Tang, S.; Mo, F.; Ma, X. Machine learning based estimation of coastal bathymetry from ICESat-2 and Sentinel-2 data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023, 17, 1748–1755. [Google Scholar] [CrossRef]
  67. Chen, G.; Yang, J.; Zhang, B.; Ma, C. Thoughts and Prospects on the New Generation of Marine Science Satellites. Peri. Ocean Univ.China 2019, 49, 110–117. [Google Scholar]
  68. Ma, J.; Xiao, Y. Autonomous orbit determination and precise measurement of relative position of formation constellation. J. Beijing Univ. Aeronaut. Astronaut. 2008, 34, 665–668. [Google Scholar]
Remotesensing 16 02886 g001
Figure 1. Flowchart of the technological roadmap.
Figure 1. Flowchart of the technological roadmap.
Remotesensing 16 02886 g001
Remotesensing 16 02886 g002
Figure 2. Graph of publication volume and citation frequency of the literature.
Figure 2. Graph of publication volume and citation frequency of the literature.
Remotesensing 16 02886 g002
Remotesensing 16 02886 g003
Figure 3. Information on the 20 authors with the most publications: (a) Research Output, (b) Centrality Index, (c) H-index.
Figure 3. Information on the 20 authors with the most publications: (a) Research Output, (b) Centrality Index, (c) H-index.
Remotesensing 16 02886 g003
Remotesensing 16 02886 g004
Figure 4. Author partnership analysis: (a) CiteSpace partnership analysis, (b) VOSviewer–based partnership network.
Figure 4. Author partnership analysis: (a) CiteSpace partnership analysis, (b) VOSviewer–based partnership network.
Remotesensing 16 02886 g004
Remotesensing 16 02886 g005
Figure 5. Journal and discipline analysis: (a) journal titles; (b) disciplines.
Figure 5. Journal and discipline analysis: (a) journal titles; (b) disciplines.
Remotesensing 16 02886 g005
Remotesensing 16 02886 g006
Figure 6. Literature co-citation network diagram.
Figure 6. Literature co-citation network diagram.
Remotesensing 16 02886 g006
Remotesensing 16 02886 g007
Figure 7. Statistics of hotspot areas: (a) is the percentage of each water body in the total number of documents; (b) represents the top 5 hotspot research areas in the water body.
Figure 7. Statistics of hotspot areas: (a) is the percentage of each water body in the total number of documents; (b) represents the top 5 hotspot research areas in the water body.
Remotesensing 16 02886 g007
Remotesensing 16 02886 g008
Figure 8. Keyword co-occurrence network diagram.
Figure 8. Keyword co-occurrence network diagram.
Remotesensing 16 02886 g008
Remotesensing 16 02886 g009
Figure 9. Keyword emergence analysis chart, visualizing 52 keywords. The solid circle in the figure marks the peak research year for the keyword during this timeframe, with its size indicative of the volume of literature published on the keyword in that year.
Figure 9. Keyword emergence analysis chart, visualizing 52 keywords. The solid circle in the figure marks the peak research year for the keyword during this timeframe, with its size indicative of the volume of literature published on the keyword in that year.
Remotesensing 16 02886 g009
Remotesensing 16 02886 g010
Figure 10. Comparison of citation counts between Web of Science, Google Scholar, and Scopus.
Figure 10. Comparison of citation counts between Web of Science, Google Scholar, and Scopus.
Remotesensing 16 02886 g010
Table 1. Basic Information of the WOS CC.
Table 1. Basic Information of the WOS CC.
TypeNumber
Documents13,500
Authors23,677
Countries/Regions97
Institutions4725
Sources1593
Average Times Citing per Item38.76
Average Times Cited per Item31.65
Table 2. Answer accuracy obtained for different instructions.
Table 2. Answer accuracy obtained for different instructions.
Questions Pertaining to the Literature ReviewAccuracyQuestions Pertaining to the Academic LiteratureAccuracy
What is the structure of this literature? 92%What are the research methods used in this literature?89%
What is the current state of research in this literature?92%What datasets were analyzed in this research?89%
What innovative viewpoints does the author present?88%What impact does this literature have on its field?86%
What is the summary of the key points in this literature?92%What is the summary of the key points in this literature?89%
Table 3. Altimetry satellite information.
Table 3. Altimetry satellite information.
SatelliteCountry/InstitutionLaunch YearScientific MissionAltimeter
GEOSATUSA/USN1985Military, Geodesy and OceanographyGRAS
ERS-1Europe/ESA1991Land and Ocean Surface ChangeRA-1
TOPEX/PoseidonUSA/NASA
France/CNES		1992Ocean Surface Topography MissionPoseidon-1
ERS-2Europe/ESA1995Land and Ocean Surface ChangeRA-1
Jason-1USA/NASA
France/CNES		2001Ocean Surface Topography MissionPoseidon-2
ENVISATEurope/ESA2002Earth ObservationRA-2
ICESatUSA/NASA2003Ice Sheet Mass Balance, Cloud and Aerosol HeightsGLAS
Jason-2USA/NASA
France/CNES		2008Ocean Surface Topography MissionPoseidon-3
CryoSat-2Europe/ESA2010Polar Sea Ice Thickness and Ice SheetsSIRAL
HY-2China/CNSA2011Ocean Dynamic and Environmental ParametersRA
SARALIndia/ISRO
France/CNES		2013Earth ObservationAltiKa
Sentinel-3Europe/ESA2016Ocean
and Land Observation		SRAL
Jason-3USA/NASA
France/CNES		2016Oceanography MissionPoseidon-3b
ICESat-2USA/NASA2018Ice Sheet Elevation and Sea Ice ThicknessATLAS
SWOTUSA/NASA
France/CNES		2022Surface Water and Ocean Topography MissionKaRin, Poseidon-3C
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Huang, Z.; Sun, R.; Wang, H.; Wu, X. Trends and Innovations in Surface Water Monitoring via Satellite Altimetry: A 34-Year Bibliometric Review. Remote Sens. 2024, 16, 2886. https://doi.org/10.3390/rs16162886

AMA Style

Huang Z, Sun R, Wang H, Wu X. Trends and Innovations in Surface Water Monitoring via Satellite Altimetry: A 34-Year Bibliometric Review. Remote Sensing. 2024; 16(16):2886. https://doi.org/10.3390/rs16162886

Chicago/Turabian Style

Huang, Zhengkai, Rumiao Sun, Haihong Wang, and Xin Wu. 2024. "Trends and Innovations in Surface Water Monitoring via Satellite Altimetry: A 34-Year Bibliometric Review" Remote Sensing 16, no. 16: 2886. https://doi.org/10.3390/rs16162886

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop