Research Trends in the Remote Sensing of Phytoplankton Blooms: Results from Bibliometrics

Abstract

Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. “Oceanography,” “Environmental Sciences,” and “Remote Sensing” are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that “phytoplankton,” “chlorophyll,” and “ocean” were the most frequently occurring keywords, while “eutrophication management and monitoring,” “climate change,” “lakes,” and “remote-sensing algorithms” were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field.

Urbanization shifts long‐term phenology and severity of phytoplankton blooms in an urban lake through different pathways

“…Although multi‐source remote sensing provides high precision and frequency, supporting the long‐term monitoring of PBs, the uneven data distribution due to thick cloud occlusion in the rainy season and the lack of high‐resolution images in the early stage (Figure S2) may cause uncertainty in the monitoring of PBs, and the limitations of the index model for PB monitoring in the vertical direction may also be regrettable. Fortunately, with the successive launches of high‐performance satellites and the gradual maturation of new remote sensing technologies (e.g., Unmanned Aerial Vehicle, Synthetic Aperture Radar, active LiDAR and holographic systems), the application of multi‐source remote sensing, data fusion, and model technologies will become more flexible and have higher applicability (Gronchi et al, 2021; Li et al, 2021; Ma, Zhu, et al, 2022; Moore et al, 2019). In the future, the applicability of atmospheric correction models, the limitations of index models and the lack of field validation data are still pending issues to be solved to further improve the accuracy of PB monitoring, especially long‐term monitoring (Li et al, 2021; Mouw et al, 2015; Shi, Zhang, Qin, et al, 2019).…”

“…Fortunately, with the successive launches of high‐performance satellites and the gradual maturation of new remote sensing technologies (e.g., Unmanned Aerial Vehicle, Synthetic Aperture Radar, active LiDAR and holographic systems), the application of multi‐source remote sensing, data fusion, and model technologies will become more flexible and have higher applicability (Gronchi et al, 2021; Li et al, 2021; Ma, Zhu, et al, 2022; Moore et al, 2019). In the future, the applicability of atmospheric correction models, the limitations of index models and the lack of field validation data are still pending issues to be solved to further improve the accuracy of PB monitoring, especially long‐term monitoring (Li et al, 2021; Mouw et al, 2015; Shi, Zhang, Qin, et al, 2019).…”

Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review

“…LDA determines the topic distribution based on the frequency with which vocabulary appears in documents ( 27 ). This method has been used in many research areas to identify research topics and trends in publications (e.g., 28 , 29 ). The number of LDA topics was determined based on consistency.…”

Comparative Evaluation of Semi-Empirical Approaches to Retrieve Satellite-Derived Chlorophyll-a Concentrations from Nearshore and Offshore Waters of a Large Lake (Lake Ontario)

“…The onset, duration, and intensity of algal growth are further influenced by environmental factors such as temperature, sunlight, and wind mixing [4,40]. The harmful effects of eutrophication in the region include water supply issues, human and pet health risks, diminished aquatic life, and economic impacts on tourism, recreational activities, and lakefront property value [2].…”

“…It is driven primarily by nutrient enrichment, most often accelerated by anthropogenic activities such as agricultural fertilizer application, wastewater discharge, and urbanization [1]. Cultural eutrophication promotes excessive growth of algae, leading to the deterioration of water quality and overall ecosystem health, and the loss of aquatic habitats [2]. In extreme cases, harmful algal blooms (HABs) occur through the rapid proliferation of algae, in particular cyanobacteria [3].…”