Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues

As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of t...

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Published in:Remote Sensing
Main Authors: Bajocco, Sofia, Raparelli, Elisabetta, Teofili, Tommaso, Bascietto, Marco, Ricotta, Carlo
Format: Article in Journal/Newspaper
Language:English
Published: MDPI 2019
Subjects:
Online Access:http://hdl.handle.net/11573/1362974
https://doi.org/10.3390/rs11232751
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spelling ftunivromairis:oai:iris.uniroma1.it:11573/1362974 2024-02-11T10:01:43+01:00 Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues Bajocco, Sofia Raparelli, Elisabetta Teofili, Tommaso Bascietto, Marco Ricotta, Carlo Bajocco, Sofia Raparelli, Elisabetta Teofili, Tommaso Bascietto, Marco Ricotta, Carlo 2019 http://hdl.handle.net/11573/1362974 https://doi.org/10.3390/rs11232751 eng eng MDPI place:Basel info:eu-repo/semantics/altIdentifier/wos/WOS:000508382100032 volume:11 numberofpages:23 journal:REMOTE SENSING http://hdl.handle.net/11573/1362974 doi:10.3390/rs11232751 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85076558347 info:eu-repo/semantics/openAccess bibliometric analysi land surface phenology network analysi research topic scientific mapping info:eu-repo/semantics/article 2019 ftunivromairis https://doi.org/10.3390/rs11232751 2024-01-17T18:05:14Z As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly di°cult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years (1979-2018) of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology ... Article in Journal/Newspaper Arctic Climate change Phytoplankton Sapienza Università di Roma: CINECA IRIS Arctic Remote Sensing 11 23 2751
institution Open Polar
collection Sapienza Università di Roma: CINECA IRIS
op_collection_id ftunivromairis
language English
topic bibliometric analysi
land surface phenology
network analysi
research topic
scientific mapping
spellingShingle bibliometric analysi
land surface phenology
network analysi
research topic
scientific mapping
Bajocco, Sofia
Raparelli, Elisabetta
Teofili, Tommaso
Bascietto, Marco
Ricotta, Carlo
Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues
topic_facet bibliometric analysi
land surface phenology
network analysi
research topic
scientific mapping
description As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly di°cult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years (1979-2018) of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology ...
author2 Bajocco, Sofia
Raparelli, Elisabetta
Teofili, Tommaso
Bascietto, Marco
Ricotta, Carlo
format Article in Journal/Newspaper
author Bajocco, Sofia
Raparelli, Elisabetta
Teofili, Tommaso
Bascietto, Marco
Ricotta, Carlo
author_facet Bajocco, Sofia
Raparelli, Elisabetta
Teofili, Tommaso
Bascietto, Marco
Ricotta, Carlo
author_sort Bajocco, Sofia
title Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues
title_short Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues
title_full Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues
title_fullStr Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues
title_full_unstemmed Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues
title_sort text mining in remotely sensed phenology studies. a review on research development, main topics, and emerging issues
publisher MDPI
publishDate 2019
url http://hdl.handle.net/11573/1362974
https://doi.org/10.3390/rs11232751
geographic Arctic
geographic_facet Arctic
genre Arctic
Climate change
Phytoplankton
genre_facet Arctic
Climate change
Phytoplankton
op_relation info:eu-repo/semantics/altIdentifier/wos/WOS:000508382100032
volume:11
numberofpages:23
journal:REMOTE SENSING
http://hdl.handle.net/11573/1362974
doi:10.3390/rs11232751
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85076558347
op_rights info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.3390/rs11232751
container_title Remote Sensing
container_volume 11
container_issue 23
container_start_page 2751
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