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Article summary:
1. This review analyzed the last 40 years (1979–2018) of publications in the remotely sensed phenology (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.
2. 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.
3. 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.
Article analysis:
The article “Text Mining in Remotely Sensed Phenology Studies: A Review on Research Development, Main Topics, and Emerging Issues” provides an overview of remotely sensed phenology (rs+pheno) research over the last 40 years (1979–2018). The authors used a text mining approach to analyze bibliographic and text data from publications retrieved from the Scopus database. The results showed 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.
The article is well-structured and provides an extensive overview of rs+pheno research development over time. The authors have done an extensive literature search to identify relevant studies for their analysis and have provided detailed information about their methodology. However, there are some potential biases in this study that should be noted. Firstly, as only English language documents were included in this study, there may be some bias towards English-speaking countries or regions where English is widely spoken or used as an official language. Secondly, as only peer-reviewed documents published between 1979 and 2018 were included in this study, there may be some bias towards more recent studies which may not reflect long-term trends or developments in rs+pheno research accurately. Finally, as only documents from Elsevier Scopus electronic scientific databases were included in this study, there may be some bias towards certain journals or publishers which are indexed by Scopus but not by other databases such as Web of Science or PubMed Central.
In conclusion, this article provides an extensive overview of remotely sensed phenology research over the last 40 years using a text mining approach to analyze bibliographic and text data from publications retrieved from the Scopus database. However, potential biases should be noted when interpreting these