## 4 Discussion
### Major Research Topics  
The term network maps for the period 1999-2008 and 2009-2018 allowed to identify the main clusters, i.e., the main scientific domains, or macro-topics, in the rs+pheno field. In the first decade, according to the terms distribution, two groups can be distinguished: one cluster (red) is an expression of a pheno-climatic macro-topic and its application in forestry, the other (blue) represents the researchstudies related to classification and its application in agriculture. In the second decade, three groups can be distinguished: one cluster (red), like in the previous decade, is expression of a pheno-climatic macro-topic and its implications in forest dynamics, the second cluster (blue) is expression of the agriculture-related domain, and the third cluster (orange) represents the research studies related to the classification field. Such results highlighted that, while in the first decade, agriculture was included in the classification cluster and did not represent an independent macro-topic, in 2009-2018, it arose as a single, standalone research theme, with specific targets and objectives; on the contrary, both in the first and in the second decade, forestry remained linked with the pheno-climatic macro-topic. This suggests that while rs+pheno is a key tool for agricultural studies, forest studies represent a key tool to investigate rs+pheno and climate change impacts.  
The term occurrence analysis proved that mapping and its accuracy were the most common topics for both 1999-2008 and 2009-2018. Also, thanks to the increasing availability of new high-resolution satellites and cloud computing platforms, a growing number of papers have been published on thematic mapping, as well as on classification/validation methods. In the framework of both forestry and agriculture rs+pheno studies, classification methods like spectral angle mapper (SAM), object-oriented techniques, random forest, and support vector machine (SVM), among others, have been to be used since 2000. However, the high relevance gained in the last decade by such techniques highlights the recent use of specific classification methods for addressing specific rs+pheno targets, like land cover mapping [63; 64; 65], phenological characterization [66; 67], and anomalies detection [68; 69]. The use of _ad hoc_ methodological approaches according to different objectives, scales, sensors, temporal, and spatial resolutions represents an important achievement. The continuous exploitation of new technological resources is one of the main characteristics of rs+pheno science, which guarantees this discipline to be up-to-date and always evolving, according to the availability of innovative instruments, the development of novel methodologies, and the emergence of new questions [70].  
The attention towards the phenology metrics detection and extraction also represented the main topic for both 1999-2008 and 2009-2018, with the majority of the studies focusing on harmonic analysis [57; 63; 52]. The start of the season (SOS) represents the metric most investigated; its study has increased through time to such an extent that it became a major, common topic and, hence, lost its relevance. In the last decade, a large number of papers were published about spring phenology and its response to climate change [71; 72; 73; 51]; on the contrary, end of season (EOS) and length of season (LOS) received far less consideration. As clearly stated by [50], autumn remains a relatively disregarded season in climate change research both in temperate and arctic ecosystems, notwithstanding the role of autumn in determining the length of the growing season [74; 75] and in controlling influence on the carbon-uptake period [76]. However, EOS is generally considered to be more variable than SOS phenology and less often observed, making its detection and analysis challenging [75; 77].