## 3 Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Journal** & **Rs+Pheno Docs** & **Impact Factor** & **H Index** \\ \hline Remote Sensing of Environment & 232 & 8.218 & 238 \\ International Geoscience and Remote Sensing Symposium-IGARSS & 166 & na & 58 \\ Remote Sensing & 150 & 4.118 & 81 \\ International Journal of Remote Sensing & 148 & 2.493 & 151 \\ Proceedings of SPIE: - The International Society for Optical Engineering & 117 & na & 151 \\ Agricultural and Forest Meteorology & 57 & 4.189 & 144 \\ ISPRS - Journal of Photogrammetry and Remote Sensing & 52 & 6.942 & 110 \\ IEEE - Journal of Selected Topics in Applied Earth Observations and Remote Sensing & 35 & 3.972 & 64 \\ IEEE - Transactions on Geoscience and Remote Sensing & 32 & 5.63 & 216 \\ International Journal of Applied Earth Observation and Geoinformation & 32 & 4.846 & 86 \\ Global Change Biology & 30 & 8.88 & 217 \\ Ecological Indicators & 23 & 4.490 & 97 \\ Photogrammetric Engineering and Remote Sensing & 21 & 3.15 & 114 \\ Journal of Applied Remote Sensing & 20 & 1.344 & 39 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of the most used scientific journals in rs+pheno (remotely sensed phenology) studies (>20 documents in 1979–2018), with corresponding impact factor (source: Thomson Reuters) and H index (source: SCImago Journal Rank - SJR). Figure 2: Temporal trend of publications in rs+pheno studies over the period 1979–2018 for the five most publishing remote sensing-based journals (upper panel) and the three most publishing ecology-based journals (lower panel). 2009-2018 (Figure 4), the keywords most used in the recent years were 'cost efficiency' and 'random forest', while those mainly used at the beginning of the decade were 'geology', 'water supply', 'irrigation', and 'carbon flux'. The text data analysis produced the term network maps of Figures 5 and 6, respectively, for the period 1999-2008 (99 terms) and 2009-2018 (383 terms). In the first decade (Figure 5), two groups could be identified, even if quite interspersed. The red cluster was mainly characterized by terms strictly associated to phenology metrics (e.g., greenness, start, end, and length of season), vegetation and canopy indices and their annual profiles (e.g., NDVI time-series, EVI, LAI, fAPAR, amplitude, peak), climate change-related issues (e.g., temperature, trend, interannual variability, drought, fire), and forestry (e.g., canopy, leaf, forest type, tropical forest, boreal forest). The blue cluster was mainly composed of terms related to the classification (i.e., accuracy, mapping, change detection, discrimination, land cover, unsupervised classification), crop modeling (e.g., crops, growth, yield, prediction, cost efficiency, management), and the crop growing season (i.e., months from March to November). It is to be noticed that the only highly cited sensor was Landsat that belongs to the blue cluster. In the second decade (Figure 6), three groups could be clearly distinguished. The red cluster was mainly characterized by terms associated to phenology climate-based drivers (e.g., precipitation, temperature, LUE (light use efficiency), latitude, altitude, solar radiation), phenology metrics and their trend (e.g., start, end, and length of season, response, increase, delay, MODIS time-series), climate change-related effects (e.g., disturbance, dry season, moisture, fire, resilience, species composition), forest ecophysiology (e.g., carbon, carbon cycle, carbon flux, ecosystem respiration, fAPAR, GPP, net ecosystem exchange (NEE), exchange), ocean and arctic dynamics (e.g., phytoplankton, phytoplankton bloom, ice, ice cover, GIMMS (Global Inventory Modeling and Mapping Studies), and regions like Cerrado, Tropic, Savanna, North America, and Tibetan Plateau.