## 1 Introduction
Most studies on vegetation phenology are based on the normalized difference vegetation index (NDVI, [16]) or the enhanced vegetation index (EVI, [15]), measures of 'greenness' [17] that can be related to plant canopy absorption of photosynthetically active radiation [18] and Gross Primary Productivity (GPP) [19]. The evolution of remotely sensed VIs through time exhibits a strong correlation with the typical vegetation growth stages. The VIs temporal curves allow to quantify intra-annual changes in the timing and intensity of vegetation activity and to extract useful metrics about the vegetation growing season, such as the timing of the start, the end, and the length of the growing season, at various scales [20]. In the past few decades, within the framework of increasing interest in phenology, rs+pheno studies showed a marked intensification that could be explained by the nonstop development ofsatellite remote sensing technology and its ability to provide temporally continuous information about the ecosystem responses to climate change at different spatio-temporal scales. Satellite observations are hence becoming increasingly important for phenological investigations, and given the growing interest towards rs+pheno, an overall vision of what has been explored up to now, together with the identification of research trends, key topics, gaps, and new questions may represent an important knowledge tool when framing a research and exploring the state-of-the-art of a scientific issue. In recent years, bibliometric analysis has played an increasingly important role in the framework of understanding science and technology tendencies [21], thanks to its potentiality to document the development of a wide range of research fields [22; 23; 24; 25]. Some studies started to explore topic frequencies and concept trends in phenological studies, but only for a specific country or a specific subject area. For instance, [PERSON] et al. [26] examined all peer-reviewed literature on Africa's vegetation phenology to provide a synthesis of such studies and classified them based on the methods and techniques used in order to identify major research gaps; [PERSON] et al. [27] reviewed remote sensing phenological studies in Japan and discussed current knowledge, problems, and future developments based on these studies; [PERSON] et al. [28] in the bibliometric overview of the Forests journal aimed at highlighting the state of the art of forestry, cited as a major research cluster the integration of MODIS and Landsat imagery for mapping forest biomass and phenology. Other bibliometric studies analyzed major topics and concepts in remote sensing research fields either in general [29; 30] or based on specific issues, like crop growth monitoring [31], water research [32], archaeology [33], etc. To the best of our knowledge, this is the first attempt to carry out a broad literature analysis to explore and summarize the remotely sensed phenology (rs+pheno) studies through time at a global level. We performed such analysis through a text mining approach. Text mining is the use of automated methods for exploiting the huge amount of knowledge available in the scientific literature [34]; it consists of deriving high-quality information (in terms of relevance, novelty, and interest) from text through the recognitions of patterns and trends by means of statistical pattern learning [35]. In this review, we performed a bibliometric text mining analysis of rs+pheno research over the last 40 years (1979-2018) aimed at (1) quantifying the distribution of publications and journals for articles covering rs+pheno research, (2) examining the rs+pheno keywords frequency and tendencies, and (3) exploring the rs+pheno research development to identify key topics, new research niches, and emerging issues.