Satellite imagery is gaining popularity as a valuable tool to lower the impact on natural resources and increase profits for farmers. The purpose of this study is twofold: to mine the scientific literature to reveal the structure of this research domain, and to investigate to what extent scientific results can reach a wider public audience. To meet these two objectives, a Web of Science and a Twitter dataset were retrieved and analysed, respectively. For the academic literature, different performances of various countries were observed: the USA and China resulted as the leading actors, both in terms of published papers and employed researchers. Among the categorised keywords, "resolution", "Landsat", "yield", "wheat" and "multispectral" are the most used. Then, analysing the semantic network of the words used in the various abstracts, the different facets of the research in satellite remote sensing were detected. The importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes emerged from these analyses. As emerging topics, classification tasks for land use assessment and crop recognition stand out, alongside the use of hyperspectral sensors. Regarding the interaction of academia with the public, the analysis showed that it is practically absent on Twitter: most of the activity therein stems from private companies advertising their business. This shows that there is still a communication gap between academia and actors from other societal sectors.

翻译：卫星图像作为降低自然资源影响并增加农民利润的重要工具正日益受到关注。本研究旨在实现双重目标：通过挖掘科学文献揭示该研究领域的结构，并探究科学成果在多大程度上能够触及更广泛的公众受众。为实现这两个目标，分别检索并分析了Web of Science数据集和Twitter数据集。针对学术文献，观察到不同国家的表现差异：美国和中国在论文发表数量及研究人员规模上均处于领先地位。在分类关键词中，"分辨率"、"Landsat"、"产量"、"小麦"及"多光谱"使用频率最高。通过对各摘要所用词语的语义网络分析，识别出卫星遥感研究的多个维度。分析揭示出通过遥感获取气象参数的重要性以及植被指数的广泛应用。作为新兴研究主题，土地利用评估与作物识别的分类任务，以及高光谱传感器的应用尤为突出。在学术界与公众互动方面，分析显示Twitter平台上几乎不存在此类互动：该平台上的大部分活动来自私营企业的商业推广。这表明学术界与其他社会部门参与者之间仍存在沟通鸿沟。