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 for revealing the structure of this research domain and to investigate to what extent scientific results are able to reach a wider public. To fulfill these, respectively, a Web of Science and a Twitter dataset were retrieved and analysed. Regarding academic literature, different performances of the 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. It emerged the importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes. As emerging topics, classification tasks for land use assessment and crop recognition stand out, together with 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 is due to private companies advertising their business. Therefore, there is still a communication gap between academia and actors from other societal sectors.

翻译：卫星图像作为一种降低自然资源影响并增加农民收益的宝贵工具正日益受到关注。本研究旨在达成双重目标：挖掘科学文献以揭示该研究领域的结构，并探讨科研成果能在多大程度上触及更广泛的公众。为此，我们分别检索并分析了Web of Science和Twitter数据集。在学术文献方面，不同国家的表现存在差异：美国和中国在发表论文数量和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"和"多光谱"使用频率最高。随后，通过分析各摘要所用词语的语义网络，我们发现了卫星遥感研究的多个维度。研究揭示了通过遥感反演气象参数的重要性以及植被指数的广泛应用。在新兴主题方面，土地利用评估和作物识别的分类任务，以及高光谱传感器的应用尤为突出。关于学术界与公众的互动，分析表明其在Twitter上几乎不存在：该平台上的活动主要由私营公司推广其业务所驱动。因此，学术界与其他社会领域参与者之间仍存在沟通鸿沟。