# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 5 Conclusions  
We carried out a review of the impact of EO satellite data and analyzed possible future trends. The peer-reviewed articles referencing EO satellites that were included in this review were selected from the WoS core collection and analyzed using both scientometric analysis (i.e., CiteSpace) and meta-analysis. A total of 45,673 papers related to 15 well-known EO satellite missions were analyzed. These analyses can provide an in-depth understanding of the contributions to Earth observation made by EO satellite missions and the similarities and differences between the applications of Sentinel, Landsat, MODIS, Gaofen, and WorldView data. Our most significant findings are as follows. 1. In recent years, the number of publications and citations referencing EO satellites--particularly Landsat, MODIS and Sentinel--has increased rapidly. However, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease. The Sentinel mission shows the most promise for future applications. 2. The published EO satellite papers were concentrated in a small number of journals, with 43.79% of articles being published in only 13 journals. The United States is the most important host country for the relevant journals and the most important source of articles in the relevant journals, thus making a significant contribution to the development of remote sensing. 3. A new impact index, the RSIF, was constructed to measure the impact of the use of EO satellite data and future possible trends in their application. Based on the values of the RSIF that we calculated, we believe that currently, the EO satellite missions that are of the most significance are Landsat, Sentinel, and MODIS. We also believe that, within the next five years (2021-2025), Sentinel data will become the most widely used EO satellite data. Sentinel, Landsat, and MODIS will still be the most influential satellite missions; there will also be more opportunities for the Gaofen and WorldViewmissions to promote significant advances in remote sensing applications and be used widely if their data are open access. 4. EO satellite data have been widely used in studies of the land, the atmosphere, water, phenology, and biomass. Vegetation, climate, and land cover are the main research focuses in which EO data are used. Landsat is currently the most important EO satellite. The EO satellite mission for the widest range of EO applications, however, is MODIS. 5. RF is a popular machine-learning classifier and is widely used for processing images. The use of multi-source satellite data helps to produce high-quality information. 6. In terms of the number of relevant references, the United States and China dominate research in which EO satellite data are applied: together, they account for 25,168 or 39.17% of the papers that we analyzed. Related research in China has developed rapidly in recent years. EO satellite data are a very effective and indispensable tool to provide measurements and support for the environmental, social, political, and economic studies. In general, applications of EO satellite data have been developed with the increasing demand in these fields. For example, the refinement requirements of land use policies have led to the development of land cover mapping products toward higher resolution and stronger real-time performance. In this point, high-resolution EO satellite missions are becoming popular these days to monitor nature, the environment, and disasters [16]. Among the top five satellites as measured by the RSIF, Sentinel, Gaofen, and WorldView all carry high-resolution instruments, which is one of the reasons that these data have quickly become widely used. The fusion of remote sensing data from multiple sources will also become an important focus of research into the use of geospatial data [101]; however, significant advances in fusion algorithms are still needed. Big Earth Data consists of complex and unstructured data, and more advanced tools are needed to explore multi-temporal, multi-scale geo-information [102].