Earth Observation (EO) is the collection of information about the Earth’s surface using remote sensing and in situ surveying on the ground [1
]. Hence, it encompasses imagery from a range of satellite sensors, aerial imagery from airplanes and increasingly unmanned aerial vehicles, as well as permanent ground-based sensors and field-based measurements collected using handheld sensors, digital questionnaires or using paper-based formats. Once the domain of only professionals, the field of EO has seen new inputs coming from Citizen Science (CS), which is the involvement of citizens in scientific research, from data collection through to hypothesis generation [2
]. This is clearly reflected in the increasing number of publications that have appeared in the area of EO and CS as shown in Figure 1
, which is based on a search of the terms ‘Earth Observation’ and ‘Citizen Science’ in both Scopus and Google Scholar.
Citizens generally provide inputs to EO in two main ways, i.e., through image interpretation and through collection of in situ data, both of which are useful for the calibration and validation of remotely-sensed imagery or products derived from EO [3
]. Table 1
lists a number of different CS projects that are currently providing data useful for EO, classified by the type of data collected. Although the list is not exhaustive since this field is changing rapidly, it does serve to illustrate the vast breadth of projects that have emerged over more than four decades. Despite the long history of such initiatives, the majority of projects listed in Table 1
are much more recent and were started in the current decade.
We have also indicated whether the data collection is carried out outdoors, i.e., field-based, and which ones are carried out online, i.e., usually indoors. Some projects combine both data collection options. The summary of projects in Table 1
shows that around 58% are field-based only, 25% are online only, while 17% have both a field and online component. This shows that the majority of projects (around 83%) are collecting in situ data, which can help to fill a much needed data gap [4
CS is used here in the widest sense since most of the CS projects listed in Table 1
involve citizens mainly in data collection rather than in scientific analysis of the data or project design. However, there are documented examples of where citizens are involved in the full sequence or workflow of a CS project, from project design, data collection, methodology development to data analysis and interpretation of results [6
The increased role that citizens are playing in the field of EO has been driven by a number of factors. The first is the availability of very high resolution satellite imagery through initiatives like Google Maps and Bing Maps, which have brought very high resolution satellite imagery much closer to citizens and made it part of their daily lives. The second is technological, as advances in mobile technology and Web 2.0 have resulted in an environment where citizens can literally map the world, e.g., through OpenStreetMap (OSM) or Google MapMaker, or collect georeferenced data as they move throughout their physical space. At the same time, new satellite sensors have been launched, resulting in new big data streams from the Copernicus Sentinel satellites and through Planet, which will become enablers for many new EO applications in CS in the future.
However, CS remains a challenge in itself due to numerous issues such as quality [7
], data interoperability [11
], the engagement and motivation of citizens [13
], strategies for retention and sustainability of participation [15
], and increasingly, legal issues related to privacy, ethics and licensing [17
], among others. This special issue includes papers that address some of these issues in relation to the use of CS for EO, while other aspects associated with crowdsourcing, in particular the value of crowdsourced data, are also considered within the different application-oriented papers. In the next section, a summary of the findings presented in the special issue papers is given, which represent some of the latest advances in CS and EO. In the final section, some ideas about the possible future directions for CS and EO are presented.
3. The Future Outlook for Citizen Science and Earth Observation
In the last decade, we have seen a massive increase in research on CS and EO (see Figure 1
) and this trend is expected to continue in the future. Moreover, as we showed in Table 1
, there are already many different ongoing CS projects that have relevance for EO, but most are not embedding their data streams into operational EO applications, while others suffer from insufficient citizen participation. One important element needed to make citizen observations an official data stream is legal recognition by governments and local authorities that citizen-based data are a valid source of information and to recognize that citizens are able to deliver data of sufficient quality that can potentially complement, but not necessarily replace, existing observation networks. For example, in the USA, the Crowdsourcing and CS Act, which came into force in January 2017, gives federal agencies clear authorization to use CS and crowdsourced data. This recognition paves the way for the development of truly integrated environmental monitoring systems involving citizens as a key contributor. No such legislation exists in the EU or other countries around the world.
The ubiquitous dispersion of smartphone technology has acted as an enabler and allows citizens, with little effort, using well-designed and user friendly apps, to collect observations of the environment, which can then be stored in a data repository for curation and further analysis. A second enabler of CS and EO is the sheer amount of new and freely available remotely-sensed data. When fully operational, the Sentinel satellites of the European Copernicus program will collect several TB of data per day, where Sentinel-2 images, for example, will be acquired every five days at a 10 m resolution. There are also new commercial providers such as Planet, which will eventually provide daily observations of the globe at a 3 m resolution, improving the chances of obtaining cloud free imagery. This will enable near real-time monitoring of changes occurring on the Earth; such data are becoming increasingly attractive since they can be used to build more applications for citizens that will potentially stimulate their engagement and further mainstream the use of satellite data by citizens in their daily lives.
The two biggest challenges for CS at present are related to the quality of the data and how to engage and retain citizen participation in the longer term. Quality can be improved via training; through the continuous monitoring of data quality with embedded feedback provided to citizens, the volunteers can improve over time. In terms of engagement, Budhathoki and Haythornthwaite [29
] showed that there are several reasons why citizens participate, including a range of motivations such as altruism, self-achievement and personal interest, among others. Successful CS or crowdsourcing projects have been those where citizens were able to derive a direct benefit from the data collected by others. One typical example is mountain bike tracks that are recorded at some locations in great detail. For example, a new route entered by one mountain biker can be used directly by another one living close by. Another increasingly popular way of engaging citizens is via gamification and making data collection a fun experience. For the first time, Pokémon Go has managed to engage a massive crowd of players to go outside their homes, sometimes to remote places, to collectively catch 88 billion monsters (as of 1 March 2017). Had Pokémon Go also collected information about real objects found in the environment, e.g., taking a picture of a tree while simultaneously catching a virtual monster, such data could have been very valuable and an initial global database on tree species, health and location could have been derived from such a game. Although the majority of observations would not have been in remote or inaccessible places, this could have been used to document tree health in cities and towns and benefited the CS applications of urban tree monitoring (see Table 1
). The data would also have been provided at an incredibly high temporal resolution, i.e., possibly seconds between observations, which would allow for rapid infestations to be clearly identified.
This special issue dedicated to CS and EO includes some review-oriented publications but mainly consists of research papers demonstrating applications in many diverse environmental domains. As with much of CS today, the citizen contributions in the application papers are mainly in the form of data collection and not in project co-design or data analysis. Moreover, many of the most relevant aspects related to CS have been addressed in some way in this compilation of papers, such as the role of citizens in collecting data, the quality of crowdsourced data, data conflation, and the combination of CS with other technologies and methods applied by experts, to name just a few. Even though the use of CS for EO is still at an early stage, the huge potential arising from the combination of both data streams is already very clear.