2_Kovacs_et_al.indd 119Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.DOI: 10.15201/hungeobull.68.2.2 Hungarian Geographical Bulletin 68 2019 (2) 119–139. Introduction Environment-related pressure on our society is intense. The attentive use and management of environmental resources are crucial for future generations’ prosperity. Better observation, un- derstanding, protection, and enhancement of our environment is only feasible with the active involvement of citizens. Although our political, economic and administrative structures may be designed to tackle our environmental con- cerns through scale and strategic decisions, cit- izens often feel as though they are un-engaged, silent observers (McGlade, J. 2009; Liu, H.Y. et al. 2014). Formal institutions like EU Water Framework Directive, Flood Risk Directive and Citizen observatory based soil moisture monitoring – the GROW example Károly Zoltán KOVÁCS1, Drew HEMMENT2, Mel WOODS3, Naomi K. van der V E L D E N 4, Angelika XAVER5, Rianne H. G I E S E N 6, Victoria J. B U R T O N 4, Natalie L. G A R R E T T 7, Luca Z A P PA 5, Deborah Long3, Endre D O B O S 1 and Rastislav S K A L S K Y 8 Abstract GROW Observatory is a project funded under the European Union’s Horizon 2020 research and innovation program. Its aim is to establish a large scale (more than 20,000 participants), resilient and integrated ‘Citizen Observatory’ (CO) and community for environmental monitoring that is self-sustaining beyond the life of the project. This article describes how the initial framework and tools were developed to evolve, bring together and train such a community; raising interest, engaging participants, and educating to support reliable obser- vations, measurements and documentation, and considerations with a special focus on the reliability of the resulting dataset for scientific purposes. The scientific purposes of GROW observatory are to test the data quality and the spatial representativity of a citizen engagement driven spatial distribution as reliably inputs for soil moisture monitoring and to create timely series of gridded soil moisture products based on citizens’ observations using low cost soil moisture (SM) sensors, and to provide an extensive dataset of in situ soil moisture observations which can serve as a reference to validate satellite-based SM products and support the Copernicus in situ component. This article aims to showcase the initial steps of setting up such a monitoring network that has been reached at the mid-way point of the project’s funded period, focusing mainly on the design and development of the CO monitoring network. Keywords: citizen science, citizen observatory, crowdsourced data, soil moisture monitoring 1 Institute of Geography and Geoinformatics. University of Miskolc. H-3515 Miskolc, Egyetemváros. E-mails: ecocares@uni-miskolc.hu, ecodobos@uni-miskolc.hu 2 University of Edinburgh, E-mail: drew.hemment@ed.ac.uk 3 University of Dundee. E-mails: m.j.woods@dundee.ac.uk, d.long@dundee.ac.uk 4 Permaculture Association (Britain). E-mails: naomi@permaculture.org.uk, victoria@permaculture.org.uk 5 Department of Geodesy and Geoinformation, TU Wien, Gußhausstraße 27–29, 1040 Wien, Austria. E-mails: angelika.xaver@geo.tuwien.ac.at, luca.zappa@geo.tuwien.ac.at 6 HydroLogic Research, Delft, The Netherlands, rianne.giesen@hydrologic.com 7 Met Office, United Kingdom, natalie.garrett@metoffice.gov.uk 8 International Institute for Applied Systems Analysis, Laxenburg, Austria skalsky@iiasa.ac.at Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.120 the Aarhus Convention require citizen partici- pation. Despite the long history of ‘Citizen Ob- servatory’ (CO) quality assurance for the scien- tific use of citizen science generated data is still missing (Freitag, A. et al. 2016). Nevertheless, robust examples exist, such as, The National Audubon Society Christmas Bird Count which has been running since 1900 in North America, the data collected by observers over the past century allow researchers, conservation biolo- gists, wildlife agencies and other interested individuals to study the long-term health and status of bird populations across North Ameri- ca (Butcher, G.S. et al. 1990; Hochachka, W.M. et al. 2012). The chosen subject of observation itself has an influence on how successful a citizen obser- vatory will be. Examining the motivations of citizen science participants can give insights into why certain initiatives are more success- ful than others (Clary, E.G. and Snyder, M. 1999; Rotman, D. et al. 2012; Geoghegan, H. et al. 2016). Citizen science projects are domi- nated by biodiversity topics rather than the abiotic environment (Pocock, M.J.O. et al. 2017), possibly because experiencing and improving the environment is a common motivator of citizen observations (West, S.E. 2015). Weather and climate is also a popular topic (Gharesifard, M. et al. 2017) because this has an impact on citizens’ everyday life. Sufficient data and validation have been col- lected to initiate commercial applications for citizen observation data in weather forecasts. The GROW Observatory monitors soil properties, aiming to engage a target au- dience of smallholders, and community groups practicing sustainable growing. The participants’ motivation is mostly focussed on: improving their immediate environment; growing crops; getting the most out of their land through sustainable practices, without harming the environment and concerns about soil degradation. Emotional motivation is evi- dent for the stakeholders, but to engage and train participants to generate an observation dataset of high scientific standards poses challenges. GROW Observatory during the funded period of the project aims to organize a CO, which is viable after the funded period. To engage and train core groups of stakehold- ers all over Europe is a must to reach this goal. The first half of the project was to estab- lish the framework, develop the training and communication tools and strategies, to define and engage communities around Europe. As soil formation is slower than the human- induced degradation processes, it can be con- sidered a non-renewable or a conditionally renewable resource. To sustainably manage soils over a large geographic scale, sophisti- cated environmental monitoring infrastructure is required and society of environmentally con- scious citizens. By the continuous observation and documentation of our environmental con- ditions, both regular individuals and scientists can learn the impact of the related activities. Awareness raising is one of the most important goals of the GROW Observatory project. The soil has various physical, chemical and biological properties, which define its ability to support its functions. Soil moisture is the amount of water present in the soil. It defines the thermal buffering capacity of warming and cooling the environment, the amount of available water for biomass production. Soil is a reservoir of a significant amount of conti- nental freshwater from the water cycle as well. Soil moisture is an ever-changing property of the soils. It is influenced by climate, soil tex- ture and structure, organic matter content and above all land use and land-cover (Várallyay, G. 1989). Detailed observation of the spatial and temporal distribution and variation of soil moisture is fundamental for drought and flood modelling, global climate predictions or the precise use of agricultural land (Várallyay, G. 2010). Soil moisture serves as a key input parameter in wind and water erosion estima- tions and is a driving parameter of soil bio- logical activity and diversity, organic matter development, hence carbon sequestration (Lavelle, P. et al. 2006). One of the biggest threats to agricultural land is soil compaction and soil moisture is a principal parameter in- fluencing soil strength, so it is a particularly helpful characteristic when assessing the likely magnitude of the soil shearing resistance and 121Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139. hence the inherent vulnerability of subsoil to compaction. Long term soil moisture measure- ments describe the soil moisture regime, which defines salinization processes on salt-affected soils. For these reasons and more, it is extreme- ly important for soil moisture to be measured. In situ measurements have been histori- cally used as the main source of information on local moisture conditions (Várallyay, G. 1994; Makó, A. et al. 2010). Several techniques have been developed for measuring in situ soil moisture, each having specific advantag- es, characteristics and measurement accuracy (Robinson, D.A. et al. 2008; Dorigo, W.A. et al. 2011b). The most commonly used instru- ments measuring soil moisture over a small area which hence were the only representative of the conditions a few centimetres around the sensor. Even though a large number of local and regional soil moisture networks are oper- ating worldwide, they lack common standards (e.g. observed variables, sensor types, sensor setup, etc.) and the generated data are often not freely available. The International Soil Moisture Network (ISMN, https://ismn.geo.tu- wien.ac.at/) (Dorigo, W.A. et al. 2011a, b) is an international initiative trying to overcome such issues. In situ soil moisture observations are collected from various networks distributed all over the globe, harmonized in terms of the sampling interval, units, and data format and made freely available to the public through a web portal (IPCC, 2007). In Europe, there are fewer than 250 stations available in the ISMN providing information about the water content of the soil. It is, therefore, evident that there is great potential offered by COs (e.g. GROW) to contribute with an unprecedented stream of data from thousands of sensors. Nevertheless, European-wide and global analysis based on ground observations would remain challeng- ing because such measurements are spatially sparse. Due to the high spatial variability of soil moisture, a huge number of stations would be necessary. However, the high costs related to installation, operation and maintenance of the sensors, as well as the limited accessibility of certain regions, make the setup of such a network not feasible (Gruber, A. et al. 2013). To fill this gap, remotely sensed data from optical/thermal and microwave instruments are being used to retrieve soil moisture glob- ally (Wang, L. and Qu, J.J. 2009). In particular, microwave sensors, both active and passive, have proven successful for estimating dielec- tric properties of soil, thus, leading to the es- timation of soil moisture (Mohanty, B.P. et al. 2017). Furthermore, when compared to opti- cal/thermal sensors, microwave remote sens- ing has the great advantage of observing the Earth’s surface independently from the weath- er (i.e. cloud cover) and solar conditions (i.e. both during day and night). Several satellite- derived datasets have been available for the last two decades, providing long-term records of global soil moisture conditions. The use of coarse-scale observations (10–50 km) from ac- tive or passive sensors is well established and used for operational purposes. For example, remotely sensed soil moisture products from the Advanced Scatterometer (ASCAT) aboard Metop (Wagner, W. et al. 2013), the Soil Moisture and Ocean Salinity (SMOS) (Kerr, Y.H. et al. 2012) and Soil Moisture Active Passive (SMAP) (Chan, S.K. et al. 2016) mis- sions have been extensively evaluated and found widespread use (Grainger, A. 2017; Bauer-Marschallinger, B. et al. 2018). However, such coarse scale products do not meet the requirements of many applica- tions, such as irrigation management, erosion/ landslide prediction, and catchment-scale hydrologic processes. The recently launched Sentinel-1 mission is scanning the Earth’s surface at unprecedented spatial resolution (backscatter retrieved at 20 m). In particular, Sentinel-1 is a mission of the European Earth observation program Copernicus, consisting of two identical satellites, Sentinel-1A and Sentinel-1B, launched in April 2014 and April 2016 respectively, and carrying a Synthetic Aperture Radar (SAR) system. The soil mois- ture retrieval from Sentinel-1 poses some chal- lenges because of the complex influence of ter- rain roughness and vegetation on the backscat- tered signal and is, therefore, available at a 1 km spatial resolution (Bauer-Marschallinger, B. et al. 2018). This product is currently in prep- Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.122 aration for operational dissemination through the Copernicus Global Land Service (https:// land.copernicus.eu/global/products/ssm). Regardless of the sensor used to estimate soil moisture, satellite-derived products are becom- ing more and more valuable for local to global monitoring of the Earth status. The calibration of algorithms and validation of products are of vital importance, therefore, so too are spa- tially distributed in situ monitoring networks providing long-term reference measurements (Mohanty, B.P. et al. 2017). The GROW Observatory aims to demon- strate a CO can deliver widespread uptake, robust science, societal impact, and by proto- typing new innovative services, be a sustain- able business model for long-term operation. Citizens’ Observatories are a concept devel- oped at the European Union (EU) level. COs are communities of stakeholders which include citizens, scientists, policymakers and others collaborating on research, and in this case for environmental monitoring, whose issues have impacts related to land cover and land use. A soil moisture participatory monitoring network is a step forward in environmental monitoring. A European-wide network of stakeholders interested in the state of soil on local, regional, national and EU level generates not only data and other observations but also discussion and knowledge-sharing related to soil protection, land use, soil conditions, and climate monitoring, documentation on grow- ing practices and harvest data. The basis for the professional and scientific framework of the dialogue of participants of the CO is provided by the widespread and accurate communica- tion and training materials, face-to-face work- shops. The value of the common knowledge generated by the CO is worth as much for the community as much the generated quality data is important for science. Methods and procedure Functional citizen observatories can take a number of routes to development, many are bottom-up initiatives developing organically to address a matter of environmental concern, with an initial momentum. To address the spe- cific scientific needs, the GROW Observatory is taking the example and top-down organizes a soil moisture citizen-based monitoring net- work. Demand exists for the science part, and for part of professional land users also, the for- mulation of the real return value is still a must. The GROW Observatory set out to dem- onstrate a complete ‘Citizens’ Observatory’ system for monitoring SM, land use and land- cover, contributing in situ data for satellite validation, creating useful data products and applications, and overcoming barriers to up- take. GROW entails a particular approach to mobilizing citizens and stakeholders from sci- ence and policy in data collection, data aware- ness, and data innovation. This approach was described and formalized in the first year of the project as a framework, in order that it can be effectively developed, evaluated and replicated. The GROW citizens’ observatory framework is here proposed as a process model underpinned by four cross-cutting val- ues. Together these documents an ideal and conceptualized representation of the GROW Citizens’ Observatory (Figure 1, Table 1). One of GROW’s distinguishing features among other projects is its aim is to focus on ‘closing the loop’, moving from citizen issues Fig. 1. GROW Citizens’ Observatory model 123Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139. Ta bl e 1. T he fr am ew or k st ag es o f G R O W C it iz en s’ O bs er va to ry G R O W p ha se s D es cr ip ti on A ct iv it ie s Sc op in g M ap id en ti fy is su es , p os it io ni ng m is si on s, in fr as tr uc tu re , p ar ti ci - pa nt s, d at a, s er vi ce s, c ri te ri a R es ea rc h ci ti ze ns , e xp er ts , p ol ic y M ap pi ng is su es a nd c on ce rn s Pl at fo rm a nd p ro d uc t d ev el op m en t G ap a na ly si s C it iz en s ci en ce B es t p ra ct ic es fo r C O ’s C om m un it y bu ild in g R ec ru it m en t, on -b oa rd in g ci ti ze ns , e xp er ts , p ol ic ym ak er s E ng ag em en t a nd c om m un ic at io ns w it h ki nd re d n et w or ks C om m un it y st or yt el lin g T he m es M ed ia p ar tn er sh ip C om m un it y ch am pi on s D is co ve ry E d uc at io n an d b ui ld in g un d er st an d in g, c on te xt , s ci en ce , p ro to - co ls , m is si on a im s an d o bj ec ti ve s L ea rn in g an d tr ai ni ng D at a lit er ac y Pe er a nd s oc ia l k no w le d ge e xc ha ng e Se ns in g D at a ga th er in g an d o bs er va ti on s us in g te ch no lo gy a nd w it h ci ti ze ns , s ha ri ng d at a Se ns or d is tr ib ut io n D ep lo ym en t D at a up lo ad a nd a cc es s A w ar en es s D at a lit er ac y, a na ly si s, a pp lic at io n an d a ct io n W eb in ar W or ks ho ps C el eb ra ti on D at a ac ce ss , a gg re ga ti on Sh ar in g in si gh ts Sc ie nt ifi c or e xp er t i nt er pr et at io n C ri ti ca l r efl ex io n w it h ci ti ze ns In no va ti on N ew d at as et s, p ro to ty pi ng a nd te st in g se rv ic es w it h us er s, v al i- d at in g C O p la tf or m a nd in fr as tr uc tu re D es ig n fo r ne w s er vi ce s B us in es s pr op os al s N ew r es ou rc es a nd a ss et s fo r co m m un it ie s R ob us t p ro to co ls a nd d at as et s A d vo ca cy P ol ic y, s oi l, se rv ic es , t ec hn ol og y, r ob us t d at a, C O a pp ro ac h C ha m pi on in g B i- la te ra ls C ha ng e: in p ra ct ic es , p ol ic y, u pt ak e E xt er na l a pp ro pr ia ti on Su st ai na bi lit y of th e pr oj ec t, to ol s an d o ut pu ts In te rv en ti on s Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.124 and data collection to development of criti- cal innovative services with the value given back to citizens, experts, and policymakers. The challenge generally for COs is moving from a top-down contributory model of citizen science to a more distributed model, and to sustain community building and en- gagement throughout the project cycle. The GROW Model structure outlined in Figure 1 provides a mechanism to develop and over- view the whole cycle and set the direction when planning, developing, delivering and evaluating the primary steps to: 1. generate, share and utilize data and in- formation presented and adapted to a range of stakeholders; 2. address community, science and policy challenges; 3. innovate in services using GROW data for land and soil issues of participants: citi- zens, decision-makers and scientists. Results, the concept and elements of GROW Having used the model described above, the following major elements and tools have been developed to support the public en- gagement process and to structure the sensor data and the important covariates provided by citizen science activities into a functional platform to store the data and make it public- ly available for any further scientific or com- mercial use. The monitoring system has two main factors influencing the final database quality. The human factor is the depth of engagement, the level of knowledge and in- terest. The backend system and sensor infra- structure is the technical factor. The results are grouped and presented in these contexts. Elements of engagement tools developed and integrated into the procedure GROW Missions The definition of a GROW Mission is a pe- riod of coordinated citizen science activity, that can involve observations, sampling, and sense-making, designed to deliver a clearly stated output linked to a GROW ambition. Each Mission represents a complete cy- cle through the seven stages of the GROW Framework. GROW Missions bring together a community of citizens and stakeholders in science and policy to collaborate on research for environmental monitoring, whose issues have impacts related to land cover and land use. Pilot Missions were delivered in the first year of the project to test project concepts and infrastructure, and two main Missions were then defined from year two of the project: a) Changing Climate Mission This mission is open to those located in 9 GROW Places which were selected using key criteria through an open call. We are focus- ing the sensors in a limited number of areas because a high density of measurements is the most valuable to science. Additionally, there is a limited supply (15,000) of the low- cost soil sensor we are using in GROW. In this mission, we are deploying several thou- sand soil sensors around Europe, which send soil moisture data back to the GROW Observatory. These data are used to validate soil moisture readings taken by European Space Agency satellites and to inform deci- sions by food growers and policymakers, ul- timately the ambition is to help society adapt to extreme climate events. b) Living Soils Mission This mission is open to anyone, anywhere. The aim is to develop and support an active network of small-scale growers and gardeners who grow food by using and collaboratively investigating, practices that regenerate soils and create resilient ecosystems. Two key ele- ments are the provision of scientifically robust information on selected regenerative practices such as using mulches, reducing digging or tilling, and growing polycultures via free massive open online courses (MOOCs). This was combined with a citizen experiment on polycultures called the Great GROW Experiment, which was designed to enable individual growers to investigate whether growing three crops together in a polycul- 125Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139. ture or separately in monocultures was more productive. A final element is the sharing of planting and harvesting times for key crops to improve localized growing advice available in the GROW Observatory App. GROW Places GROW Places (GP) are an innovation for the delivery of a CO sensor network. They contribute to the mission of delivering a vi- able, high-density distribution of sensors across geographically diverse areas, using geographic and scientific criteria, designed for scientific exploitation, and enabled by the participation of a place-based commu- nity. GROW Places were specified as focus areas for citizen science activity, that provide GROW with a mechanism to establish direct contact with local growing communities in Europe (Figure 2). They have been defined as a solution for sensing activities and to meet the geospatial requirements of the ‘gridded product’. Up to 15,000 Flower Power soil sen- sors (Parrot Drones SAS, Paris, France) are available to participants in GROW Places. These are a formally commercially available product, a detailed description can be found in Description and technical details of the sensors used in GROW Observatory. GROW places are carefully selected areas where the capacity of engagement and the spatial and technical requirements of the soil moisture monitoring network meet. To cre- ate a representative spatial coverage of sen- sor deployment a clustered-nested monitor- ing network was designed. The GROW Place areas represent the regional heterogeneity of Europe, selected by climatic regions. Within these 50–100 km wide windows, there are the local clusters nested, which cover the topo- graphic, microclimatic and soil heterogene- ity. The degrees of freedom of the networks’ coverage is limited by the available stake- holders and clustering communities, but this way of constructing the network can provide results from the beginning. The details of the selection method described in Spatial cover- age, the relation of observation network develop- ment and engagement process. Community Champions Community Champions (CC) are ‘ambassa- dors’ on the ground. They support local com- munity participants through the provision of a sensor and materials needed for the sensing survey as well as ‘meet-ups’ to provide sup- port and training for participants. Through the Community Champions, GROW is able to build a network of engaged participants in each GROW Place. CCs are the regional organizing force on each GPs, they are directly and contin- uously connected to project partners, feedback and action are through CC organization. Online Community GROW’s online community is central to meeting GROW’s ambitions to engage thou- sands of people in sustainable soil manage- ment and food growing. The online com- munity has access to training materials, information sheets and support materials. Our communication strategy brings novelty through the application of the ‘Storytelling method’. Through GROW’s online commu-Fig. 2. GROW Places around Europe Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.126 nity and tools (discussion forum, knowledge base, learning platform, social media), users can upload and explore data, discuss find- ings and share stories. The aim is to promote deeper engagement – as the primary goal is for the participants to talk to, learn from and support each other, rather than receiving top-down information from the GROW team. Experimenters’ community The Great GROW Experiment, part of the Liv- ing Soils Mission took a different approach to engage and work with the community. It delivered an innovative hypothesis-driven rather than an observation-based approach to citizen science. This was founded in training citizens in how to do research in their grow- ing space as well as in how to implement the experiment and interpret their own results. As such, it requires the intensive investment of both time (across the growing season) and growing space for participants and is likely to attract fewer participants than simpler ob- servation-based approaches (Bonney, R. et al. 2009). Experimenters were supported from May to October 2018 with regular emails, a dedicated online forum, and monthly live meetings where they could learn from the scientists running the experiment and share insights with each other. MOOC courses assisted in training citizens, culminating in helping experimenters to graph and under- stand their own experiments. This approach not only enhanced a sense of community and learning, but also provided valuable insights into the progress of the experiment, issues with crops, and technical limitations for data input. In later online meetings and in the final MOOC, initial results were shared and dis- cussed, allowing participants to contextual- ize their own findings with those of others. We have observed this approach achieved a high level of deep engagement, for the par- ticipants. This committed small group of indi- viduals is making clear plans for continuing experimenting on their own and involving their communities. Online learning – GROW Massive Open Online Courses (MOOCs) An innovative element of GROW is provid- ing rigorous training for citizens in scientific protocols for data collection. This is seen as an approach to improve data quality and validity by enabling cohorts of citizens to receive train- ing and grow their confidence in providing data. Data quality of measurements is a par- ticular challenge in citizen science (Hecker, S. et al. 2018), especially with a large number of participants distributed over a wide area, or even an entire continent. It is widely acknowl- edged that the ways in which citizens learn and gain knowledge are changing, with new tools and educational materials available to foster citizens’ autonomy and responsibility for change through lifelong learning. In ad- dition to training in techniques, each MOOC also offers a recruitment opportunity. It builds a cohort of learners who become familiar with GROW’s aims and activities and who can ac- cess and sign up the GROW Observatory’s wider activities outside the MOOCs. Online learning is the tool of quality assurance and also important in raising interest, develop communities and cluster common knowledge. Elements of database development The data provided by the CO is the out- put and the tool for further engagement. The quality and applicability of the result- ing database is the best measure of the COs functionality. In order to structure the con- tributions and make them accessible for any further use, the following tools and elements were developed and integrated into the framework. Data quality assurance and data governance 1. Sensing Handbook and Sensing Manuals The Sensing Handbook and Sensing Manuals are printed and downloadable 127Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139. training resources in use by participants, translated in the local language of GROW Places, that communicate the Mission objec- tives and instructions for: – identifying a suitable location for the sensor; – placing and registering the sensor; – carrying out the land survey; – troubleshooting and accessing support. 2. Sensing infrastructure The GROW Observatory aims to set-up a pilot citizen soil moisture monitoring net- work with 15,000 deployed sensors EU-wide from the beginning. The data, generated by the CO will be incorporated in GEOSS and used to validate for soil moisture SAR remote sensing data. The gridded product creation and Sentinel-1 soil moisture model ground-truthing were the two main aims for GROW soil moisture data. The scientific cri- teria and quality assurance were designed to satisfy these aims and form the basis of the soil moisture sensing aspect of the CO. The intention is to develop the platform further to connect other brands and do-it-yourself (DIY) soil sensors. The upper 10 cm of soil dynamic properties vary fast in time and highly within a small area. Sensor measurements’ inaccuracy can be dissolved in the real range of values. To be aware of the quality of the generated da- tabase the sensors were tested two ways. One is against professional, calibrated probes and the other is in the laboratory, measuring real values of water content as described below. The Flower Power sensor logs soil mois- ture, soil surface temperature, light inten- sity, and conductivity measurements every 15 minutes. The device can store 80 days’ worth of measurements which is accessed with a mobile app through low energy Bluetooth connection. The application to con- nect the sensor to mobile devices only runs on Android and iOS systems. Batteries will last for 6 months in summer and 4 months in the winter period, on average (depending on temperatures). The measured values are: – Air Temperature (Range: – 5 °C to + 55 °C; Accuracy: +/– 1.5 °C). – Light (Range: 0.13 to 104 [mole × m-2 × d-1]; Accuracy: +/–15%). The light sensor is calibrated to measure Photo-synthetically Active Radiation (PAR), defined as light in the wave length between 400 and 700 nm. – Soil Moisture (Range: 0 to 50 [v/v %]; Accuracy: +/– 3%). – Fertilizer level / Conductivity (Range: 0 to 10 [mS × cm-1]; Accuracy: +/– 20%). The soil moisture measurements are the main focus of the CO, but the other values documented give good environmental data of the current state of soil and weather. Information about the sensor location is gen- erated the first time data are uploaded using an internet connection from the device to the Parrot cloud. This can generate inaccuracy in the geolocation entered in the database since the internet connection is needed at the sensor location. The ability to amend sensor coordinates is included in the Collaboration Hub (CH) but requires a European wide cam- paign to train and motivate users to use it. 3. Quality check of sensing infrastructure FP sensor performance compared to pro- fessional probes. In order to evaluate the performance of the Flower Power soil moisture sensors, they were placed alongside professional probes in two different study areas, located in Austria and Italy. The main study area is the Hydrological Open Air Laboratory (HOAL, http://hoal.hydrology.at; Blöschl, G. et al. 2016) located in Petzenkirchen, Austria. HOAL is an agricultural catchment covering 66 ha and equipped with soil moisture sta- tions (20 permanents and 11 temporaries). The permanent stations are located in pas- ture and forest, while the temporary stations are installed in agricultural fields and are re- moved on a regular basis to allow for field management. The majority of the stations are equipped with SPADE Time Domain Transmission sensors, one station uses the Decagon 5TM sensor to measure soil mois- ture. The sensors are installed in a horizontal position at different depths: 0.05 m, 0.10 m, 0.20 m, 0.50 m and 1.00 m. In addition, there are two professional soil moisture stations Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.128 installed 20 km North of Petzenkirchen, in Dietsam, Austria. They are located in grass- land and equipped with Decagon 5TM sen- sors in a depth of 0.05 m and 0.10 m. A total of 37 Flower Power soil moisture sensors were placed on the 30th of April 2017 alongside the technical grade sensors in the HOAL catchment, four on the 24th of May 2017 in Dietsam. Up to the beginning of 2018, 7 ad- ditional sensors were placed in Petzenkirchen and 3 sensors had to be replaced in Dietsam. In total, 51 Flower Power sensors were used to evaluate their performance in comparison to 31 professional probes in Austrian test sites. The Flower Power sensors are installed vertically, providing information about the water content of the first ten centimetres of soil. The comparison period between profes- sional and Flower Power sensors ranges from 2 to 10 months (due to different installation dates and/or removal caused by field man- agement practices). The second study area consists of two sites located in Umbria, Italy. The first site ‘Petrelle’ is part of the network ‘UMBRIA’ (Brocca, L. et al. 2011), which is part of the International Soil Moisture Network (ISMN, http://ismn. geo.tuwien.ac.at/; Dorigo, W.A. et al. 2011a, b 2013). The ‘UMBRIA’ station is equipped with ThetaProbe ML2X sensors, installed vertically (0.05–0.15 m and 0.15–0.25 m). The second site consists of two professional stations of the net- work ‘HYDROL-NET_PERUGIA’ (Morbidelli, R. et al. 2014), which is part of the ISMN as well. At these professional stations, TDR TRASE sensors are horizontally installed at a depth of 0.05 m. 2 Flower Power sensors were in- stalled next to one professional probe from the ‘UMBRIA’ network and 4 Flower Power sensors were installed alongside two profes- sional stations of the network ‘HYDROL-NET_ PERUGIA’. The Flower Power sensors were installed in the middle of November 2017 and provided data for more than two months. As shown in Figure 3 good agreement of the temporal variability between the Flower Power and the professional soil moisture sen- sors can be observed in both study areas. For the sensors located in Austria, a stronger re- sponse of the Flower Power sensors to pre- cipitation events is visible which can be ex- plained by the different sensor positioning. A more or less pronounced bias between the soil moisture levels from the low-cost and the professional probes can often be observed, and is not surprising due to the lack of site-specific calibration of the Flower Power sensors. However, for satellite validation, the soil moisture relative variability is of higher im- portance than the absolute values. Therefore, the scientific goals of GROW remain invio- late, but accurate measures of absolute water content in the soil would be more valuable for farmers and growers. Fig. 3. Time series plots of Flower Power and 5TM (0.10 m depth) soil moisture readings in Dietsam, Austria 129Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139. 4. Laboratory testing of Flower Power sensors Flower Power sensors were validated in laboratory conditions in four different kinds of soils and two different setups; 28 sen- sors were deployed on four different kinds of soils (seven sensors for each: clay loam; sandy loam; loam; loamy sand). Two differ- ent experimental setups with four sensors were installed into the same container for cross-validation and three sensors were in- stalled in separate containers with large di- ameters to test the measured soil volume and for cross-correlation. First, four different types of natural soils were selected and prepared and labora- tory tested for basic chemical and physical properties. The volume of the samples was measured and recorded and then the soil was saturated with water and the weight of saturated soil recorded. The saturated soil samples were dried naturally for 31 days in an undisturbed room and the weight was measured each day with exact time records. The actual soil moisture content was calculat- ed for each measurement and the measured values were compared with the downloaded Flower Power sensor data for the same time. Statistical analysis to define the measurement uncertainties and its soil type dependencies were performed. Conclusions were that there is a clear correlation in the levels of uncer- tainty identified. Flower Power sensor meas- urements on dryer soils have larger positive divergence from the actual measured value, of around 40 per cent moisture content and overestimated moisture when below 40 per cent actual value, and underestimated it above 40 per cent actual soil moisture. The cross-validation among the sensors was more or less constant, except in very dry condi- tions due to the cracking of the soil. Flower Power measurements had a severe distortion in dry soil moisture conditions (< 20%) and can almost double the real value. In the most common soil moisture range (20% to 40%), the estimation differences are less than 20–25 per cent. Within the sen- sors, variation does exist but is negligible, but cross sensor variation can reach 15 per cent. The deviation from the lab measured values show a very strong trend line – the deviation increases towards the dry section. There are significant differences between the different soil types, but the same trends can be observed in Flower Power sensor meas- urement uncertainty (Figure 4). The sensors performance measuring the real water content was not so reliable, but sensitive enough to detect spatial variability of soil moisture. Measurements for direct Fig. 4. Deviation from the laboratory measured soil moisture, based on four repetitions of FP sensor measurements for all conditions Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.130 agricultural decision making are not suffi- ciently accurate, but by empirically the sen- sor user can get information as within sen- sor variation is negligible. The purpose of creating continuous soil moisture maps by extrapolation gives some difficulties but to represent spatial variability of soil conditions the sensors are suitable. Other parameters measured by Flower Power sensors were not validated as soil moisture is the primary dataset to be used for the project aims. 5. GROW Observatory mobile app The GROW App provides three services to growers: it provides a local growing, planting and harvesting advice for small scale grow- ers, gives practical information on specific growing approaches that will also improve soils and ecosystems, and it allows the sub- mission of site description for the Changing Climate mission. Information on suitable crops is derived from GROW’s Edible Plant Database and is interrogated based on the phone’s GPS to show crops that are suitable for the location and time of the query. Each crop has detailed information on site require- ments and cultivation. The practice-based information highlights the value of specific regenerative practices as well as guidance on how to implement them. The site infor- mation data gives step-by-step guidance for a consistent land-survey for the placement of each sensor including the categorization of side position, slope, canopy cover, and aspect-oriented site photos to enable a con- sistent comparison of sites. 6. Data platform development The data collected in GROW is made avail- able to growers and other interested stake- holders through the GROW data platform (Figure 5). The two GROW front-end ser- vices, the Collaboration Hub and the GROW Observatory mobile app, are both connected to the GROW user account database. This ensures that participants can use their GROW user ac- count for both services and data collected by the user through different channels can be com- bined. Data from the Flower Power sensors is collected in the field using the Flower Power mobile application. After a Flower Power ac- count has been created in the mobile applica- tion, it connects to the sensor via Bluetooth and uploads the data to the Flower Power database. Fig. 5. The GROW data platform (in the green box) and connected external applications and tools 131Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139. Growers register their sensor with GROW through their user account in the Collaboration Hub. After successful registration, the GROW sensor database starts to request and store all sensor data collected by the user from the Flower Power database. The Collaboration Hub requests these data from the GROW sen- sor database and displays them in the user’s personal pages. The visualization includes line graphs of the sensor observations and a map with the sensor location. If the location is not correctly registered, users can adjust their sensor location in the Collaboration Hub and the corrected location is saved in the GROW sensor database. The GROW Observatory mobile app re- quests information about suitable plants for the user’s location and time of year from the Edible Plant Database. Data collected in the mobile app by users performing the land sur- vey is stored in the Land Survey database. Scientists and companies interested in work- ing with the data collected in GROW can ac- cess all data or a selection based on geographic extent or time span through the GROW API. These data are also discoverable through the GEOSS Portal (http://www.geoportal.org/), where Earth Observation data from archives all over the world can be searched. Individual users who would like to access the sensor data they collected, can use the MyData download tool. This is a simple pro- gram that asks the user to provide the user- name and password of their Flower Power account, after which it requests all data for this user from the Flower Power database. For each sensor that the user owns, the pro- gram creates a text file with the data. 7. GROW Data Governance and Infrastructure Standards and infrastructure are central to GROW – or, indeed, to any CO – and need to be developed and maintained beyond the life of an individual project. GROW is un- derpinned by standards and infrastructure that are detailed below. The values of GROW relating to handling and sharing data are set out in the GROW Data Governance state- ment. These, in turn, reflect the core values of the project. 8. Service Innovation Through innovation, GROW aims to deliver services based on collaborative data to en- hance the GROW experience for its stakehold- ers and create an interface with specialist data users in science, policy, and business. Through a human-centred design approach, the needs and interests of users and specialist audiences in science, industry, and policy were scoped in the early stages of the project. This user research underpins service design and de- velopment to achieve an effective transfer of environmental knowledge to policy and other specialist communities and the widespread uptake of GROW data and information. 9. Observatory Policy Interface (OPI) One of GROW’s aims is to promote and enable more effective and inclusive participa- tory governance around the management of soils. Soil-related problems are complex, un- certain, multi-scale and impacts upon mul- tiple actors and stakeholders. The OPI helps to inform the underlying assumptions and resulting assertions to establish how partici- pants can inform policy through data gather- ing and active engagement. This is supported by leveraging established relationships with the policy community to communicate find- ings to policymakers and forums. 10. Data dissemination and visualization The Collaboration Hub (CH) is a place to be part of the GROW community and to con- nect and discuss with other stakeholders. It is where people connect their soil sensors to the GROW database and where participants can visualize and compare their observations to regional and local environmental data. Data visualization and interpretation is one of the project’s most important tool of engagement, education and raising interest. Most participants operating sensors are gen- erally aware of the soil conditions of their property and can manage this instinctively by experience. But the recorded dataset, which documents the changes and anoma- lies throughout time, offers a higher level of knowledge. The CH is the platform where the recorded and interpreted soil moisture data and the participants’ knowledge and Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.132 experience are joined together. Local groups can discuss and analyse the measured values, participants of different professional levels interpret their soil conditions. 11. Interpretation of measured soil moisture Different levels of data visualization have been developed within the GROW Project. The first encounter with the measured soil moisture data occurs when a user connects the sensor with the smartphone to download the logged measurements. The Flower Power smartphone application connects to the sen- sor, downloads the data and then uploads to the service provider’s data storage cloud. On the server side, averaging is made to make the dataset scalable then the processed data- set is downloaded to the device, where a scalable graph view visualizes the measured values. The app contains a global plant da- tabase of 7,000 species and varieties and the graph view compares the actual values to the plants’ water, light and temperature needs. Further data visualizations are planned for GROW’s CH so participants can compare their observations to regional and local envi- ronmental data. One visualization will dis- play long-term average characteristics of the water balance in the plant-soil-atmosphere system at the site of the sensor. Long-term monthly mean values of potential evapotran- spiration, actual evapotranspiration, rainfall, and mean daily temperature which together defines periods in the year with specific soil hydrologic situations such as water surplus, water utilization, water deficit, and water recharge having a potential impact on cul- tivated plants/agroecosystems. Other eco- logical interpretations of the measured soil moisture will display the temporal record of actual soil moisture in the top-most soil layer (0–10 cm) as measured with soil mois- ture sensor (volumetric %) at the site/parcel with soil moisture measurements. There are plans to display background static values of soil moisture ecological intervals (re-calcu- lated into volumetric %) estimated for the soil texture class taken from the underlying soil map/grid based on GPS coordinates of the sensor. 12. GROW gridded product visualization Gridded products generated from point measurements and user’s land and soil ob- servations are the visual interpretation of the collected data and the continuous extension of point measurements for the entire area of the GROW Places. The quality of the estima- tion for the area between measuring points depends on the distribution of the sensors and the quality of the explanatory variables (other sources of environmental data avail- able for the relevant area). A methodology for the extrapolation of measured soil mois- ture data for the area of Europe will be de- veloped, using available free source environ- mental data as explanatory variables. Also, measurements of the error of the estimation of values for the intervals between measur- ing sites will be elaborated. This will facilitate the use of GROW data in climate modelling, drought/flooding forecast or in precision ag- riculture. In the Miskolc area (NE Hungary) a pilot area was established for which high- resolution environmental data (relief, land cover, soil, and daily meteorological data) are available. A dense network of soil moisture sensors had been set up and two months of soil moisture measurements collected. The resulting dataset was used to create a time se- ries of a gridded product with varied density of sensor network and with different explan- atory variables, for known weather events. The gridded product pilot aimed to set up the optimal distribution pattern for sensor meas- urements for the GROW places. This is part of the sensor distribution plan synchronized with the demands of citizen engagement. As a result of the gridded product pilot, a set of environmental variables was listed which are needed for gridded product development for GROW place areas. This visualization will be used in the engagement process. The point soil moisture measurements gath- ered by the observatory are processed, ana- lysed and interpreted. The most powerful tool for visualization is the continuous prediction map of soil moisture for the available biggest areas where the sensor spatial distribution allows. The sensor distribution plan was de- 133Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139. veloped to generate a spatially coherent, and representative sensing network. The aim is to interpolate the point information and estimate the properties for any non-visited site, create a continuous surface from the point observa- tions. These soil moisture layers represent the final products of the monitoring system. The performance or accuracy of the estimations are functions of the spatial coverage of the point measurements and the availability of ac- curate, high-resolution explanatory variables. Open source environmental data is used for the soil moisture map development. As the sensor deployment moves forward during the project, the soil moisture maps will have higher accuracy and greater spa- tial and temporal coverage, providing rich- er data over larger geographical areas and engaging more stakeholders. Some GROW participants are professional agro-producers, and this stakeholder group is interested in the spatial and temporal variability of the soil within their property. To satisfy this demand a sophisticated, artistic visualization of the data is to be provided in addition to scientifi- cally accurate soil moisture maps. 13. Visualization of experiment data The original intention was that experi- ment participant would be able to see their own data graphically as they submitted it. However, this proved beyond capacity within the timescales of the project. Instead, visuali- zations were produced by scientists during the experiment to show collective results at various stages. Thus, participants could un- derstand that the data they submitted was of importance, and see how their own expe- rience compared with the collective results. Productivity data were represented in simple graphs showing that polycultures tended to be more productive. In addition, animated graphics (in .gif format) were used to show maps of monthly productivity from each site for the monocultures and for the polyculture and presented side by side to allow both par- ticipants and other interested parties to watch the monthly yield data change. Here partici- pants could see how their site compared and also see patterns e.g. earlier harvests in the South of Europe and later harvests coming in in the North. A full report of the experi- ment will be described both in the scientific literature and as a public-facing accessible summary. It will be used to guide advice for growing from advocacy organizations like the Permaculture Association (Britain). Provisional results of the GROW system establishment 1. Spatial coverage, the relation of observation network development and engagement process A monitoring network like the GROW Observatory will have spatial biases as it relies on citizen scientists. Thus, the require- ments of spatial and temporal coverage of the network need to be carefully designed with recruitment and engagement protocols. The scientific objectives within GROW, like creating a gridded soil moisture product based on citizen’s observations using low cost soil moisture sensors and freely availa- ble environmental explanatory variables; and to provide an extensive dataset of in situ soil moisture observations which can serve as a reference to validate satellite-based soil mois- ture products and support the Copernicus in situ component set up restrictions on the are- as to be sampled and strong demands on par- ticipants. The sensors must be deployed in as many different climate regimes, land cover classes, soil types and topographic positions as possible, on representative, non-urban areas and the longest time span of continu- ous observations are crucial providing data for climate-related applications, validation of satellite-based products and Copernicus. Therefore, the main static scientific criteria were the followings: – meaningful geographic coverage (climate, soil, land use, agro-technology) size ap- prox. 50 × 50 km, can be described by ap- proximately 1,000 sensors; – soil, terrain and land use variability, with relatively large homogenous units; – good quality environmental data (terrain, soil, and land use); Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.134 – having a scientific institution capable of supervising the process; – interested local organization to maintain and extend the network for later network expansion-community champion ap- proach, demonstration CO network devel- opment to test the engagement, awareness raising strategies and toolsets. However, the GROW project was initiated to demonstrate the development of soil mois- ture monitoring CO network. The first invita- tion round resulted in several scientifically appropriate areas, a good pool to choose the Grow areas. Thus, the first priority area list has been refined based on the need to have a strong and reliable partnership between a GROW partner and the local community champion. A strong relationship of the local community with the GROW consortia also can support good quality local environmen- tal data (terrain, soil, land use) availability. To achieve the best selection, a clustered- nested sampling strategy was developed to cover most of the geographical diversity for the area of Europe. In parallel with the ex- ploration of potential communities, GROW Places had been designated based on the Köppen-Geiger climate classification to cov- er the most of climatic homogeneity within the area of Europe. GROW Places are geo- graphic focus areas where a high quantity of Flower Power sensors is deployed to record soil moisture and associated data at a high density of observations. They are in specific areas in Europe with strong stakeholder buy-in. The originally selected 17 priority areas covered 4 dominant climate classes: Cold, without dry season, warm summer; Temperate, without dry season, warm sum- mer; Temperate, dry summer, hot summer; Temperate, without dry season, hot summer. Sentinel-1 ground truthing for soil mois- ture modelling requires further restrictions of the sensor placement. The reliability of remotely sensed soil moisture products is influenced by the presence of water bodies and rough topography. Thus, GROW places with a smooth topographical surrounding and a low percentage of water bodies are favoured. For each GROW places ancillary dataset were used to derive topographic complexity and wetland fraction at the scale of a satellite footprint (Dorigo, W.A. et al. 2015), but this selection was used only as a starting point to contact local communities and start concrete discussions about the im- plementation of the GROW places. From that contact round, it became clear that some of the selected GROW places had to be updated and others replaced as no supporting com- munities could be identified. An enthusiastic community champion is a key to the success of continuous data collection. The scien- tific criteria were not used to select GROW places but were used to evaluate them, es- pecially new GROW places. When adding new GROW places, only the climatic zone criteria were used to evaluate the relevance of the geographic location of the proposed locations. The engagement process outputs were placed as the first priority, and pro- vided with a set of real GROW places, with committed local actors. A wide range of the European climate regions, land use types and topography are covered by the eight final GROW places: Evros and Laconia (Greece), Southeast and Northwest Ireland, Miskolc (Hungary), Barcelona (Spain), Algarve / Alentejo (Portugal), Tayside and Central Belt (Scotland), Vienna (Austria), ’s-Hertogenbosch (Netherlands), and Luxembourg. Within the regional level, there are the nested dense observation clusters represent- ing the local diversity of soil, land use, and topography. This usually covers 40 to 1,500 hectares with high sensor density. Here stakeholders are communities of sustainable growing practices or agricultural producers with economic interest or research institu- tions. These dense sampling networks are complemented by observation points in be- tween covered by smaller-scale growers with few sensors deployed. The primary demographic of GROW is small-scale growers, professionals, and hob- byists, who were expected to be financially independent, intellectually curious and emo- tionally connected to growing. One strong 135Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139. motivation for joining a growing community is found in the common need of living in a sustainable and harmonious environment close to and minimize harm to nature. The overall communication and engagement strategy and toolset were developed to reach out to this type of audience. 2. Piloting engagement and sensing network development To test engagement tools and procedure within the constraints of the scientific cri- teria three pilot missions were set up on GROW places: Alexandroupolis (Greece), Cloughjordan (Ireland) and Miskolc (Hungary), where project partners were in- volved. The main objectives of the pilot mis- sions were: – to validate scientific usefulness of the data through satellite validation activities and preliminary gridded products production; – to validate the material, protocols, and instructions for citizens to deploy, and to maintain the sensing network from a sci- entific point of view; – testing local aspects of the engagement protocol (participant pathway, commu- nity champions) for engaging participants within the project; – validating that the growers are able to take benefits of the sensors and additional ac- tivities, and to test GROW’s back end sys- tem and its capacity to collect and provide the data to users. The missions were imple- mented by project partners from partici- pant recruitment through workshops and training and sensor deployment. One very important point to note about the pilots is even with well-developed engage- ment tools, it can be difficult to implement on a local scale. GROW Places are large ar- eas throughout Europe, where social, demo- graphic and economic differences result in the different target audience and require differ- ent ways of communicating and training. The overall strategy and tools can result in differ- ent levels of engagement and generated data quality depending on location. Global tools are for highlighting the aims and objectives but the local organizing force is indispensable. With small-scale growers, the emotional connection to their work is strong and en- gagement is possible through workshops and awareness raising. This demographic is motivated to observe their soil and environ- ment, but the number of sensors available for them to deploy is very limited, therefore, spatial coverage is random and dispersed if there is no community for a local clustering role. Thus, the database resulting from these observations is not suitable for growing con- sultancy, nor for validating that the growers are able to take benefits of the sensors. Technical difficulties influence the extent and speed of engagement. GROW aims to deploy 15,000 relatively low-cost commer- cial soil moisture sensors. The commercial product contains all backend services of data download, storage, and query which is provided for all participants of the project. Sensor usage training is limited to the opera- tion of the plug-and-play product. Even with these limits in place, during the pilot missions continuous technical support was needed for sensor deployment, data upload and data connection to CH. Small scale growers, using a small number of sensors, may face problems with the technical infrastructure and on occasion need personal assistance. It is not feasible to address these issues and to support participants on an individual basis, although emerging problems are unique. One important issue noted within this pro- ject, the extent of which is heterogeneous throughout Europe, is the physical security of properties. The North East Hungarian pilot faced serious problems of sensor disappear- ance by theft. This is a very limiting factor of spatial and temporal coverage. As an example of specific land use, in vineyards where har- vesting is carried out by contracted seasonal workers be monitored during harvest time, sensors must be removed for the period of one to months. Unfortunately, when the sensors are not deployed over the late summer and early autumn, it is not possible to provide important data on the phenological state of the grapevines in preparation for the winter. Soil moisture over this time period is impor- Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.136 tant for the vines’ nutrient uptake, and, thus, heavily influences the force of sprouting in springtime. So, important data with forecast- ing potential is lost as a consequence of hav- ing to remove the sensors during harvesting. As the project aims to set up a long-term, self-sustained CO during the funding period, the time-frame of engagement and sensor de- ployment is limited. With sufficient profes- sional and economic interest from agricul- tural producers and scientific researchers, sensor deployment could be accelerated to ensure high quality, reliable and continuous observations. Communication towards these potential stakeholders was only undertaken by project partners, with no general commu- nication and engagement strategy developed. GROW’s science partner responsible for the gridded product development and data qual- ity (University of Miskolc), set up a pilot mis- sion with two professional stakeholder com- panies, with whom previous research collabo- ration had been established prior to GROW. The pilot areas cover two different land uses: vineyard and arable land. High sensor density provides data from representative sites of soil and topography. To set up the monitoring net- work, the science partner deployed the sensors based on high resolution local environmental data and empirical knowledge. Regular read- ings are implemented by researchers with the help of the stakeholders, and harmonization of the data needs of producer and research is being undertaken. Continuous effort is made to generate an up-to-date operative database for professional agricultural decision-making and research purposes. One important issue is the frequency with which the sensor data is uploaded into the da- tabase. Retrospective access to the database can still be of use for scientific research, but agricul- tural consultancy requires a regularly updated database. The fundamentals of an operational up-to-date monitoring system that can provide data for agricultural consultancy and forecast- ing, must be set up with scientific vigour and needs an extensive infrastructure of sensors and data storage and processing and a technical front end application to serve decision making. Conclusions, achievements and challenges identified One of the important roles of the GROW project has been to set up standards and protocols for soil moisture monitoring car- ried out by citizen observation networks, to meet scientific and professional criteria. Sci- ence protocols are set, harmonization with professional agricultural needs is being im- plemented in the second half of the project. Science can provide data quality assurance and reliable data interpretation, but a func- tioning CO gives the platform to science to create data interpretation useful for citizens. However, along with the advantages and potentials of the CO approach, several chal- lenges have been identified as well. One of the most important findings is that the results of 100 years of public awareness raising and tradition, like the bird watching example or the weather watchers, is difficult to replicate within a few years. Huge efforts are needed for topics like soil moisture to be integrated into common societal knowledge. Awareness raising and public engagement strategy development are the two most criti- cal elements of any success, where top-down efforts supported by policy-making can make a difference. Data representativity is also a relevant is- sue. A good monitoring system needs to cov- er all different kinds of environmental set- tings, defined by geomorphological, land use and soil properties – among others. A system targeting small-holders may result in a spa- tially biased, incomplete distribution of the monitoring sites, where the point density is high within the village and low or even zero for areas outside of the villages. The soils of the small-holders garden are often changed by cultivation, artificial additives, therefore, the point may represent only a small neigh- bourhood, extrapolation of its information is often limited. One potential means to accelerate the de- velopment of a high-quality soil moisture monitoring system in Europe is to move to- wards engaging with large-scale agriculture, 137Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139. which would require an increasing interest in maintaining soil moisture monitoring sys- tems provide public data. Such a reliable data source could catalyse break-through in all the disciplines affected in earth observation, cli- mate modelling and precision soil and land management and use. The GROW project as a top-down initiative funded by the European Commission has the potential to initiate these necessary processes. With the tools of engage- ment, communication, training and aware- ness raising enforces the bottom-up devel- opment of the CO, reaching out to the wid- est spectra of interested stakeholders. This catalyses self-organizing data communities with the interest in soil monitoring, which through open source APIs and DIY sensors can develop sensing and data infrastructure, or economic interest sets up professional net- works with researchers. Based on the pilot missions’ experiences GROW involved larger scale agriculture and research institutions in the targeted audience. DIY sensor knowledge base and an API to connect any soil sensors to the GROW Collaboration Hub had been developed and will be communicated to the end of the project. A major finding of the GROW project is that low-cost soil moisture sensors can provide data both for home and for scientific use. However, some conflicts between the home user and the scientific interest have been identified. Sentinel-1 data is sensitive to soil moisture of the upper 10 centimetres. This layer dries out and can be rewetted fast. The layer directly below the soil surface represents a more sta- ble source of available water for the plants. Therefore, sensors deployed under the surface layer would provide more relevant informa- tion for the grower, but less representative for the Sentinel-1 data validation and calibration. It has been concluded, that these low-cost sensors have a relatively good performance. The comparison of several readings from the different sensors within the same condition was quite consistent. However, a significant deviation from the lab measurements was identified, probably due to the built-in soil moisture estimation algorithm. It is known, that different soils have different relationships between their dielectric constants and their soil moisture content, so different estimation algorithms need to be fitted to different soils. Any common platform aiming to integrate different sources of data should take the direct raw measurement and apply the appropriate algorithm afterward to avoid inconsistency due to the different estimation algorithms applied within the different kinds of sensors. The ground truthing of the Sentinel-1 soil moisture model’s main criteria is the avail- ability of fresh, up to date data. In order to develop a close to real-time, operational data platform providing up to date soil moisture estimations need to have more frequent data upload to the server. The strong interest of data providers to ensure frequent data up- load is the issue to emphasize. Besides of the traditional engagement mechanisms de- scribed in this paper, other innovative ap- proaches for better outreach to the society, like the integration of environmental art to catch broader community attention is also initiated and currently being developed. A well-functioning back end system of sens- ing, data storage and visualization can provide a stable environment for database continuity. A good visualization of data is an important tool for science to develop data interpretation based on professional agricultural needs. GROW must emphasize awareness-rais- ing, communication, and engagement for the widest range of audience. The post-funding sustainability of the CO depends on the sustaining of both the communities and the infrastructure of sensing, data logging, and processing and interpretation. An engage- ment tool for stakeholders interested in the technical part of soil-sensing is being devel- oped and communicated in the second part of the project. This must be emphasized be- cause DIY sensors and open source commu- nities can provide self-organizing infrastruc- ture for soil moisture monitoring. An existing infrastructure of observations is more likely to generate data interpretation and visualiza- tion which is attractive and engaging for a wide audience and encourage participation. Kovács, K.Z. et al. Hungarian Geographical Bulletin 68 (2019) (2) 119–139.138 Acknowledgements: This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 690199. R E F E R E N C E S Bauer-Marschallinger, B., Freeman, V., Cao, S., Paulik, C., Schaufler, S., Stachl, T. and Wagner, W. 2018. Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles. IEEE Transactions on Geoscience and Remote Sensing 57. (1): 520–539. 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