Abstract
Abstract. Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and scepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project the GROW Observatory (GROW; https://growobservatory.org, last access: 30 March 2020). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within 24 high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low-cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias, and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low intersensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes; only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science.
Highlights
The importance of soil moisture for the hydrological cycle and the climate system is well known (Seneviratne et al, 2010)
Several in situ soil moisture monitoring stations and networks have been established around the globe, ranging from short-term campaigns to long-term observations, and many of them provide their valuable data through the International Soil Moisture Network (ISMN; Dorigo et al, 2011), acquisition, installation, and maintenance of the professional equipment are costly and laborious
The gravimetrically determined and the observed soil water content obtained by using the five Flower Power (FP) sensors are shown in Fig. 3 and Table A2
Summary
The importance of soil moisture for the hydrological cycle and the climate system is well known (Seneviratne et al, 2010). One of the most promising means to observe the state of soil moisture in the top soil layer on a global and long-term scale is microwave remote sensing. A range of active and passive satellite instruments have collected and are still gathering data, forming the basis for various soil moisture products including but not limited to Advanced Microwave Scanning Radiometer (AMRS-E) Land Parameter Retrieval Model. Several in situ soil moisture monitoring stations and networks have been established around the globe, ranging from short-term campaigns to long-term observations, and many of them provide their valuable data through the International Soil Moisture Network (ISMN; Dorigo et al, 2011), acquisition, installation, and maintenance of the professional equipment are costly and laborious. The coverage of such stations is mostly limited in space, density, and duration
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