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.

中文翻译：

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评估消费型低成本鹦鹉花动力土壤湿度传感器在科学环境中的适用性

由非专家或由非专家进行的公民科学，科学工作和数据收集正在迅速增长。尽管公民科学活动产生大量数据的潜力是被广泛认可的，否则它是不可行的，但是对获得的数据通常要谨慎和怀疑。由于它们是由非专家使用低成本仪器或未经科学验证的方法获得的，因此其质量和可靠性并未得到完全信任。在这项研究中，我们评估了欧洲公民科学项目GROW Observatory（GROW; https://growobservatory.org）中使用的Parrot的Flower Power土壤湿度传感器的性能，最后访问时间：2020年3月30日）。GROW的目的是使科学家能够通过众包数据以前所未有的高空间分辨率验证基于卫星的土壤水分产品。为此，它动员了欧洲成千上万的公民采取科学和气候行动，包括数百人被授权监测欧洲各地24个覆盖不同气候和土壤条件的高密度集群中的土壤湿度和其他环境变量。显然，要用作参考数据集，地面观测的质量至关重要，尤其是从低成本传感器获得的情况下。为了调查此类测量的准确性，在实验室和现场对Flower Power传感器进行了评估。对于现场试验，它们与专业的土壤湿度探头一起安装在奥地利Petzenkirchen的水文露天实验室（HOAL）中。我们使用各种方法针对专业探针评估了低成本传感器的技能。除了相关性，偏差和均方根差之类的常用统计指标外，我们还根据国际土壤水分网络（ISMN）质量指标调查并比较了时间稳定性，土壤湿度记忆和标记统计。我们在实验室中发现传感器之间的差异较小，并且与该领域的专业传感器具有很高的时间一致性。对于低成本和专业的探头，土壤湿度记忆和ISMN质量标记分析的结果在可比范围内；只有时间稳定性分析显示出相反的结果。