Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

Water quality data represent a critical resource for evaluation of the well-being of aquatic ecosystems and assurance of clean water sources for human populations. While the availability of water quality datasets is growing, the absence of a publicly accessible national water quality dataset for both inland and the ocean in China has been notable. To address this issue, we utilized R and Python programming languages to collect, tidy, reorganize, curate, and compile three publicly available datasets, thereby creating an extensive spatiotemporal repository of surface water quality data for China. Distinguished as the most expansive, clean, and easily accessible water quality dataset in China to date, this repository comprised over 330 000 observations encompassing daily (3588), weekly (217 751), and monthly (114 954) records of surface water quality covering the period from 1980 to 2022. It spanned 18 distinct indicators, meticulously gathered at 2384 monitoring sites, which were further categorized as daily (244 sites), weekly (149 sites), and monthly (1991 sites), ranging from inland locations to coastal and oceanic areas. This dataset will support studies relevant to the assessment, modeling, and projection of water quality, ocean biomass, and biodiversity in China, and therefore make substantial contributions to both national and global water resources management. This water quality dataset and supplementary metadata are available for download from the figshare repository at https://doi.org/10.6084/m9.figshare.22584742 (Lin et al., 2023b).

In geophysics, crowdsourcing is an emerging nontraditional environmental monitoring approach that supports data acquisition from individual citizens. However, because of the involvement of undertrained citizens and imprecise low‐cost sensors, crowdsourced data applications suffer from different types of noises that can deteriorate the overall monitoring accuracy. In this study, we propose a machine learning approach for automatic crowdsourced data quality control (CSQC) that detects and removes noisy data inputs in spatially and temporally discrete crowdsourced observations coming from both fixed‐point sensors (e.g., surveillance cameras) and moving sensors (e.g., moving cars/pedestrians). We design a set of features from original and interpolated rainfall data and use them to train and test the CSQC models using both supervised and unsupervised machine learning algorithms. The performances of the CSQC models under various scenarios assuming no retraining are also tested (hereafter referred to as transferability). The results based on synthetic but realistic data show that the CSQC models can significantly reduce the overall rainfall estimate errors. Under the stationary assumption, the CSQC models based on both supervised and unsupervised algorithms perform well in noisy data identification and overall rainfall estimation error reduction; however, if the model is transferred to other cities with different rainfall patterns or noise compositions (without retraining), supervised multilayer perceptrons (MLPs) show the best performance.