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Remote sensing, as well as the recent advancements of crowd sensing, along with novel and recent geospatial technologies, have great potential to explore and understand the relationships between our surroundings—in particular our urban and rural environments and natural spaces—and public health through environmental factors [1,2]. Emerging phenomena including climate change, extreme weather conditions, dynamic and mega cities, air pollution, and dust storms, among others, have significant impacts on human and environmental health [3]. On the one hand, the rising volume of Earth observatories and citizen observatories has provided research scholars with a tremendous amount of data streams in space and time, which are novel, unique, and even freely available; therefore, new research agendas are to be designed to exploit the power of these data [4,5]. On the other hand, recent geospatial technologies, such as novel geocomputational techniques, clustering algorithms, visual analytics, data/information mining approaches, Web 2.0, and collaborative sensing techniques, among others, have presented a wide variety of techniques for exploring these data and discovering latent information about public health [6]. To address these issues, the prime aim of this Special Issue is to present novel sensing and computational techniques in order to better understand public health, developing diverse public health applications, and to explore their underlying implications. Thus, we can move towards securing healthier urban/rural environments and natural spaces. This collection of papers should provide a selection of interesting approaches and methodologies, useful for audiences including researchers, practitioners, and professionals. In February 2016, this Special Issue was started by announcing widely the call for papers, and inviting leading scholars to contribute. It received a great response by a significant number of scholars. By the deadline for submission in September 2016, a total of 23 submissions were submitted. Through a single-blind review process following standard MDPI review guidelines, we invited at least three expert reviewers to review the manuscripts and comment on the quality, originality, relevance, as well as fit for the Special Issue. This led to 12 of the 23 submitted manuscripts being accepted (almost 52% acceptance rate) for publication and thus were included in the Special Issue. The Special Issue consists of the following papers: Zhang et al. present a Geographically Weighted Regression (GWR) model using NO2, Enhanced Vegetation Index (EVI), Aerosol Optical Depth (AOD) product, and other meteorological parameters to explain the variation of PM2.5 across China [7]. They also provide a discussion on the usefulness of their results as a reasonable reference for assessing health impacts as well as examining the effectiveness of emission control strategies being implemented in China. Wu et al. explore the spatial associations between thrombocytopenia syndrome virus (SFTSV) infections and several potential determinants (average temperature, average monthly precipitation and average relative humidity) using a GWR model in order to predict the high-risk areas in China [8]. Their proposed approach is suggested for predicting high-risk areas in the implementation of public health strategies. Wang et al. present a study for estimating long-term urban nocturnal boundary layers (NBLs) using elastic backscatter light detection and ranging (LiDAR) in Wuhan, China [9].

Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial